U.S. patent application number 09/853118 was filed with the patent office on 2002-05-16 for system for determining the occupancy state of a seat in a vehicle and controlling a component based thereon.
Invention is credited to Breed, David S., DuVall, Wilbur E., Johnson, Wendell C., Kussul, Michael E., Morin, Jeffrey L., Xu, Kunhong.
Application Number | 20020059022 09/853118 |
Document ID | / |
Family ID | 27538017 |
Filed Date | 2002-05-16 |
United States Patent
Application |
20020059022 |
Kind Code |
A1 |
Breed, David S. ; et
al. |
May 16, 2002 |
System for determining the occupancy state of a seat in a vehicle
and controlling a component based thereon
Abstract
System for determining the occupancy of a seat in a vehicle
using pattern recognition technologies and techniques that apply to
any combination of transducers that provide information about seat
occupancy, for example, weight sensors, capacitive sensors,
inductive sensors, ultrasonic, optical, electromagnetic, motion,
infrared and radar sensors. A processor is coupled to the
transducers for receiving data therefrom and processes the data to
obtain an output indicative of the seat's current occupancy state.
A combination neural network is resident in the processor and is
created from data sets, each representing a different occupancy
state of the seat and being formed from data from the transducers
while the seat is in that occupancy state. The combination neural
network produces the output indicative of the current occupancy
state of the seat upon inputting a data set representing the
current occupancy state of the seat and being formed from data from
the transducers.
Inventors: |
Breed, David S.; (Boonton
Township, NJ) ; DuVall, Wilbur E.; (Kimberling City,
MO) ; Johnson, Wendell C.; (Signal Hill, CA) ;
Morin, Jeffrey L.; (Lincoln Park, MI) ; Xu,
Kunhong; (Rochester Hills, MI) ; Kussul, Michael
E.; (Kyiv, UA) |
Correspondence
Address: |
BRIAN ROFFE, ESQ
366 LONGACRE AVENUE
WOODMERE
NY
11598
|
Family ID: |
27538017 |
Appl. No.: |
09/853118 |
Filed: |
May 10, 2001 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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09853118 |
May 10, 2001 |
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09474147 |
Dec 29, 1999 |
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09474147 |
Dec 29, 1999 |
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09382406 |
Aug 24, 1999 |
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09382406 |
Aug 24, 1999 |
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08919823 |
Aug 28, 1997 |
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5943295 |
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08919823 |
Aug 28, 1997 |
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08798029 |
Feb 6, 1997 |
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60136163 |
May 27, 1999 |
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Current U.S.
Class: |
701/45 ;
280/735 |
Current CPC
Class: |
B60R 21/0152 20141001;
B60R 21/01534 20141001; B60R 21/01554 20141001; G01S 15/04
20130101; B60R 21/01532 20141001; B60R 21/01546 20141001; G01S
15/42 20130101; B60R 21/01516 20141001; B60R 21/01548 20141001;
B60R 21/01536 20141001; G01S 15/88 20130101; G06V 40/10
20220101 |
Class at
Publication: |
701/45 ;
280/735 |
International
Class: |
B60R 021/32 |
Claims
We claim:
1. A vehicle including a system for determining the occupancy state
of a seat in the vehicle occupied by an occupying item, the system
comprising: a plurality of transducers arranged in the vehicle,
each of said transducers providing data relating to the occupancy
state of the seat; and processor means coupled to said transducers
for receiving the data from said transducers and processing the
data to obtain an output indicative of the current occupancy state
of the seat, said processor means comprising a combination neural
network created from a plurality of data sets, each of said data
sets representing a different occupancy state of the seat and being
formed from data from said transducers while the seat is in that
occupancy state, said combination neural network producing the
output indicative of the current occupancy state of the seat upon
inputting a data set representing the current occupancy state of
the seat and being formed from data from at least some of said
transducers.
2. The vehicle of claim 1, wherein said combination neural network
comprises a neural network trained to determine the type of the
occupying item based on data from at least some of said
transducers.
3. The vehicle of claim 1, wherein said combination neural network
comprises a neural network trained to determine the size of the
occupying item based on data from at least some of said
transducers.
4. The vehicle of claim 1, wherein said combination neural network
comprises a neural network trained to determine the position of the
occupying item based on data from at least some of said
transducers.
5. The vehicle of claim 1, wherein said combination neural network
comprises a first neural network trained to determine the type of
the occupying item based on data from at least some of said
transducers and a second neural network trained to determine the
size of the occupying item based on data from at least some Of said
transducers.
6. The vehicle of claim 5, wherein said combination neural network
further comprises a plurality of additional neural networks, each
trained to determine the position of the occupying item for a
particular size of occupying item based on data from at least some
of said transducers, one of said plurality of additional neural
networks determining the position after said second neural network
determines size.
7. The vehicle of claim 6, wherein said combination neural network
comprises a feedback loop wherein the determination of the position
at one instance is used as input into one of said plurality of
additional neural networks at a subsequent instance.
8. The vehicle of claim 1, wherein said combination neural network
comprises a feedback loop wherein the determination of the
occupancy state at one instance is used as input into said
combination neural network at a subsequent instance.
9. The vehicle of claim 1, wherein said combination neural network
comprises a first neural network trained to identify the occupying
item based on data from at least some of said transducers and a
second neural network trained to determine the position of the
occupying item based on data from at least some of said
transducers.
10. The vehicle of claim 1, wherein said combination neural network
comprises a first neural network trained to determine the current
occupancy state of the seat based on data from at least some of
said transducers and a second neural network trained to determine
whether the data set representing the current occupancy state of
the seat and formed from data from said transducers is similar to a
data set on which said first neural network is trained whereby said
second neural network prevents unreasonable data sets from being
input to said first neural network.
11. The vehicle of claim 1, wherein said combination neural network
comprises a first neural network trained to determine whether the
occupying item is a child seat based on data from at least some of
said transducers, a second neural network trained to determine the
orientation of the child seat based on data from at least some of
said transducers when said first neural network determines that the
occupying item is a child seat.
12. The vehicle of claim 11, wherein said combination neural
network comprises a third neural network trained to determine the
position of the child seat based on data from at least some of said
transducers.
13. The vehicle of claim 1, wherein said combination neural network
comprises a plurality of neural networks each trained to determine
the occupancy state of the seat based on data from a respective set
of said transducers whereby each set of transducers is different
than other sets of transducers, said processor means being arranged
to factor the determination of the occupancy states by said
plurality of neural networks in the determination of the occupancy
state of the seat.
14. The vehicle of claim 1, wherein said transducers include at
least two ultrasonic sensors capable of receiving waves at least
from a space above the seat, each of said ultrasonic sensors being
arranged at a different location.
15. The vehicle of claim 14, wherein a first one of said two
ultrasonic sensors is arranged on or adjacent to a ceiling of the
vehicle and a second one of said two ultrasonic sensors is arranged
at a different location in the vehicle such that an axis connecting
said first and second ultrasonic sensors is substantially parallel
to a second axis traversing a volume in the vehicle above the
seat.
16. The vehicle of claim 14, wherein the system further comprises
horns or grills for adjusting the transducer field angles of said
ultrasonic sensors.
17. The vehicle of claim 1, wherein said transducers include four
ultrasonic sensors capable of receiving waves at least from a space
above the seat, said ultrasonic sensors being arranged at comers of
an approximate rhombus which surrounds the seat.
18. The vehicle of claim 1, wherein said transducers include a
plurality of ultrasonic sensors capable of transmitting waves at
least into a space above the seat and receiving waves at least from
the space above the seat, each of said ultrasonic sensors being
arranged at a different location, said ultrasonic sensors having
different transmitting and receiving frequencies and being arranged
in the vehicle such that sensors having adjacent transmitting and
receiving frequencies are not within a direct ultrasonic field of
each other.
19. The vehicle of claim 1, wherein at least one of said
transducers is a capacitive sensor.
20. The vehicle of claim 1, wherein said transducers are selected
from a group consisting of seat belt buckle sensors, seatbelt
payout sensors, infrared sensors, inductive sensors and radar
sensors.
21. The vehicle of claim 1, wherein at least one of said
transducers comprises a weight sensor for measuring the weight
applied onto the seat.
22. The vehicle of claim 1, wherein said transducers are selected
from a reclining angle detecting sensor for detecting a tilt angle
of the seat between a back portion of the seat and a seat portion
of the seat, a seat position sensor for detecting the position of
the seat relative to a fixed reference point in the vehicle and a
heartbeat sensor for sensing a heartbeat of an occupying item of
the seat.
23. The vehicle of claim 1, wherein said transducers include a
force, pressure or strain gage arranged to measure the weight
applied to the entire seat.
24. The vehicle of claim 23, wherein the seat includes a support
structure for supporting the seat above a floor of a passenger
compartment of the vehicle, said force, pressure or strain gage
being attached to the support structure.
25. The vehicle of claim 1, wherein said transducers include a
plurality of electromagnetic wave sensors capable of receiving
waves at least from a space above the seat, each of said
electromagnetic wave sensors being arranged at a different
location.
26. The vehicle of claim 1, wherein said transducers include
wave-receiving sensors capable of receiving waves modified by
passing through a space above the seat.
27. The vehicle of claim 26, wherein at least one of said
wave-receiving sensors is a capacitive sensor.
28. A method of developing a system for determining the occupancy
state of a seat in the vehicle occupied by at least one occupying
item, comprising the steps of: mounting transducers in the vehicle;
forming at least one database comprising multiple data sets, each
of the data sets representing a different occupancy state of the
seat and being formed by receiving data from the transducers while
the seat is in that occupancy state, and processing the data
received from the transducers; creating a combination neural
network from the at least one database capable of producing an
output indicative of the occupancy state of the seat upon inputting
a data set representing an occupancy state of the seat; and
inputting a data set representing the current occupancy state of
the seat into the combination neural network to obtain the output
indicative of the current occupancy state of the seat.
29. The method of claim 28, wherein the step of creating the
combination neural network comprises the step of training a neural
network to determine the type of the occupying item based on data
from at least some of the transducers.
30. The method of claim 28, wherein the step of creating the
combination neural network comprises the step of training a neural
network to determine the size of the occupying item based on data
from at least some of the transducers.
31. The method of claim 28, wherein the step of creating the
combination neural network comprises the step of training a neural
network to determine the position of the occupying item based on
data from at least some of the transducers.
32. The method of claim 28, wherein the step of creating the
combination neural network comprises the steps of training a first
neural network to determine the type of the occupying item based on
data from at least some of the transducers and training a second
neural network to determine the size of the occupying item based on
data from at least some of the transducers.
33. The method of claim 32, wherein the step of creating the
combination neural network comprises the step of training a
plurality of additional neural networks to determine the position
of the occupying item for a respective, different size of occupying
item based on data from at least some of the transducers.
34. The method of claim 33, wherein the step of creating the
combination neural network further comprises the step of training
the additional neural networks to consider the position of the
occupying item at a prior time when determining the position for
the current occupancy state.
35. The method of claim 28, wherein the step of creating the
combination neural network comprises the steps of training a first
neural network to identify the occupying item based on data from at
least some of the transducers and training a second neural network
to determine the position of the identified occupying item based on
data from at least some of the transducers.
36. The method of claim 28, wherein the step of creating the
combination neural network comprises the steps of training a first
neural network to determine the current occupancy state of the seat
based on data from at least some of the transducers and training a
second neural network to determine whether the data set
representing the current occupancy state of the seat and formed
from data from the transducers is similar to a data set on which
the first neural network is trained whereby the second neural
network prevents unreasonable data sets from being input to the
first neural network.
37. The method of claim 28, wherein the step of creating the
combination neural network comprises the steps of training a first
neural network to determine whether the occupying item is a child
seat based on data from at least some of the transducers, and
training a second neural network to determine the orientation of
the child seat based on data from at least some of the transducers
when the first neural network determines that the occupying item is
a child seat.
38. The method of claim 37, wherein the step of creating the
combination neural network further comprises the step of training a
third neural network to determine the position of the child seat
based on data from at least some of the transducers.
39. The method of claim 28, wherein the step of creating the
combination neural network comprises the steps of training a
plurality of neural networks to determine the occupancy state based
on data from a respective set of the transducers whereby each set
of transducers is different than other sets of transducers, further
comprising the step of factoring the determination of the occupancy
states by the plurality of neural networks in the determination of
the occupancy state.
40. The method of claim 28, further comprising the step of:
pre-processing the data prior to processing the data to form the
data sets.
41. The method of claim 40, wherein the pre-processing step
comprises the step of using data created from features of the data
in the data set.
42. The method of claim 28, further comprising the step of: biasing
the combination neural network toward a particular occupancy state
thereby increasing the accuracy of identifying that occupancy
state.
43. The method of claim 28, wherein the step of creating the
combination neural network further comprises the step of training
the combination neural network to consider the occupancy state at a
prior time when determining the current occupancy state.
44. A method of developing a database for use in developing a
system for determining the occupancy state of a vehicle seat by an
occupying item, comprising the steps of: mounting transducers in
the vehicle; providing the seat with an initial occupancy state;
receiving data from the transducers; processing the data from the
transducers to form a data set representative of the initial
occupancy state of the vehicle seat; changing the occupancy state
of the seat and repeating the data collection process to form
another data set; collecting at least 1000 data sets into a first
database, each data set representing a different occupancy state of
the seat; creating a combination neural network from the first
database which correctly identifies the occupancy state of the seat
for most of the data sets in the first database; testing the
combination neural network using a second database of data sets
which were not used in the creation of the combination neural
network; identifying the occupancy states in the second database
which were not correctly identified by the combination neural
network; collecting new data comprising similar occupancy states to
the incorrectly identified states; combining this new data with the
first database; creating a new combination neural network based on
the combined database; and repeating this process until the desired
accuracy of the combination neural network is achieved.
45. The method of claim 44, wherein the step of creating the
combination neural network comprises the step of training a neural
network to determine the type of the occupying item based on data
from at least some of the transducers.
46. The method of claim 44, wherein the step of creating the
combination neural network comprises the step of training a neural
network to determine the size of the occupying item based on data
from at least some of the transducers.
47. The method of claim 44, wherein the step of creating the
combination neural network comprises the step of training a neural
network to determine the position of the occupying item based on
data from at least so me of the transducers.
48. The method of claim 44, wherein the step of creating the
combination neural network comprises the steps of training a first
neural network to determine the type of the occupying item based on
data from at least some of the transducers and training a second
neural network to determine the size of the occupying item based on
data from at least some of the transducers.
49. The method of claim 48, wherein the step of creating the
combination neural network comprises the step of training a
plurality of additional neural networks to determine the position
of the occupying item for a respective, different size of occupying
item based on data from at least some of the transducers.
50. The method of claim 49, wherein the step of creating the
combination neural network further comprises the step of training
the additional neural networks to consider the position of the
occupying item at a prior time when determining the position for
the current occupancy state.
51. The method of claim 44, wherein the step of creating the
combination neural network comprises the steps of training a first
neural network to identify the occupying item based on data from at
least some of the transducers and training a second neural network
to determine the position of the identified occupying item based on
data from at least some of the transducers.
52. The method of claim 44, wherein the step of creating the
combination neural network comprises the steps of training a first
neural network to determine the current occupancy state of the seat
based on data from at least some of the transducers and training a
second neural network to determine whether the data set
representing the current occupancy state of the seat and formed
from data from the transducers is similar to a data set on which
the first neural network is trained whereby the second neural
network prevents unreasonable data sets from being input to the
first neural network.
53. The method of claim 44, wherein the step of creating the
combination neural network comprises the steps of training a first
neural network to determine whether the occupying item is a child
seat based on data from at least some of the transducers, and
training a second neural network to determine the orientation of
the child seat based on data from at least some of the transducers
when the first neural network determines that the occupying item is
a child seat.
54. The method of claim 53, wherein the step of creating the
combination neural network further comprises the step of training a
third neural network to determine the position of the child seat
based on data from at least some of the transducers.
55. The method of claim 44, wherein the step of creating the
combination neural network comprises the steps of training a
plurality of neural networks to determine the occupancy state of
the seat based on data from a respective set of the transducers
whereby each set of transducers is different than other sets of
transducers, further comprising the step of factoring the
determination of the occupancy states by the plurality of neural
networks in the determination of the occupancy state.
56. The method of claim 44, wherein the step of creating the
combination neural network comprises the step of training a
plurality of neural networks to perform a respective, different
function.
57. The method of claim 56, wherein the step of creating the
combination neural network comprises the step of training the
plurality of neural networks on different data sets of the first
database.
58. The method of claim 44, further comprising the step of:
pre-processing the data prior to processing the data to form the
data sets.
59. The method of claim 58, wherein the pre-processing step
comprises the step of using data created from features of the data
in the data set.
60. The method of claim 44, further comprising the step of: biasing
the combination neural network toward a particular occupancy state
thereby increasing the accuracy of identifying that occupancy
state.
61. The method of claim 44. further comprising the step of:
creating some of the occupancy states of the seat using live human
beings.
62. The method of claim 44, further comprising the step of: varying
the environmental conditions inside the vehicle while data is being
collected.
63. The method of claim 62, wherein the environmental conditions
varying step comprises the step of creating thermal gradients
within the passenger compartment.
64. The method of claim 44, wherein the varying occupancy states
are created by automatically moving various vehicle complements
such as the seat and seatback during the data collection
process.
65. The method of claim 44, further comprising the step of:
validating proper functioning of the transducers and the data
collection process by using a standard occupancy state of the seat
and corresponding prerecorded data set, wherein a data set is
periodically taken of the standard occupancy state and compared
with the prerecorded data set.
66. The method of claim 44, wherein the step of creating the
combination neural network the step of training the combination
neural network to consider the occupancy state at a prior time when
determining the current occupancy state.
67. A method of developing a system for determining the occupancy
state of a vehicle seat in a passenger compartment of a vehicle,
comprising the steps of: mounting a set of transducers on the
vehicle; receiving data from the transducers; processing the data
from transducers to form a data set representative of the occupancy
state of the vehicle; forming a database comprising multiple data
sets; creating a combination neural network from the database
capable of producing an output indicative of the occupancy state of
the vehicle seat upon inputting a new data set; developing a
measure of system accuracy; removing at least one of the
transducers from the transducer set; creating a new database
containing data only from the reduced number of transducers;
creating a new combination neural network based on the new
database; testing the new combination neural network to determine
the new system accuracy; and continuing the process of removing
transducers, combination neural network creation and testing until
the minimum number of sensors is determined which produces a
combination neural network having desired accuracy.
68. The method of claim 67, wherein the transducers are selected
from a group consisting of ultrasonic transducers, optical sensors,
capacitive sensors, weight sensors, seat position sensors, seatback
position sensors, seat belt buckle sensors, seatbelt payout
sensors, infrared sensors, inductive sensors and radar sensors.
69. A vehicle including a system for determining the occupancy
state of a seat in the vehicle occupied by an occupying item, the
system comprising: a plurality of transducers arranged in the
vehicle, each of said transducers providing data relating to the
occupancy state of the seat; and a processing unit coupled to said
transducers for receiving the data from said transducers and
processing the data to obtain an output indicative of the current
occupancy state of the seat, a combination neural network created
from a plurality of data sets being resident in said processing
unit, each of said data sets representing a different occupancy
state of the seat and being formed from data from said transducers
while the seat is in that occupancy state, said combination neural
network providing the output indicative of the current occupancy
state of the seat upon inputting a data set representing the
current occupancy state of the seat and being formed from data from
at least some of said transducers.
70. The vehicle of claim 69, wherein said combination neural
network comprises at least one neural network trained to determine
at least one of the type, size and position of the occupying item
based on data from at least some of said transducers.
71. The vehicle of claim 69, wherein said combination neural
network comprises a first neural network trained to identify the
occupying item based on data from at least some of said transducers
and a second neural network trained to determine the position of
the occupying item based on data from at least some of said
transducers.
72. The vehicle of claim 69, wherein said combination neural
network comprises a first neural network trained to determine the
current occupancy state of the seat based on data from at least
some of said transducers and a second neural network trained to
determine whether the data set representing the current occupancy
state of the seat and formed from data from said transducers is
similar to a data set on which said first neural network is trained
whereby said second neural network prevents unreasonable data sets
from being input to said first neural network.
73. The vehicle of claim 69, wherein said combination neural
network comprises a first neural network trained to determine
whether the occupying item is a child seat based on data from at
least some of said transducers, a second neural network trained to
determine the orientation of the child seat based on data from at
least some of said transducers when said first neural network
determines that the occupying item is a child seat.
74. The vehicle of claim 73, wherein said combination neural
network comprises a third neural network trained to determine the
position of the child seat based on data from at least some of said
transducers.
75. The vehicle of claim 69, wherein said combination neural
network comprises a plurality of neural networks each trained to
determine the occupancy state of the seat based on data from a
respective set of said transducers whereby each set of transducers
is different than other sets of transducers, said processor means
being arranged to factor the determination of the occupancy states
by said plurality of neural networks in the determination of the
occupancy state of the seat.
76. The vehicle of claim 69, wherein said transducers include a
plurality of electromagnetic wave sensors capable of receiving
waves at least from a space above the seat, each of said
electromagnetic wave sensors being arranged at a different
location.
77. The vehicle of claim 69, wherein said transducers include
wave-receiving sensors capable of receiving waves modified by
passing through a space above the seat.
78. The vehicle of claim 77, wherein at least one of said
wave-receiving sensors is a capacitive sensor.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is continuation-in-part of U.S. patent
application Ser. No. 09/474,147 filed Dec. 29, 1999 which in turn
is a continuation-in-part of U.S. patent application Ser. No.
09/382,406 filed Aug. 24, 1999 which is a continuation-in-part of
U.S. patent application Ser. No. 08/919,823, now U.S. Pat. No.
5,943,295, which in turn is a continuation-in-part of U.S. patent
application Ser. No. 08/798,029 filed Feb. 6, 1997, now
abandoned.
[0002] This application claims priority under 35 U.S.C.
.sctn.119(e) of U.S. provisional patent application Ser. No.
60/136,163 filed May 27, 1999 through U.S. patent application Ser.
No. 09/474,147.
[0003] This application is related to, on the grounds that it
contains common disclosure with: (i) U.S. Pat. No. 5,653,462
entitled "Vehicle Occupant Position and Velocity Sensor" filed Jul.
21, 1995, which is a continuation of U.S. patent application Ser.
No. 08/040,978 filed Mar. 31,1993, now abandoned, which in turn is
a continuation of U.S. patent application Ser. No. 07/878,571 filed
May 5, 1992, now abandoned; (ii) U.S. Pat. No. 5,829,782 entitled
"Vehicle Interior Identification and Monitoring System" filed May
9, 1994; (iii) U.S. Pat. No. 5,845,000 entitled "Optical
Identification and Monitoring System Using Pattern Recognition for
Use with Vehicles" filed Jun. 7, 1995; (iv) U.S. Pat. No. 5,822,707
entitled "Automatic Vehicle Seat Adjuster" filed Jun. 7, 1995; (v)
U.S. Pat. No. 5,748,473 entitled "Automatic Vehicle Seat Adjuster"
filed Jun. 7, 1995; and (vi) U.S. Pat. No. 5,835,613 entitled
"Optical Identification and Monitoring System Using Pattern
Recognition for use with Vehicles" filed Jun. 7, 1995, which are
all incorporated by reference herein.
FIELD OF THE INVENTION
[0004] The present invention relates generally to the field of
determining the occupancy state of the vehicle which entails
sensing, detecting, monitoring and/or identifying various objects,
and parts thereof, which are located within the passenger
compartment of the vehicle. The occupancy state is a broad or
narrow description of the state or condition of one or more
occupying items in the vehicle. Thus, a determination of the
occupancy state may include a determination of the type or class of
any occupying items, the size of any occupying items, the position
of any occupying item including the orientation of occupying items,
the identification of any occupying items and/or the status of any
occupying items (whether the occupying items are conscious or
unconscious). The determination of the occupancy state is used to
control a vehicular component.
[0005] In particular, the present invention relates to an efficient
and highly reliable system for evaluating the occupancy of a
vehicle by detecting the presence and optionally orientation of
objects in the seats of the passenger compartment, e.g., a rear
facing child seat (RFCS) situated in the passenger compartment in a
location where it may interact with a deploying occupant protection
apparatus, such as an airbag, and/or for detecting an
out-of-position occupant. The system permits the control and
selective suppression of deployment of the occupant protection
apparatus when the deployment may result in greater injury to the
occupant than the crash forces themselves. This is accomplished in
part through a specific placement of transducers of the system, the
use of a pattern recognition system, possibly a trained neural
network and combinations of neural networks called modular neural,
voting or ensemble neural networks, and/or a novel analysis of the
signals from the transducers.
BACKGROUND OF THE INVENTION
[0006] 1. Prior Art on Sensing of Out-of-Position Occupants and
Rear Facing Child Seats
[0007] Whereas thousands of lives have been saved by airbags, a
large number of people have also been injured, some seriously, by
the deploying airbag, and thus significant improvements to the
airbag system are necessary. As discussed in detail in one or more
of the patents and patent applications cross-referenced above, for
a variety of reasons, vehicle occupants may be too close to the
airbag before it deploys and can be seriously injured or killed as
a result of any deployment thereof. Also, a child in a rear facing
child seat which is placed on the right front passenger seat is in
danger of being seriously injured if the passenger airbag deploys.
For these reasons and, as first publicly disclosed in Breed, D. S.
"How Airbags Work" presented at the International Conference on
Seatbelts and Airbags in 1993, in Canada, occupant position sensing
and rear facing child seat detection is required in order to
minimize the damages caused by deploying airbags. It also may be
required in order to minimize the damage caused by the deployment
of other types of occupant protection and/or restraint devices
which might be installed in the vehicle.
[0008] Initially, these systems will solve the out-of-position
occupant and the rear facing child seat problems related to current
airbag systems and prevent unneeded and unwanted airbag deployments
when a front seat is unoccupied. However, airbags are now under
development to protect rear seat occupants in vehicle crashes and
all occupants in side impacts. A system is therefore needed to
detect the presence of occupants, determine if they are
out-of-position, defined below, and to identify the presence of a
rear facing child seat in the rear seat. Future automobiles are
expected to have eight or more airbags as protection is sought for
rear seat occupants and from side impacts. In addition to
eliminating the disturbance and possible harm of unnecessary airbag
deployments, the cost of replacing these airbags will be excessive
if they all deploy in an accident needlessly.
[0009] Inflators now exist which will adjust the amount of gas
flowing to or from the airbag to account for the size and position
of the occupant and for the severity of the accident. The vehicle
identification and monitoring system (VIMS) discussed in U.S. Pat.
Nos. 5,829,782, and 5,943,295 among others, will control such
inflators based on the presence and position of vehicle occupants
or of a rear facing child seat. The instant invention is concerned
with the process of adapting the vehicle interior monitoring
systems to a particular vehicle model and achieving a high system
accuracy and reliability as discussed in greater detail below as
well as the resulting pattern recognition system architecture.
[0010] The automatic adjustment of the deployment rate of the
airbag based on occupant identification and position and on crash
severity has been termed "smart airbags". Central to the
development of smart airbags is the occupant identification and
position determination systems described in the above-referenced
patents and patent applications and to the methods described herein
for adapting those systems to a particular vehicle model. To
complete the development of smart airbags, an anticipatory crash
detecting system such as disclosed in U.S. patent application Ser.
No. 08/247,760 filed May 23, 1994 is also desirable. Prior to the
implementation of anticipatory crash sensing, the use of a neural
network smart crash sensor which identifies the type of crash and
thus its severity based on the early part of the crash acceleration
signature should be developed and thereafter implemented. U.S. Pat.
No. 5,684,701 (Breed) describes a crash sensor based on neural
networks. This crash sensor, as with all other crash sensors,
determines whether or not the crash is of sufficient severity to
require deployment of the airbag and, if so, initiates the
deployment. A neural network based on a smart airbag crash sensor
could also be designed to identify the crash and categorize it with
regard to severity thus permitting the airbag deployment to be
matched not only to the characteristics and position of the
occupant but also the severity and timing of the crash itself as
described in more detail in U.S. Pat. No. 5,943,295 referenced
above.
[0011] The need for an occupant out-of-position sensor has also
been observed by others and several methods have been described in
certain U.S. patents for determining the position of an occupant of
a motor vehicle. However, no patents have been found that describe
the methods of adapting such sensors to a particular vehicle model
to obtain high system accuracy or to a resulting architecture
combination of pattern recognition algorithms. Each of these
systems will be discussed below and have significant
limitations.
[0012] In White et al. (U.S. Pat. No. 5,071,160), for example, a
single acoustic sensor and detector is described and, as
illustrated, is disadvantageously mounted lower than the steering
wheel. White et al. correctly perceive that such a sensor could be
defeated, and the airbag falsely deployed, by an occupant adjusting
the control knobs on the radio and thus they suggest the use of a
plurality of such sensors. White et al. does not disclose where the
such sensors would be mounted, other than on the instrument panel
below the steering wheel, or how they would be combined to uniquely
monitor particular locations in the passenger compartment and to
identify the object(s) occupying those locations. The adaptation
process to vehicles is not described nor is a combination of
pattern recognition algorithms.
[0013] Mattes et al. (U.S. Pat. No. 5,118,134) describe a variety
of methods for measuring the change in position of an occupant
including ultrasonic, active or passive infrared radiation,
microwave radar sensors, and an electric eye. The use of these
sensors is to measure the change in position of an occupant during
a crash and they use that information to assess the severity of the
crash and thereby decide whether or not to deploy the airbag. They
are thus using the occupant motion as a crash sensor. No mention is
made of determining the out-of-position status of the occupant or
of any of the other features of occupant monitoring as disclosed in
the above-referenced patents and/or patent applications. It is
interesting to note that nowhere does Mattes et al. discuss how to
use a combination of ultrasonic sensors/transmitters to identify
the presence of a human occupant and then to find his/her location
in the passenger compartment or any pattern recognition algorithm
let alone a combination of such algorithms.
[0014] The object of an occupant out-of-position sensor is to
determine the location of the head and/or chest of the vehicle
occupant in the passenger compartuent relative to the occupant
protection apparatus, such as an airbag, since it is the impact of
either the head or chest with the deploying airbag which can result
in serious injuries. Both White et al. and Mattes et al. disclose
only lower mounting locations of their sensors which are mounted in
front of the occupant such as on the dashboard or below the
steering wheel. Both such mounting locations are particularly prone
to detection errors due to positioning of the occupant's hands,
arms and legs. This would require at least three, and preferably
more, such sensors and detectors and an appropriate logic
circuitry, or pattern recognition system, which ignores readings
from some sensors if such readings are inconsistent with others,
for the case, for example, where the driver's arms are the closest
objects to two of the sensors. The determination of the proper
transducer mounting locations, aiming and field angles and pattern
recognition system architectures for a particular vehicle model are
not disclosed in either White et al. or Mattes et al. and are part
of the vehicle model adaptation process described herein.
[0015] White et al. also describe the use of error correction
circuitry, without defining or illustrating the circuitry, to
differentiate between the velocity of one of the occupant's hands,
as in the case where he/she is adjusting the knob on the radio, and
the remainder of the occupant. Three ultrasonic sensors of the type
disclosed by White et al. might, in some cases, accomplish this
differentiation if two of them indicated that the occupant was not
moving while the third was indicating that he or she was moving.
Such a combination, however, would not differentiate between an
occupant with both hands and arms in the path of the ultrasonic
transmitter at such a location that they were blocking a
substantial view of the occupant's head or chest. Since the sizes
and driving positions of occupants are extremely varied, trained
pattern recognition systems, such as neural networks and
combinations thereof, are required when a clear view of the
occupant, unimpeded by his/her extremities, cannot be guaranteed.
White et al. do not suggest the use of such neural networks.
[0016] Fujita et al., in U.S. Pat. No. 5,074,583, describe another
method of determining the position of the occupant but do not use
this information to control and suppress deployment of an airbag if
the occupant is out-of-position, or if a rear facing child seat is
present. In fact, the closer that the occupant gets to the airbag,
the faster the inflation rate of the airbag is according to the
Fujita et al. patent, which thereby increases the possibility of
injuring the occupant. Fujita et al. do not measure the occupant
directly but instead determine his or her position indirectly from
measurements of the seat position and the vertical size of the
occupant relative to the seat. This occupant height is determined
using an ultrasonic displacement sensor mounted directly above the
occupant's head.
[0017] It is important to note that in all cases in the above-cited
prior art, except those assigned to the current assignee of the
instant invention, no mention is made of the method of determining
transducer location, deriving the algorithms or other system
parameters that allow the system to accurately identify and locate
an object in the vehicle. In contrast, in one implementation of the
instant invention, the return ultrasonic echo pattern over several
milliseconds corresponding to the entire portion of the passenger
compartment volume of interest is analyzed from multiple
transducers and sometimes combined with the output from other
transducers, providing distance information to many points on the
items occupying the passenger compartment.
[0018] Many of the teachings of this invention are based on pattern
recognition technologies as taught in numerous textbooks and
technical papers. Central to the diagnostic teachings of this
invention are the manner in which the diagnostic module determines
a normal pattern from an abnormal pattern and the manner in which
it decides what data to use from the vast amount of data available.
This is accomplished using pattern recognition technologies, such
as artificial neural networks, and training. The theory of neural
networks including many examples can be found in several books on
the subject including: Techniques And Application Of Neural
Networks, edited by Taylor, M. and Lisboa, P., Ellis Horwood, West
Sussex, England, 1993; Naturally Intelligent Systems, by Caudill,
M. and Butler, C., MIT Press, Cambridge Mass., 1990; J. M. Zaruda,
Introduction to Artificial Neural Systems, West publishing Co.,
N.Y., 1992 and, Digital Neural Networks, by Kung, S. Y., PTR
Prentice Hall, Englewood Cliffs, N.J., 1993, Eberhart, R., Simpson,
P. and Dobbins, R., Computational Intelligence PC Tools, Academic
Press, Inc., 1996, Orlando, Fla., all of which are included herein
by reference. The neural network pattern recognition technology is
one of the most developed of pattern recognition technologies. The
invention described herein uses combinations of neural networks to
improve the pattern recognition process.
[0019] Other patents describing occupant sensor systems include
U.S. Pat. No. 5,482,314 (Corrado et al.) and U.S. Pat. No.
5,890,085 (Corrado et al.). These patents describe a system for
sensing the presence, position and type of an occupant in a seat of
a vehicle for use in enabling or disabling a related airbag
activator. A preferred implementation of the system includes two or
more different but collocated sensors which provide information
about the occupant and this information is fused or combined in a
microprocessor circuit to produce an output signal to the airbag
controller. According to Corrado et al., the fusion process
produces a decision as to whether to enable or disable the airbag
with a higher reliability than a single phenomena sensor or
non-fused multiple sensors. By fusing the information from the
sensors to make a determination as to the deployment of the airbag,
each sensor has only a partial effect on the ultimate deployment
determination. The sensor fusion process is a crude pattern
recognition process based on deriving the fusion "rules" by a trial
and error process rather than by training.
[0020] The sensor fusion method of Corrado et al. requires that
information from the sensors be combined prior to processing by an
algorithm in the microprocessor. This combination could be found to
unnecessarily complicate the processing of the data from the
sensors and other data processing methods might provide better
results. For example, as discussed more fully below, it has been
found to be advantageous to use a more efficient pattern
recognition algorithm such as a combination of neural networks or
fuzzy logic algorithms which are arranged to receive a separate
stream of data from each sensor, without that data being combined
with data from the other sensors (as in done in Corrado et al.)
prior to analysis by the pattern recognition algorithms. In this
regard, it is critical to appreciate that sensor fusion is a form
of pattern recognition but is not a neural network and that
significant and fundamental differences exist between sensor fusion
and neural networks. Thus, some embodiments of the invention
described below differ from that of Corrado et al. because they
include a microprocessor which is arranged to accept only a
separate stream of data from each sensor such that the stream of
data from the sensors are not combined with one another. Further,
the microprocessor processes each separate stream of data
independent of the processing of the other streams of data (i.e.,
without the use of any fusion matrix as in Corrado et al.).
[0021] 2. Definitions
[0022] The use of pattern recognition, or more particularly how it
is used, is central to the instant invention. In the above-cited
prior art, except in that assigned to the current assignee of the
instant invention, pattern recognition which is based on training,
as exemplified through the use of neural networks, is not mentioned
for use in monitoring the interior passenger compartment or
exterior environments of the vehicle. Thus, the methods used to
adapt such systems to a vehicle are also not mentioned.
[0023] "Pattern recognition" as used herein will generally mean any
system which processes a signal that is generated by an object
(e.g., representative of a pattern of returned or received
impulses, waves or other physical property specific to and/or
characteristic of and/or representative of that object) or is
modified by interacting with an object, in order to determine to
which one of a set of classes that the object belongs. Such a
system might determine only that the object is or is not a member
of one specified class, or it might attempt to assign the object to
one of a larger set of specified classes, or find that it is not a
member of any of the classes in the set. The signals processed are
generally a series of electrical signals coming from transducers
that are sensitive to acoustic (ultrasonic) or electromagnetic
radiation (e.g., visible light, infrared radiation, capacitance or
electric and magnetic fields), although other sources of
information are frequently included. Pattern recognition systems
generally involve the creation of a set of rules that permit the
pattern to be recognized. These rules can be created by fuzzy logic
systems, statistical correlations, or through sensor fusion
methodologies as well as by trained pattern recognition systems
such as neural networks.
[0024] A trainable or a trained pattern recognition system as used
herein generally means a pattern recognition system which is taught
to recognize various patterns constituted within the signals by
subjecting the system to a variety of examples. The most successful
such system is the neural network used either singly or as a
combination of neural networks. Thus, to generate the pattern
recognition algorithm, test data is first obtained which
constitutes a plurality of sets of returned waves, or wave
patterns, from an object (or from the space in which the object
will be situated in the passenger compartment, i.e., the space
above the seat) and an indication of the identify of that object. A
number of different objects are tested to obtain the unique wave
patterns from each object. As such, the algorithm is generated, and
stored in a computer processor, and which can later be applied to
provide the identity of an object based on the wave pattern being
received during use by a receiver connected to the processor and
other information. For the purposes here, the identity of an object
sometimes applies to not only the object itself but also to its
location and/or orientation in the passenger compartment. For
example, a rear facing child seat is a different object than a
forward facing child seat and an out-of-position adult is a
different object than a normally seated adult.
[0025] To "identify" as used herein will generally mean to
determine that the object belongs to a particular set or class. The
class may be one containing, for example, all rear facing child
seats, one containing all human occupants, or all human occupants
not sitting in a rear facing child seat depending on the purpose of
the system.
[0026] In the case where a particular person is to be recognized,
the set or class will contain only a single element, i.e., the
person to be recognized.
[0027] An "object" in a vehicle or an "occupying item" of a seat
may be a living occupant such as a human or a dog, another living
organism such as a plant, or an inanimate object such as a box or
bag of groceries or an empty child seat.
[0028] "Out-of-position" as used for an occupant will generally
mean that the occupant, either the driver or a passenger, is
sufficiently close to the occupant protection apparatus (airbag)
prior to deployment that he or she is likely to be more seriously
injured by the deployment event itself than by the accident. It may
also mean that the occupant is not positioned appropriately in
order to attain beneficial, restraining effects of the deployment
of the airbag. An occupant is too close to the airbag when the
occupant's head or chest is closer than some distance, such as
about 5 inches, from the deployment door of the airbag module. The
actual distance value where airbag deployment should be suppressed
depends on the design of the airbag module and is typically farther
for the passenger airbag than for the driver airbag. "Transducer"
as used herein will generally mean the combination of a transmitter
and a receiver. In come cases, the same device will serve both as
the transmitter and receiver while in others two separate devices
adjacent to each other will be used. In some cases, a transmitter
is not used and in such cases transducer will mean only a receiver.
Transducers include, for example, capacitive, inductive,
ultrasonic, electromagnetic (antenna, CCD, CMOS arrays), weight
measuring or sensing devices.
[0029] "Adaptation" as used here will generally represent the
method by which a particular occupant sensing system is designed
and arranged for a particular vehicle model. It includes such
things as the process by which the number, kind and location of
various transducers is determined. For pattern recognition systems,
it includes the process by which the pattern recognition system is
designed and then taught to recognize the desired patterns. In this
connection, it will usually include (1) the method of training, (2)
the makeup of the databases used for training, testing and
validating the particular system, or, in the case of a neural
network, the particular network architecture chosen, (3) the
process by which environmental influences are incorporated into the
system, and (4) any process for determining the pre-processing of
the data or the post processing of the results of the pattern
recognition system. The above list is illustrative and not
exhaustive. Basically, adaptation includes all of the steps that
are undertaken to adapt transducers and other sources of
information to a particular vehicle to create the system which
accurately identifies and determines the location of an occupant or
other object in a vehicle.
[0030] A "combination neural network" as used herein will generally
apply to any combination of two or more neural networks that are
either connected together or that analyze all or a portion of the
input data. A combination neural network can be used to divide
tasks in solving a particular occupant problem. For example, one
neural network can be used to identify an object occupying a
passenger compartment of an automobile and a second neural network
can be used to determine the position of the object or its location
with respect to the airbag, for example, within the passenger
compartment. In another case, one neural network can be used merely
to determine whether the data is similar to data upon which a main
neural network has trained or whether there is something radically
different about this data and therefore that the data should not be
analyzed.
[0031] With respect to a comparative analysis performed by neural
networks to that perform by the human mind, once the human mind has
identified that the object observer is a tree, the mind does not
try to determine whether it is the black bear or a grizzly. Further
observation on the tree might center on whether it is a pine tree,
an oak tree etc. Thus the human mind appears to operate in some
manner like a hierarchy of neural networks. Similarly, neural
networks for analyzing the occupancy of the vehicle can be
structured such that higher order networks are used to determine,
for example, whether there is an occupying item of any kind
present. This could be followed by the neural network that, knowing
that there is information on the item, attempts to categorize the
item into child seats and human adults etc., i.e., determine the
type of item Once it has decided that a child seat is present, then
another neural network can be used to determine whether the child
seat is rear facing or forward facing. Once the decision has been
made that the child seat is facing rearward, the position of the
child seat relative to the airbag, for example, can be handled by
still another neural network. The overall accuracy of the system
can be substantially improved by breaking the pattern recognition
process down into a larger number of smaller pattern recognition
problems.
[0032] In some cases, the accuracy of the pattern recognition
process can be improved if the neural network has data indicating
its own recent decisions. Thus, for example, if the neural network
system had determined that a forward facing adult was present, then
that information can be used as input into another neural network,
biasing any results toward the forward facing human compared to a
rear facing child seat, for example. Similarly, for the case when
an occupant is being tracked in his or her forward motion during a
crash, for example, the location of the occupant at the previous
calculation time step can be valuable information to determining
the location of the occupant from the current data. There is a
limited distance an occupant can move in 10 milliseconds, for
example.
[0033] In this latter example, feedback of the decision of the
neural network tracking algorithm becomes important input into the
same algorithm for the calculation of the position of the occupant
at the next time step. What has been described above is generally
referred to as modular neural networks with and without feedback.
Actually, the feedback does not have to be from the output to the
input of the same neural network. The feedback from a downstream
neural network could be input to an upstream neural network, for
example.
[0034] The neural networks can be combined in other ways, for
example in a voting situation. Sometimes the data upon which the
system is trained is sufficiently complex or imprecise that
different views of the data will give different results. For
example, a subset of transducers may be used to train one neural
network and another subset to train a second neural network etc.
The decision can then be based on a voting of the parallel neural
networks, known as an ensemble neural networks. In the past, neural
networks have usually only been used in the form of a single neural
network algorithm for identifying the occupancy state of an
automobile. This invention is primarily advancing the state of the
art and using combination neural networks wherein two or more
neural networks are combined to arrive at a decision.
[0035] In the description herein on anticipatory sensing, the term
"approaching" when used in connection with the mention of an object
or vehicle approaching another will generally mean the relative
motion of the object toward the vehicle having the anticipatory
sensor system. Thus, in a side impact with a tree, the tree will be
considered as approaching the side of the vehicle and impacting the
vehicle. In other words, the coordinate system used in general will
be a coordinate system residing in the target vehicle. The "target"
vehicle is the vehicle which is being impacted. This convention
permits a general description to cover all of the cases such as
where (i) a moving vehicle impacts into the side of a stationary
vehicle, (ii) where both vehicles are moving when they impact, or
(iii) where a vehicle is moving sideways into a stationary vehicle,
tree or wall.
[0036] Also, for the purposes herein, a "wave sensor" or "wave
transducer" is generally any device, which senses either ultrasonic
or electromagnetic waves. An electromagnetic wave sensor, for
example, includes devices that sense any portion of the
electromagnetic spectrum from ultraviolet down to a few hertz. The
most commonly used kinds of electromagnetic wave sensors include
CCD and CMOS arrays for sensing visible and/or infrared waves,
millimeter wave and microwave radar, and capacitive or electric and
magnetic field monitoring sensors that rely on the dielectric
constant of the object occupying a space but also rely on the time
variation of the field, expressed by waves, to determine a change
in state. In this regard, reference is made to, for example, U.S.
patents by Kithil et al. U.S. Pat. Nos. 5,366,241, 5,602,734,
5,691,693, 5,802,479 and 5,844,486 and Jinno et al. U.S. Pat. No.
5,948,031 which are included herein by reference.
[0037] 3. Pattern Recognition Prior Art
[0038] Japanese Patent No. 3-42337 (A) to Ueno describes a device
for detecting the driving condition of a vehicle driver comprising
a light emitter for irradiating the face of the driver and a means
for picking up the image of the driver and storing it for later
analysis. Means are provided for locating the eyes of the driver
and then the irises of the eyes and then determining if the driver
is looking to the side or sleeping. Ueno determines the state of
the eyes of the occupant rather than determining the location of
the eyes relative to the other parts of the vehicle passenger
compartment. Such a system can be defeated if the driver is wearing
glasses, particularly sunglasses, or another optical device which
obstructs a clear view of his/her eyes. Pattern recognition
technologies such as neural networks are not used. The method of
finding the eyes is described but not a method of adapting the
system to a particular vehicle model.
[0039] U.S. Pat. No. 5,008,946 to Ando uses a complicated set of
rules to isolate the eyes and mouth of a driver and uses this
information to permit the driver to control the radio, for example,
or other systems within the vehicle by moving his eyes and/or
mouth. Ando uses natural light and illuminates only the head of the
driver. He also makes no use of trainable pattern recognition
systems such as neural networks, nor is there any attempt to
identify the contents of the vehicle nor of their location relative
to the vehicle passenger compartment. Rather, Ando is limited to
control of vehicle devices by responding to motion of the driver's
mouth and eyes. As with Ueno, a method of finding the eyes is
described but not a method of adapting the system to a particular
vehicle model.
[0040] U.S. Pat. No. 5,298,732 to Chen also concentrates on
locating the eyes of the driver so as to position a light filter
between a light source such as the sun or the lights of an oncoming
vehicle, and the driver's eyes. Chen does not explain in detail how
the eyes are located but does supply a calibration system whereby
the driver can adjust the filter so that it is at the proper
position relative to his or her eyes. Chen references the use of an
automatic equipment for determining the location of the eyes but
does not describe how this equipment works. In any event, in Chen,
there is no mention of monitoring the position of the occupant,
other that the eyes, determining the position of the eyes relative
to the passenger compartment, or identifying any other object in
the vehicle other than the driver's eyes. Also, there is no mention
of the use of a trainable pattern recognition system. A method for
finding the eyes is described but not a method of adapting the
system to a particular vehicle model.
[0041] U.S. Pat. No. 5,305,012 to Faris also describes a system for
reducing the glare from the headlights of an oncoming vehicle.
Faris locates the eyes of the occupant by using two spaced apart
infrared cameras using passive infrared radiation from the eyes of
the driver. Faris is only interested in locating the driver's eyes
relative to the sun or oncoming headlights and does not identify or
monitor the occupant or locate the occupant, a rear facing child
seat or any other object for that matter, relative to the passenger
compartment or the airbag. Also, Faris does not use trainable
pattern recognition techniques such as neural networks. Faris, in
fact, does not even say how the eyes of the occupant are located
but refers the reader to a book entitled Robot Vision (1991) by
Berthold Horn, published by MIT Press, Cambridge, Mass. Also, Faris
uses the passive infrared radiation rather than illuminating the
occupant with ultrasonic or electromagnetic radiation as in some
implementations of the instant invention. A method for finding the
eyes of the occupant is described but not a method of adapting the
system to a particular vehicle model.
[0042] The use of neural networks, or neural fuzzy systems, and
particular combination neural networks, as the pattern recognition
technology and the methods of adapting this to a particular
vehicle, such as the training methods, is important to this
invention since it makes the monitoring system robust, reliable and
accurate. The resulting systems are easy to implement at a low cost
making them practical for automotive applications. The cost of the
ultrasonic transducers, for example, is expected to be less than
about $1 in quantities of one million per year and CMOS cameras,
currently less than $5 each in similar quantities. Similarly, the
implementation of the techniques of the above-referenced patents
requires expensive microprocessors while the implementation with
neural networks and similar trainable pattern recognition
technologies permits the use of low cost microprocessors typically
costing less than about $5 in quantities of one million per
year.
[0043] The present invention uses sophisticated software that
develops trainable pattern recognition algorithms such as neural
networks and combination neural networks. Usually the data is
preprocessed, as discussed below, using various feature extraction
techniques and the results post-processed to improve system
accuracy. A non-automotive example of such a pattern recognition
system using neural networks on sonar signals is discussed in two
papers by Gorman, R. P. and Sejnowski, T. J. "Analysis of Hidden
Units in a Layered Network Trained to Classify Sonar Targets",
Neural Networks, Vol. 1. pp. 75-89, 1988, and "Learned
Classification of Sonar Targets Using a Massively Parallel
Network", IEEE Transactions on Acoustics, Speech, and Signal
Processing, Vol. 36, No. 7, July 1988. Examples of feature
extraction techniques can be found in U.S. Pat. No. 4,906,940
entitled "Process and Apparatus for the Automatic Detection and
Extraction of Features in Images and Displays" to Green et al.
Examples of other more advanced and efficient pattern recognition
techniques can be found in U.S. Pat. No. 5,390,136 entitled
"Artificial Neuron and Method of Using Same" and U.S. Pat. No.
5,517,667 entitled "Neural Network That Does Not Require Repetitive
Training" to Wang, S. T. Other examples include U.S. Pat. No.
5,235,339 (Morrison et al.), U.S. Pat. No. 5,214,744 (Schweizer et
al), U.S. Pat. No. 5,181,254 (Schweizer et al), and U.S. Pat. No.
4,881,270 (Knecht et al). All of the references herein are included
herein by reference.
[0044] 4. Ultrasonics and Optics
[0045] Both laser and non-laser optical systems in general are good
at determining the location of objects within the two dimensional
plane of the image and a pulsed laser radar system in the scanning
mode can determine the distance of each part of the image from the
receiver by measuring the time of flight through range gating
techniques. Distance can also be determined by using modulated
electromagnetic radiation and measuring the phase difference
between the transmitted and received waves. It is also possible to
determine distance with the non-laser system by focusing, or
stereographically if two spaced apart receivers are used and, in
some cases, the mere location in the field of view can be used to
estimate the position relative to the airbag, for example. Finally,
a recently developed pulsed quantum well diode laser also provides
inexpensive distance measurements as discussed in U.S. provisional
patent application Ser. No. 60/114,507, filed Dec. 31, 1998, which
is included herein by reference as if the entire contents were
copied here.
[0046] Acoustic systems are additionally quite effective at
distance measurements since the relatively low speed of sound
permits simple electronic circuits to be designed and minimal
microprocessor capability is required. If a coordinate system is
used where the z-axis is from the transducer to the occupant,
acoustics are good at measuring z dimensions while simple optical
systems using a single CCD or CMOS arrays are good at measuring x
and y dimensions. The combination of acoustics and optics,
therefore, permits all three measurements to be made from one
location with low cost components as discussed in commonly assigned
U.S. Pat. Nos. 5,845,000 and 5,835,613 cross-referenced above.
[0047] One example of a system using these ideas is an optical
system which floods the passenger seat with infrared light coupled
with a lens and a receiver array, e.g., CCD or CMOS array, which
receives and displays the reflected light and an analog to digital
converter (ADC) which digitizes the output of the CCD or CMOS and
feeds it to an Artificial Neural Network (ANN) or other pattern
recognition system for analysis. This system uses an ultrasonic
transmitter and receiver for measuring the distances to the objects
located in the passenger seat. The receiving transducer feeds its
data into an ADC and from there the converted data is directed into
the ANN. The same ANN can be used for both systems thereby
providing full three-dimensional data for the ANN to analyze. This
system, using low cost components, will permit accurate
identification and distance measurements not possible by either
system acting alone. If a phased array system is added to the
acoustic part of the system, the optical part can determine the
location of the driver's ears, for example, and the phased array
can direct a narrow beam to the location and determine the distance
to the occupant's ears. Although the use of ultrasound for distance
measurement has many advantages, it also has some drawbacks. First,
the speed of sound limits the rate at which the position of the
occupant can be updated to approximately 10 milliseconds, which
though sufficient for most cases, is marginal if the position of
the occupant is to be tracked during a vehicle crash. Second,
ultrasound waves are diffracted by changes in air density that can
occur when the heater or air conditioner is operated or when there
is a high-speed flow of air past the transducer. Third, the
resolution of ultrasound is limited by its wavelength and by the
transducers, which are high Q tuned devices. Typically, the
resolution of ultrasound is on the order of about 2 to 3 inches.
Finally, the fields from ultrasonic transducers are difficult to
control so that reflections from unwanted objects or surfaces add
noise to the data.
[0048] Ultrasonics alone can be used in several configurations for
monitoring the interior of a passenger compartment of an automobile
as described in the above-referenced patents and patent
applications and in particular in U.S. Pat. No. 5,943,295. Using
the teachings of this invention, the optimum number and location of
the ultrasonic and/or optical transducers can be determined as part
of the adaptation process for a particular vehicle model.
[0049] In the cases of the instant invention, as discussed in more
detail below, regardless of the number of transducers used, a
trained pattern recognition system, as defined above, is used to
identify and classify, and in some cases to locate, the illuminated
object and its constituent parts.
[0050] 5. Applications
[0051] The applications for this technology are numerous as
described in the patents and patent applications listed above.
However, the main focus of the instant invention is the process and
resulting apparatus of adapting the system in the patents and
patent applications referenced above and using combination neural
networks for the detection of the presence of an occupied child
seat in the rear facing position or an out-of-position occupant and
the detection of an occupant in a normal seating position. The
system is designed so that in the former two cases, deployment of
the occupant protection apparatus (airbag) may be controlled and
possibly suppressed, and in the latter case, it will be controlled
and enabled.
[0052] One preferred implementation of a first generation occupant
sensing system, which is adapted to various vehicle models using
the teachings presented herein, is an ultrasonic occupant position
sensor. This system uses a Combination Artificial Neural Network
(CANN) to recognize patterns that it has been trained to identify
as either airbag enable or airbag disable conditions. The pattern
is obtained from four ultrasonic transducers that cover the front
passenger seating area. This pattern consists of the ultrasonic
echoes bouncing off of the objects in the passenger seat area. The
signal from each of the four transducers consists of the electrical
image of the return echoes, which is processed by the electronics.
The electronic processing comprises amplification, logarithmic
compression, rectification, and demodulation (band pass filtering),
followed by discretization (sampling) and digitization of the
signal. The only software processing required, before this signal
can be fed into the combination artificial neural network, is
normalization (i.e., mapping the input to numbers between 0 and 1).
Although this is a fair amount of processing, the resulting signal
is still considered "raw", because all information is treated
equally.
OBJECTS AND SUMMARY OF THE INVENTION
[0053] In general, it is an object of the present invention to
provide a new and improved system for identifying the presence,
position and/or orientation of an object in a vehicle.
[0054] It is another broad object of the present invention to
provide a system for accurately detecting the presence of an
occupied rear-facing child seat in order to prevent an occupant
protection apparatus, such as an airbag, from deploying, when the
airbag would impact against the rear-facing child seat if
deployed.
[0055] It is yet another broad object of the present invention to
provide a system for accurately detecting the presence of an
out-of-position occupant in order to prevent one or more deployable
occupant protection apparatus such as airbags from deploying when
the airbag(s) would impact against the head or chest of the
occupant during its initial deployment phase causing injury or
possible death to the occupant.
[0056] This invention is a system designed to identify, locate and
monitor occupants, including their parts, and other objects in the
passenger compartment and in particular an occupied child seat in
the rear facing position or an out-of-position occupant, by
illuminating the contents of the vehicle with ultrasonic or
electromagnetic radiation, for example, by transmitting radiation
waves from a wave generating apparatus into a space above the seat,
and receiving radiation modified by passing through the space above
the seat using two or more transducers properly located in the
vehicle passenger compartment, in specific predetermined optimum
locations. More particularly, this invention relates to a system
including a plurality of transducers appropriately located and
mounted and which analyze the received radiation from any object
which modifies the waves, or which analyze a change in the received
radiation caused by the presence of the object (e.g., a change in
the dielectric constant), in order to achieve an accuracy of
recognition heretofore not possible. Outputs from the receivers are
analyzed by appropriate computational means employing trained
pattern recognition technologies, and in particular combination
neural networks, to classify, identify and/or locate the contents,
and/or determine the orientation of, for example, a rear facing
child seat. In general, the information obtained by the
identification and monitoring system is used to affect the
operation of some other system, component or device in the vehicle
and particularly the passenger and/or driver airbag systems, which
may include a front airbag, a side airbag, a knee bolster, or
combinations of the same. However, the information obtained can be
used for controlling or affecting the operation of a multitude of
other vehicle systems.
[0057] When the vehicle interior monitoring system in accordance
with the invention is installed in the passenger compartment of an
automotive vehicle equipped with a occupant protection apparatus,
such as an inflatable airbag, and the vehicle is subjected to a
crash of sufficient severity that the crash sensor has determined
that the protection apparatus is to be deployed, the system has
determined (usually prior to the deployment) whether a child placed
in the child seat in the rear facing position is present and if so,
a signal has been sent to the control circuitry that the airbag
should be controlled and most likely disabled and not deployed in
the crash. It must be understood though that instead of suppressing
deployment, it is possible that the deployment may be controlled so
that it might provide some meaningful protection for the occupied
rear-facing child seat. The system developed using the teachings of
this invention also determines the position of the vehicle occupant
relative to the airbag and controls and possibly disables
deployment of the airbag if the occupant is positioned so that
he/she is likely to be injured by the deployment of the airbag. As
before, the deployment is not necessarily disabled but may be
controlled to provide protection for the out-of-position
occupant.
[0058] Principle objects and advantages of the methods in
accordance with the invention are:
[0059] 1. To provide a reliable system for recognizing the presence
of a rear-facing child seat on a particular seat of a motor
vehicle.
[0060] 2. To provide a reliable system for recognizing the presence
of a human being on a particular seat of a motor vehicle.
[0061] 3. To provide a reliable system for determining the
position, velocity or size of an occupant in a motor vehicle.
[0062] 4. To provide a reliable system for determining in a timely
manner that an occupant is out-of-position, or will become
out-of-position, and likely to be injured by a deploying
airbag.
[0063] 5. To provide a system in which transducers are located
within the passenger compartment at specific locations such that a
high reliability of classification of objects and their position is
obtained from the signals generated by the transducers.
[0064] 6. To provide a system including a variety of transducers
such as seatbelt payout sensors, seatbelt buckle sensors, seat
position sensors, seatback position sensors, and weight sensors and
which is adapted so as to constitute a highly reliable occupant
presence and position system when used in combination with
electromagnetic, ultrasonic or other radiation sensors.
[0065] Accordingly, in a vehicle including system for determining
the occupancy state of a seat therein in accordance with the
invention, the system comprises a plurality of transducers arranged
in the vehicle, each transducers providing data relating to the
occupancy state of the seat, and processor means or a processing
unit (e.g., a microprocessor) coupled to the transducers for
receiving the data from the transducers and processing the data to
obtain an output indicative of the current occupancy state of the
seat. The processor means comprise a combination neural network
algorithm created from a plurality of data sets, each representing
a different occupancy state of the seat and being formed from data
from the transducers while the seat is in that occupancy state. The
combination neural network algorithm produces the output indicative
of the current occupancy state of the seat upon inputting a data
set representing the current occupancy state of the seat and being
formed from data from the transducers. The algorithm may be a
pattern recognition algorithm or neural network algorithm generated
by a combination neural network algorithm-generating program.
[0066] In accordance with some embodiments of the invention, the
processor means are arranged to accept only a separate stream of
data from each transducer such that the stream of data from each
transducer is passed to the processor means without combining with
another stream of data. Further, the processor means may be
arranged to process each separate stream of data independent of the
processing of the other streams of data.
[0067] It is an important feature of the invention that the
transducers may be selected from a wide variety of different
sensors, all of which are affected by the occupancy state of the
seat. That is, different combinations of known sensors can be
utilized in the many variations of the invention. For example, the
sensors used in the invention may include a weight sensor arranged
in the seat, a reclining angle detecting sensor for detecting a
tilt angle of the seat between a back portion of the seat and a
seat portion of the seat, a seat position sensor for detecting the
position of the seat relative to a fixed reference point in the
vehicle, a heartbeat sensor for sensing a heartbeat of an occupying
item of the seat, a capacitive sensor, a seat belt buckle sensor, a
seatbelt payout sensor, an infrared sensors, an inductive sensor, a
motion sensor and a radar sensor. The same type of sensor could
also be used, preferably situated in a different location, but
possibly in the same location for redundancy purposes. For example,
the system may include a plurality of weight sensors, each
measuring the weight applied onto the seat at a different location.
Such weight sensors may include a weight sensor, such as a strain
gage, arranged to measure displacement of a surface of a seat
portion of the seat and/or a strain, force or pressure gage
arranged to measure displacement of the entire seat. In the latter
case, the seat includes a support structure for supporting the seat
above a floor of a passenger compartment of the vehicle whereby the
strain gage can be attached to the support structure.
[0068] In some embodiments, the transducers include a plurality of
electromagnetic wave sensors capable of receiving waves at least
from a space above the seat, each electromagnetic wave sensor being
arranged at a different location. Other wave sensors, such as
capacitive sensors, can also be used.
[0069] In other embodiments, the transducers include at least two
ultrasonic sensors capable of receiving waves at least from a space
above the seat, each ultrasonic sensor being arranged at a
different location. For example, one sensor is arranged on a
ceiling of the vehicle and the other is arranged at a different
location in the vehicle, preferably so that an axis connecting the
sensors is substantially parallel to a second axis traversing a
volume in the vehicle above the seat. The second sensor may be
arranged on a dashboard or instrument panel of the vehicle. A third
ultrasonic sensor can be arranged on an interior side surface of
the passenger compartment while a fourth can be arranged on or
adjacent an interior side surface of the passenger compartment. The
ultrasonic sensors are capable of transmitting waves at least into
the space above the seat. Further, the ultrasonic sensors are
preferably aimed such that the ultrasonic fields generated thereby
cover a substantial portion of the volume surrounding the seat.
Horns or grills may be provided for adjusting the transducer field
angles of the ultrasonic sensors to reduce reflections off of fixed
surfaces within the vehicle or otherwise control the shape of the
ultrasonic field.
[0070] The actual location of the ultrasonic sensors can be
determined by placing a significant number of ultrasonic sensors in
the vehicle and removing those sensors which prove analytically to
be redundant. The ultrasonic sensors can have different
transmitting and receiving frequencies and be arranged in the
vehicle such that sensors having adjacent transmitting and
receiving frequencies are not within a direct ultrasonic field of
each other.
[0071] Once the occupancy state of the seat (or seats) in the
vehicle is known, this information can be used to control or affect
the operation of a significant number of vehicular systems,
components and devices. That is, the systems, components and
devices in the vehicle would be controlled in consideration of the
occupancy of the seat(s) in the vehicle, possibly to optimize
operation of the same. Thus, the vehicle includes control means
coupled to the processor means for controlling a component or
device in the vehicle in consideration of the output indicative of
the current occupancy state of the seat obtained from the processor
means. The component or device can be an airbag system including at
least one deployable airbag whereby the deployment of the airbag is
suppressed, e.g., if the seat is occupied by a rear-facing child
seat, or otherwise the parameters of the deployment are controlled.
In another embodiment of the invention, the system for determining
the occupancy state of a seat in a vehicle includes a plurality of
transducers arranged in the vehicle, each providing data relating
to the occupancy state of the seat, and processor means coupled to
the transducers for receiving only a separate stream of data from
each transducer (such that the stream of data from each transducer
is passed to the processor means without combining with another
stream of data) and processing the streams of data to obtain an
output indicative of the current occupancy state of the seat. The
processor means comprise an algorithm created from a plurality of
data sets, each representing a different occupancy state of the
seat and being formed from separate streams of data, each only from
one transducers, while the seat is in that occupancy state. The
algorithm produces the output indicative of the current occupancy
state of the seat upon inputting a data set representing the
current occupancy state of the seat and being formed from separate
streams of data, each only from one transducer. The processor means
preferably process each separate stream of data independent of the
processing of the other streams of data.
[0072] In yet another embodiment of the invention, the system for
determining the occupancy state of a seat in a vehicle includes a
plurality of transducers including at least two wave-receiving
transducers arranged in the vehicle, each providing data relating
to the occupancy state of the seat. One wave-receiving transducer
is arranged on or adjacent to a ceiling of the vehicle and a second
wave-receiving transducer is arranged at a different location in
the vehicle such that an axis connecting these wave-receiving
transducers is substantially parallel to a longitudinal axis of the
vehicle, substantially parallel to a transverse axis of the vehicle
or passes through a volume above the seat. A processor is coupled
to the transducers for receiving data from the transducers and
processing the data to obtain an output indicative of the current
occupancy state of the seat. The processor comprises an algorithm
which produces the output indicative of the current occupancy state
of the seat upon inputting a data set representing the current
occupancy state of the seat and being formed from data from the
transducers.
[0073] In still another embodiment of the invention, the system
includes a plurality of transducers arranged in the vehicle, each
providing data relating to the occupancy state of the seat, and
which include a wave-receiving transducer and a non-wave-receiving
transducer. The system also includes processor means coupled to the
transducers for receiving the data from the transducers and
processing the data to obtain an output indicative of the current
occupancy state of the seat. The processor means comprising an
algorithm created from a plurality of data sets, each representing
a different occupancy state of the seat and being formed from data
from the transducers while the seat is in that occupancy state. The
algorithm produces the output indicative of the current occupancy
state of the seat upon inputting a data set representing the
current occupancy state of the seat and being formed from data from
the transducers. In all of the implementations of the invention
described above, a combination or combinational neural network is
used. The particular combination neural network is determined by a
process in which a number of neural network modules are combined in
a parallel and a serial manner and an optimization program can be
utilized to determine the best combination of such neural networks
to achieve the highest accuracy. Alternately, the optimization
process can be undertaken manually in a trial and error manner. In
this manner, the optimum combination of neural networks is selected
to solve the particular pattern recognition and categorization
objective desired.
[0074] These and other objects and advantages will become apparent
from the following description of the preferred embodiments of the
vehicle identification and monitoring system of this invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0075] The following drawings are illustrative of embodiments of
the system developed or adapted using the teachings of this
invention and are not meant to limit the scope of the invention as
encompassed by the claims. In particular, the illustrations below
are limited to the monitoring of the front passenger seat for the
purpose of describing the system. Naturally, the invention applies
as well to adapting the system to the other seating positions in
the vehicle and particularly to the driver position.
[0076] FIG. 1 shows a seated-state detecting unit developed in
accordance with the present invention and the connections between
ultrasonic or electromagnetic sensors, a weight sensor, a reclining
angle detecting sensor, a seat track position detecting sensor, a
heartbeat sensor, a motion sensor, a neural network circuit, and an
airbag system installed within a vehicle compartment;
[0077] FIG. 2 is a perspective view of a vehicle containing two
adult occupants on the front seat with the vehicle shown in phantom
illustrating one preferred location of the ultrasonic transducers
placed according to the methods taught in this invention.
[0078] FIG. 3 is a view as in FIG. 2 with the passenger occupant
replaced by a child in a forward facing child seat.
[0079] FIG. 4 is a view as in FIG. 2 with the passenger occupant
replaced by a child in a rearward facing child seat.
[0080] FIG. 5 is a view as in FIG. 2 with the passenger occupant
replaced by an infant in an infant seat.
[0081] FIG. 6 is a diagram illustrating the interaction of two
ultrasonic sensors and how this interaction is used to locate a
circle is space.
[0082] FIG. 7 is a view as in FIG. 2 with the occupants removed
illustrating the location of two circles in space and how they
intersect the volumes characteristic of a rear facing child seat
and a larger occupant.
[0083] FIG. 8 illustrates a preferred mounting location of a
three-transducer system.
[0084] FIG. 9 illustrates a preferred mounting location of a
four-transducer system.
[0085] FIG. 10 is a plot showing the target volume discrimination
for two transducers.
[0086] FIG. 11 illustrates a preferred mounting location of a
eight-transducer system.
[0087] FIG. 12 is a schematic illustrating a combination neural
network system.
[0088] FIG. 13 is a schematic illustration of a method in which the
occupancy state of a seat of a vehicle is determined using a
combination neural network in accordance with the invention.
[0089] FIG. 14 is a schematic illustration of a method in which the
identification and position of the occupant is determined using a
combination neural network in accordance with the invention.
[0090] FIG. 15 is a schematic illustration of a method in which the
occupancy state of a seat of a vehicle is determined using a
combination neural network in accordance with the invention in
which bad data is prevented from being used to determine the
occupancy state of the vehicle.
[0091] FIG. 16 is a schematic illustration of another method in
which the occupancy state of a seat of a vehicle is determined, in
particular, for the case when a child seat is present, using a
combination neural network in accordance with the invention.
[0092] FIG. 17 is a schematic illustration of a method in which the
occupancy state of a seat of a vehicle is determined using a
combination neural network in accordance with the invention, in
particular, an ensemble arrangement of neural networks.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0093] System Adaptation involves the process by which the hardware
configuration and the software algorithms are determined for a
particular vehicle. Each vehicle model or platform will most likely
have a different hardware configuration and different algorithms.
Some of the various aspects that make up this process are as
follows:
[0094] The determination of the mounting location and aiming of the
transducers.
[0095] The determination of the transducer field angles
[0096] The use of a combination neural network algorithm generating
program such as available from Consultants International Ltd., LLC
to help generate the algorithms.
[0097] The process of the collection of data in the vehicle for
neural network training purposes.
[0098] The method of automatic movement of the vehicle seats etc.
while data is collected
[0099] The determination of the quantity of data to acquire and the
setups needed to achieve a high system accuracy, typically several
hundred thousand vectors.
[0100] The collection of data in the presence of varying
environmental conditions such as with thermal gradients.
[0101] The photographing of each data setup.
[0102] The makeup of the different databases and the use of three
different databases.
[0103] The method by which the data is biased to give higher
probabilities for forward facing humans.
[0104] The automatic recording of the vehicle setup including seat,
seat back, headrest, window, visor, armrest positions to help
insure data integrity.
[0105] The use of a daily setup to validate that the transducer
configuration has not changed.
[0106] The method by which bad data is culled from the
database.
[0107] The inclusion of the Fourier transforms and other
pre-processors of the data in the training process.
[0108] The use of multiple network levels, for example, for
categorization and position.
[0109] The use of multiple networks in parallel.
[0110] The use of post processing filters and the particularities
of these filters.
[0111] The addition of fuzzy logic or other human intelligence
based rules.
[0112] The method by which vector errors are corrected using, for
example, a neural network.
[0113] The use of neural works as the pattern recognition algorithm
generating system.
[0114] The use of back propagation neural networks from
training.
[0115] The use of vector normalization.
[0116] The use of feature extraction techniques including:
[0117] The number of data points prior to a peak.
[0118] The normalization factor.
[0119] The total number of peaks.
[0120] The vector mean or variance.
[0121] The use of other computational intelligence systems such as
the genetic algorithms
[0122] The use the data screening techniques.
[0123] The techniques used to develop a stable networks including
the concepts of old and new networks.
[0124] The time spent or the number of iterations spent in, and
method of, arriving at stable networks.
[0125] The technique where a small amount of data is collected
first such as 16 sheets followed by a complete data collection
sequence.
[0126] The process of adapting the system to the vehicle begins
with a survey of the vehicle model. Any existing sensors, such as
seat position sensors, seat back sensors, etc., are immediate
candidates for inclusion into the system. Input from the customer
will determine what types of sensors would be acceptable for the
final system. These sensors can include: seat structure mounted
weight sensors, pad type weight sensors, pressure type weight
sensors, seat fore and aft position sensors, seat vertical position
sensors, seat angular position sensors, seat back position sensors,
headrest position sensors, ultrasonic occupant sensors, optical
occupant sensors, capacitive sensors, inductive sensors, radar
sensors, vehicle velocity and acceleration sensors, brake pressure,
seatbelt force, payout and buckle sensors. etc. A candidate array
of sensors is then chosen and mounted onto the vehicle.
[0127] The vehicle is also instrumented so that data input by
humans is minimized. Thus, the positions of the various components
in the vehicle such as the seats, windows, sun visor, armrest, etc.
are automatically recorded. Also, the position of the occupant
while data is being taken is also recorded through a variety of
techniques such as direct ultrasonic ranging sensors, optical
ranging sensors, radar ranging sensors, optical tracking sensors
etc. Cameras are also installed to take a picture of the setup to
correspond to each vector of data collected or at some other
appropriate frequency.
[0128] A standard set of vehicle setups is chosen for initial trial
data collection purposes. Typically, the initial trial will consist
of between 20,000 and 100,000 setups, although this range is not
intended to limit the invention.
[0129] Initial digital data collection now proceeds for the trial
setup matrix. The data is collected from the transducers, digitized
and combined to form to a vector of input data for analysis by a
neural network program or combination neural network program. This
analysis should yield a training accuracy of nearly 100%. If this
is not achieved, then additional sensors are added to the system or
the configuration changed and the data collection and analysis
repeated.
[0130] In addition to a variety of seating states for objects in
the passenger compartment, the trial database will also include
environmental effects such as thermal gradients caused by heat
lamps and the operation of the air conditioner and heater. A sample
of such a matrix is presented in Appendix 1. After the neural
network has been trained on the trial database, the trial database
will be scanned for vectors that yield erroneous results (which
would likely be considered bad data). A study of those vectors
along with vectors from associated in time cases are compared with
the photographs to determine whether there is erroneous data
present. If so, an attempt is made to determine the cause of the
erroneous data. If the cause can be found, for example if a voltage
spike on the power line corrupted the data, then the vector will be
removed from the database and an attempt is made to correct the
data collection process so as to remove such disturbances.
[0131] At this time, some of the sensors may be eliminated from the
sensor matrix. This can be determined during the neural network
analysis by selectively eliminating sensor data from the analysis
to see what the effect if any results. Caution should be exercised
here, however, since once the sensors have been initially installed
in the vehicle, it requires little additional expense to use all of
the installed sensors in future data collection and analysis.
[0132] The neural network that has been developed in this first
phase is used during the data collection in the next phases as an
instantaneous check on the integrity of the new vectors being
collected. Occasionally, a voltage spike or other environmental
disturbance will momentarily effect the data from some transducers.
It is important to capture this event to first eliminate that data
from the database and second to isolate the cause of the erroneous
data.
[0133] The next set of data to be collected is the training
database. This will be the largest database initially collected and
will cover such setups as listed, for example, in Appendix 1. The
training database, which may contain 500,000 or more vectors, will
be used to begin training of the neural network. While this is
taking place additional data will be collected according to
Appendix 1 of the independent and validation databases. The
training database has been selected so that it uniformly covers all
seated states that are known to be likely to occur in the vehicle.
The independent database may be similar in makeup to the training
database or it may evolve to more closely conform to the occupancy
state distribution of the validation database. During the neural
network training, the independent database is used to check the
accuracy of the neural network and to reject a candidate neural
network design if its accuracy, measured against the independent
database, is less than that of a previous network architecture.
[0134] Although the independent database is not actually used in
the training of the neural network, nevertheless, it has been found
that it significantly influences the network structure or
architecture. Therefore, a third database, the validation or real
world database, is used as a final accuracy check of the chosen
system. It is the accuracy against this validation database that is
considered to be the system accuracy. The validation database is
composed of vectors taken from setups which closely correlate with
vehicle occupancy in real cars on the roadway. Initially the
training database is the largest of the three databases. As time
and resources permit the independent database, which perhaps starts
out with 100,000 vectors, will continue to grow until it becomes
approximately the same size as the training database. The
validation database, on the other hand, will typically start out
with as few as 50,000 vectors. However, as the hardware
configuration is frozen, the validation database will continuously
grow until, in some cases, it actually becomes larger than the
training database. This is because near the end of the program,
vehicles will be operating on highways and data will be collected
in real world situations. If in the real world tests, system
failures are discovered this can lead to additional data being
taken for both the training and independent databases as well as
the validation database.
[0135] Once a neural network has been trained using all of the
available data from all of the transducers, it is expected that the
accuracy of the network will be very close to 100%. It is usually
not practical to use all of the transducers that have been used in
the training of the system for final installation in real
production vehicle models. This is primarily due to cost and
complexity considerations. Usually the automobile manufacturer will
have an idea of how many sensors would be acceptable for
installation in a production vehicle. For example, the data may
have been collected using 20 different transducers but the
automobile manufacturer may restrict the final selection to 6
transducers. The next process, therefore, is to gradually eliminate
sensors to determine what is the best combination of six sensors,
for example, to achieve the highest system accuracy. Ideally, a
series of neural networks would be trained using all combinations
of six sensors from the available. The activity would require a
prohibitively long time. Certain constraints can be factored into
the system from the beginning to start the pruning process. For
example, it would probably not make sense to have both optical and
ultrasonic sensors present in the same system since it would
complicate the electronics. In fact, the automobile manufacturer
may have decided initially that an optical system would be too
expensive and therefore would not be considered. The inclusion of
optical sensors, therefore, serves as a way of determining the loss
in accuracy as a function of cost. Various constraints, therefore,
usually allow the immediate elimination of a significant number of
the initial group of sensors. This elimination and the training on
the remaining sensors provides the resulting accuracy loss that
results.
[0136] The next step is to remove each of the sensors one at a time
and determine which sensor has the least effect on the system
accuracy. This process is then repeated until the total number of
sensors has been pruned down to the number desired by the customer.
At this point, the process is reversed to add in one at a time
those sensors that were removed at previous stages. It has been
found, for example, that a sensor that appears to be unimportant
during the early pruning process can become very important later
on. Such a sensor may add a small amount of information due to the
presence of various other sensors. Whereas the various other
sensors, however, may yield less information than still other
sensors and, therefore may have been removed during the pruning
process. Reintroducing the sensor that was eliminated early in the
cycle therefore can have a significant effect and can change the
final choice of sensors to make up the system.
[0137] The above method of reducing the number of sensors that make
up the system is but one of a variety approaches which have
applicability in different situations. In some cases, a Monte Carlo
or other statistical approach is warranted, whereas in other cases
a design of experiments approach has proven to be the most
successful. In many cases, an operator conducting this activity
becomes skilled and after a while knows intuitively what set of
sensors is most likely to yield the best results. During the
process it is not uncommon to run multiple cases on different
computers simultaneously. Also, during this process, a database of
the cost of accuracy is generated. The automobile manufacturer, for
example, may desire to have the total of 6 transducers in the final
system, however, when shown the fact that the addition of one or
two additional sensors substantially increases the accuracy of the
system, the manufacturer may change his mind. Similarly, the
initial number of sensors selected may be 6 but the analysis could
show that 4 sensors give substantially the same accuracy as 6 and
therefore the other 2 can be eliminated at a cost saving.
[0138] While the pruning process is occurring, the vehicle is
subjected to a variety of road tests and would be subjected to
presentations to the customer. The road tests are tests that are
run at different locations than where the fundamental training took
place. It has been found that unexpected environmental factors can
influence the performance of the system and therefore these tests
can provide critical information. The system, therefore, which is
installed in the test vehicle should have the capability of
recording system failures. This recording includes the output of
all of the sensors on the vehicle as well as a photograph of the
vehicle setup that caused the error. This data is later analyzed to
determine whether the training, independent or validation setups
need to be modified and/or whether the sensors or positions of the
sensors require modification.
[0139] Once the final set of sensors has been chosen, the vehicle
is again subjected to real world testing on highways and at
customer demonstrations. Once again, any failures are recorded. In
this case, however, since the total number of sensors in the system
is probably substantially less than the initial set of sensors,
certain failures are to be expected. All such failures, if
expected, are reviewed carefully with the customer to be sure that
the customer recognizes the system failure modes and is prepared to
accept the system with those failure modes.
[0140] The system described so far has been based on the use of a
single neural network. It is frequently necessary and desirable to
use combination neural networks, multiple neural networks or other
pattern recognition systems. For example, for determining the
occupancy state of a vehicle seat there may be at least two
different requirements. The first requirement is to establish what
is occupying the seat and the second requirement is to establish
where that object is located. Another requirement might be to
simply determine whether an occupying item warranting analysis by
the neural networks is present. Generally, a great deal of time,
typically many seconds, is available for determining whether a
forward facing human or an occupied or unoccupied rear facing child
seat, for example, occupies the vehicle seat. On the other hand, if
the driver of the car is trying to avoid an accident and is engaged
in panic braking, the position of an unbelted occupant can be
changing rapidly as he or she is moving toward the airbag. Thus,
the problem of determining the location of an occupant is time
critical. Typically, the position of the occupant in such
situations must be determined in less than 20 milliseconds. There
is no reason for the system to have to determine that a forward
facing human being is in the seat while simultaneously determining
where that forward facing human being is. The system already knows
that the forward facing human being is present and therefore all of
the resources can be used to determine the occupant's position.
Thus, in this situation a dual level or modular neural network can
be advantageously used. The first level determines the occupancy of
the vehicle seat and the second level determines the position of
that occupant. In some situations, it has been demonstrated that
multiple neural networks used in parallel can provide some benefit.
This will be discussed in more detail below. Both modular and
multiple parallel neural networks are examples of combination
neural networks.
[0141] The data that is fed to the pattern recognition system
typically will usually not be the raw vectors of data as captured
and digitized from the various transducers. Typically, a
substantial amount of preprocessing of the data is undertaken to
extract the important information from the data that is fed to the
neural network. This is especially true in optical systems and
where the quantity of data obtained, if all were used by the neural
network, would require very expensive processors. The techniques of
preprocessing data will not be described in detail here. However,
the preprocessing techniques influence the neural network structure
in many ways. For example, the preprocessing used to determine what
is occupying a vehicle seat is typically quite different from the
preprocessing used to determine the location of that occupant. Some
particular preprocessing concepts will be discussed in more detail
below.
[0142] Once the pattern recognition system has been applied to the
preprocessed data, one or more decisions are available as output.
The output from the pattern recognition system is usually based on
a snapshot of the output of the various transducers. Thus, it
represents one epoch or time period. The accuracy of such a
decision can usually be substantially improved if previous
decisions from the pattern recognition system are also considered.
In the simplest form, which is typically used for the occupancy
identification stage, the results of many decisions are averaged
together and the resulting averaged decision is chosen as the
correct decision. Once again, however, the situation is quite
different for dynamic out-of-position. The position of the occupant
must be known at that particular epoch and cannot be averaged with
his previous position. On the other hand, there is information in
the previous positions that can be used to improve the accuracy of
the current decision. For example, if the new decision says that
the occupant has moved six inches since the previous decision, and,
from physics, it is known that this could not possibly take place,
than a better estimate of the current occupant position can be made
by extrapolating from earlier positions. Alternately, an occupancy
position versus time curve can be fitted using a variety of
techniques such as the least squares regression method, to the data
from previous 10 epochs, for example. This same type of analysis
could also be applied to the vector itself rather than to the final
decision thereby correcting the data prior to its being entered
into the pattern recognition system. An alternate method is to
train a module of a modular neural network to predict the position
of the occupant based on feedback from previous results of the
module.
[0143] A pattern recognition system, such as a neural network, can
sometimes make totally irrational decisions. This typically happens
when the pattern recognition system is presented with a data set or
vector that is unlike any vector that has been in its training set.
The variety of seating states of a vehicle is unlimited. Every
attempt is made to select from that unlimited universe a set of
representative cases. Nevertheless, there will always be cases that
are significantly different from any that have been previously
presented to the neural network. The final step, therefore, to
adapting a system to a vehicle, is to add a measure of human
intelligence. Sometimes this goes under the heading of fuzzy logic
and the resulting system has been termed in some cases a neural
fuzzy system. In some cases, this takes the form of an observer
studying failures of the system and coming up with rules and that
say, for example, that if sensor A perhaps in combination with
another sensor produces values in this range than the system should
be programmed to override the pattern recognition decision and
substitute therefor a human decision.
[0144] An example of this appears in R. Scorcioni, K. Ng, M. M.
Trivedi, N. Lassiter; "MoNiF: A Modular Neuro-Fuzzy Controller for
Race Car Navigation"; In Proceedings of the 1997 IEEE Symposium on
Computational Intelligence and Robotics Applications, Monterey,
Calif., USA July 1997 and describes the case of where an automobile
was designed for autonomous operation and trained with a neural
network, in one case, and a neural fuzzy system in another case. As
long as both vehicles operated on familiar roads both vehicles
performed satisfactorily. However, when placed on an unfamiliar
road, the neural network vehicle failed while the neural fuzzy
vehicle continue to operate successfully. Naturally, if the neural
network vehicle had been trained on the unfamiliar road, it might
very well have operated successful. Nevertheless, the critical
failure mode of neural networks that most concerns people is this
uncertainty as to what a neural network will do when confronted
with an unknown state.
[0145] One aspect, therefore, of adding human intelligence to the
system, is to ferret out those situations where the system is
likely to fail. Unfortunately, in the current state-of-the-art,
this is largely a trial and error activity. One example is that if
the range of certain parts of vector falls outside of the range
experienced during training, the system defaults to a particular
state. In the case of suppressing deployment of one or more
airbags, or other occupant protection apparatus, this case would be
to enable airbag deployment even if the pattern recognition system
calls for its being disabled. An alternate method is to train a
particular module of a modular neural network to recognize good
from bad data and reject the bad data before it is fed to the main
neural networks.
[0146] The foregoing description is applicable to the systems
described in the following drawings and the connection between the
foregoing description and the systems described below will be
explained below. However, it should be appreciated that the systems
shown in the drawings do not limit the applicability of the methods
or apparatus described above. Referring to the accompanying
drawings wherein like reference numbers designate the same or
similar elements, FIG. 1 shows a passenger seat 1 to which an
adjustment apparatus including a seated-state detecting system
developed according to the present invention may be applied. The
seat 1 includes a horizontally situated bottom seat portion 2 and a
vertically oriented back portion 3. The seat portion 2 is provided
with weight measuring means, such as one or more weight sensors 6
and 7, that determine the weight of the object occupying the seat,
if any. The coupled portion between the seated portion 2 and the
back portion 3 (also referred to as the seatback) is provided with
a reclining angle detecting sensor 9, which detects the tilted
angle of the back portion 3 relative to the seat portion 2. The
seat portion 2 is provided with a seat track position-detecting
sensor 10. The seat track position detecting sensor 10 fulfills a
role of detecting the quantity of movement of the seat 1 which is
moved from a back reference position, indicated by the dotted chain
line. Embedded within the seatback 3 is a heartbeat sensor 31 and a
motion sensor 33. Attached to the headliner of the vehicle is a
capacitance sensor 32. The seat 1 may be the driver seat, the front
passenger seat or any other seat in a motor vehicle as well as
other seats in transportation vehicles or seats in
non-transportation applications.
[0147] Motion sensor 33 can be a discrete sensor that detects
relative motion in the passenger compartment of the vehicle. Such
sensors are frequently based on ultrasonics and can measure a
change in the ultrasonic pattern that occurs over a short time
period. Alternately, the subtracting of one position vector from a
previous position vector to achieve a differential position vector
can detect motion. For the purposes herein, a motion sensor will be
used to mean either a particular device that is designed to detect
motion for the creation of a special vector based on vector
differences.
[0148] The weight measuring means, such as the sensors 6 and 7, are
associated with the seat, and can be mounted into or below the seat
portion 2 or on the seat structure, for example, for measuring the
weight applied onto the seat. The weight may be zero if no
occupying item is present. Sensors 6 and 7 may represent a
plurality of different sensors which measure the weight applied
onto the seat at different portions thereof or for redundancy
purposes, for example, such as by means of an airbag or bladder 5
in the seat portion 2. The bladder 5 may have one or more
compartments. Such sensors may be in the form of strain, force or
pressure sensors which measure the force or pressure on the seat
portion 2 or seat back 3, displacement measuring sensors which
measure the displacement of the seat surface or the entire seat 1
such as through the use of strain gages mounted on the seat
structural members, such as 7, or other appropriate locations, or
systems which convert displacement into a pressure wherein a
pressure sensor can be used as a measure of weight. Sensors 6 and 7
may be of the types disclosed in U.S. patent application Ser. No.
09/193,209, incorporated by reference herein.
[0149] An ultrasonic or optical sensor system 12 is mounted on the
upper portion of the front pillar, i.e., the A-Pillar, of the
vehicle and a similar sensor system 11 is mounted on the upper
portion of the intermediate pillar, i.e., the B-Pillar. Each sensor
system 11,12 may comprise a transducer. The outputs of the sensor
systems 11 and 12 are input to a band pass filter 20 through a
multiplex circuit 19 which is switched in synchronization with a
timing signal from the ultrasonic sensor drive circuit 18, and then
is amplified by an amplifier 21. The band pass filter 20 removes a
low frequency wave component from the output signal and also
removes some of the noise. The envelope wave signal is input to an
analog/digital converter (ADC) 22 and digitized as measured data.
The measured data is input to a processing circuit 23, which is
controlled by the timing signal which is in turn output from the
sensor drive circuit 18.
[0150] Each of the measured data is input to a normalization
circuit 24 and normalized. The normalized measured data is input to
the combination neural network (circuit) 25 as wave data.
[0151] The output of the weight sensor(s) 6 and 7 is amplified by
an amplifier 26 coupled to the weight sensor(s) 6 and 7 and the
amplified output is input to an analog/digital converter and then
directed to the combination neural network 25 of the processor
means. Amplifier 26 is useful in some embodiments but it may be
dispensed with by constructing the sensors 6 and 7 to provide a
sufficient strong output signal, and even possibly a digital
signal.
[0152] One manner to do this would be to construct the sensor
systems with appropriate electronics.
[0153] The reclining angle detecting sensor 9 and the seat track
position-detecting sensor 10 are connected to appropriate
electronic circuits. For example, a constant-current can be
supplied from a constant-current circuit to the reclining angle
detecting sensor 9, and the reclining angle detecting sensor 9
converts a change in the resistance value on the tilt of the back
portion 3 to a specific voltage. This output voltage is input to an
analog/digital converter 28 as angle data, i.e., representative of
the angle between the back portion 3 and the seat portion 2.
Similarly, a constant current can be supplied from a
constant-current circuit to the seat track position detecting
sensor 10 and the seat track position detecting sensor 10 converts
a change in the resistance value based on the track position of the
seat portion 2 to a specific voltage. This output voltage is input
to an analog/digital converter 29 as seat track data. Thus, the
outputs of the reclining angle-detecting sensor 9 and the seat
track position-detecting sensor 10 are input to the analog/digital
converters (ADC) 28 and 29, respectively. Each digital data value
from the ADCs 28,29 is input to the combination neural network 25.
A more detailed description of this and similar systems can be
found in the above-referenced patents and patent applications
assigned to the current assignee, all of which are included herein
by reference. The system described above is one example of many
systems that can be designed using the teachings of this invention
for detecting the occupancy state of the seat of a vehicle.
[0154] The combination neural network 25 is directly connected to
the ADCs 28 and 29, the ADC associated with amplifier 26 and the
normalization circuit 24. As such, information from each of the
sensors in the system (a stream of data) is passed directly to the
combination neural network 25 for processing thereby. The streams
of data from the sensors are not combined prior to the combination
neural network 25 and the combination neural network is designed to
accept the separate streams of data (e.g., at least a part of the
data at each input node) and process them to provide an output
indicative of the current occupancy state of the seat. The
combination neural network 25 thus includes or incorporates a
plurality of algorithms derived by training in the manners
discussed above and below. Once the current occupancy state of the
seat is determined, it is possible to control vehicular components
or systems, such as the airbag system, in consideration of the
current occupancy state of the seat.
[0155] A section of the passenger compartment of an automobile is
shown generally as 100 in FIG. 2. A driver 101 of a vehicle sits on
a seat 102 behind a steering wheel, not shown, and an adult
passenger 103 sits on seat 104 on the passenger side. Two
transmitter and receiver assemblies 110 and 111, also referred to
herein as transducers, are positioned in the passenger compartment
100, one transducer 110 is arranged on the headliner adjacent or in
proximity to the dome light and the other transducer 111 is
arranged on the center of the top of the dashboard or instrument
panel of the vehicle. The methodology leading to the placement of
these transducers is central to the instant invention as explained
in detail below. In this situation, the system developed in
accordance with this invention will reliably detect that an
occupant is sitting on seat 104 and deployment of the airbag is
enabled in the event that the vehicle experiences a crash.
Transducers 110, 111 are placed with their separation axis parallel
to the separation axis of the head, shoulder and rear facing child
seat volumes of occupants of an automotive passenger seat and in
view of this specific positioning, are capable of distinguishing
the different configurations. In addition to the ultrasonic
transducers 110, 111, weight-measuring sensors 210, 211, 212, 214
and 215 are also present. These weight sensors may be of a variety
of technologies including, as illustrated here, strain-measuring
transducers attached to the vehicle seat support structure as
described in more detail in U.S. patent application Ser. No.
08/920,822, incorporated herein by reference. Naturally other
weight systems can be utilized including systems that measure the
deflection of, or pressure on, the seat cushion. The weight sensors
described here are meant to be illustrative of the general class of
weight sensors and not an exhaustive list of methods of measuring
occupant weight. In FIG. 3, a forward facing child seat 120
containing a child 121 replaces the adult passenger 103 as shown in
FIG. 2. In this case, it is usually required that the airbag not be
disabled in the event of an accident.
[0156] However, in the event that the same child seat is placed in
the rearward facing position as shown in FIG. 4, then the airbag is
usually required to be disabled since deployment of the airbag in a
crash can seriously injure or even kill the child. Furthermore, as
illustrated in FIG. 5, if an infant 131 in an infant carrier 130 is
positioned in the rear facing position of the passenger seat, the
airbag should be disabled for the reasons discussed above. Instead
of disabling deployment of the airbag, the deployment could be
controlled to provide protection for the child, e.g., to reduce the
force of the deployment of the airbag. It should be noted that the
disabling or enabling of the passenger airbag relative to the item
on the passenger seat may be tailored to the specific application.
For example, in some embodiments, with certain forward facing child
seats, it may in fact be desirable to disable the airbag and in
other cases to deploy a depowered airbag. The selection of when to
disable, depower or enable the airbag, as a function of the item in
the passenger seat and its location, is made during the programming
or training stage of the sensor system and, in most cases, the
criteria set forth above will be applicable, i.e., enabling airbag
deployment for a forward facing child seat and an adult in a proper
seating position and disabling airbag deployment for a rearward
facing child seat and infant and for any occupant who is
out-of-position and in close proximity to the airbag module. The
sensor system developed in accordance with the invention may
however be programmed according to other criteria.
[0157] Several systems using other technologies have been devised
to discriminate between the four cases illustrated above but none
have shown a satisfactory accuracy or reliability of
discrimination. Some of these systems appear to work as long as the
child seat is properly placed on the seat and belted in. So called
"tag systems", for example, whereby a device is placed on the child
seat which is electromagnetically sensed by sensors placed within
the seat have not proven reliable by themselves but can add
information to the overall system. When used alone, they function
well as long as the child seat is restrained by a seatbelt, but
when this is not the case they have a high failure rate. Since the
seatbelt usage of the population of the United States is only about
60% at the present time, it is quite likely that a significant
percentage of child seats will not be properly belted onto the seat
and thus children will be subjected to injury and death in the
event of an accident.
[0158] The methodology of this invention was devised to solve this
problem. To understand this methodology, consider two ultrasonic
transmitters and receivers 110 and 111 (transducers) which are
connected by an axis AB in FIG. 6. Each transmitter radiates a
signal which is primarily confined to a cone angle, called the
field angle, with its origin at the transmitter. For simplicity,
assume that the transmitter and receiver are embodied in the same
device although in some cases a separate device will be used for
each function. When a transducer sends out a burst of waves, to
thereby irradiate the passenger compartment with ultrasonic
radiation, and then receives a reflection or modified radiation
from some object in the passenger compartment, the distance of the
object from the transducer can be determined by the time delay
between the transmission of the waves and the reception of the
reflected or modified waves.
[0159] When looking at a single transducer, it is not possible to
determine the direction to the object which is reflecting or
modifying the signal but it is possible to know only how far that
object is from the transducer, that is a single transducer enables
a distance measurement but not a directional measurement. In other
words, the object may be at a point on the surface of a
three-dimensional spherical segment having its origin at the
transducer and a radius equal to the distance. Consider two
transducers, such as 110 and 111 in FIG. 6, and both transducers
receive a reflection from the same object, which is facilitated by
proper placement of the transducers, the timing of the reflections
depends on the distance from the object to each respective
transducer. If it is assumed for the purposes of this analysis that
the two transducers act independently, that is, they only listen to
the reflections of waves which they themselves transmitted (which
may be achieved by transmitting ultrasonic waves at different
frequencies), then each transducer enables the determination of the
distance to the reflecting object but not its direction. If we
assume that the transducer radiates ultrasound in all directions
within the field cone angle, each transducer enables the
determination that the object is located on a spherical surface A',
B' a respective known distance from the transducer, that is, each
transducer enables the determination that the object is a specific
distance from that transducer which may or may not be the same
distance between the other transducer and the same object. Since
now there are two transducers, and the distance of the reflecting
object has been determined relative to each of the transducers, the
actual location of the object resides on a circle which is the
intersection of the two spherical surfaces A', and B'. This circle
is labeled C in FIG. 6. At each point along circle C, the distance
to the transducer 110 is the same and the distance to the
transducer 111 is the same. This, of course, is strictly true only
for ideal one-dimensional objects.
[0160] For many cases, the mere knowledge that the object lies on a
particular circle is sufficient since it is possible to locate the
circle such that the only time that an object lies on a particular
circle that its location is known. That is, the circle which passes
through the area of interest otherwise passes through a volume
where no objects can occur. Thus, the mere calculation of the
circle in this specific location, which indicates the presence of
the object along that circle, provides valuable information
concerning the object in the passenger compartment which may be
used to control or affect another system in the vehicle such as the
airbag system. This of course is based on the assumption that the
reflections to the two transducers are in fact from the same
object. Care must be taken in locating the transducers such that
other objects do not cause reflections that could confuse the
system.
[0161] FIG. 7, for example, illustrates two circles D and E of
interest which represent the volume which is usually occupied when
the seat is occupied by a person not in a child seat, C, or by a
forward facing child seat and the volume normally occupied by a
rear facing child seat, respectively. Thus, if the circle generated
by the system, (i.e., by appropriate processor means which receives
the distance determination from each transducer and creates the
circle from the intersection of the spherical surfaces which
represent the distance from the transducers to the object) is at a
location which is only occupied by an adult passenger, the airbag
would not be disabled since its deployment in a crash is desired.
On the other hand, if a circle is at a location occupied only by a
rear facing child seat, the airbag would be disabled.
[0162] The above discussion of course is simplistic in that it is
not take into account the volume occupied by the object or the fact
the reflections from more than one object surface will be involved.
In reality, transducer B is likely to pickup the rear of the
occupant's head and transducer A, the front. This makes the
situation more difficult for an engineer looking at the data to
analyze. It has been found that pattern recognition technologies
are able to extract the information from these situations and
through a proper application of these technologies, an algorithm
can be developed, which when installed as part of the system for a
particular vehicle, the system accurately and reliably
differentiates between a forward facing and rear facing child seat,
for example, or an in-position or out-of-position forward facing
human being.
[0163] From the above discussion, a method of transducer location
is disclosed which provides unique information to differentiate
between (i) a forward facing child seat or a forward properly
positioned occupant where airbag deployment is desired and (ii) a
rearward facing child seat and an out-of-position occupant where
airbag deployment is not desired. In actuality, the algorithm used
to implement this theory does not directly calculate the surface of
spheres or the circles of interaction of spheres. Instead, a
pattern recognition system is used to differenliate
airbag-deployment desired cases from those where the airbag should
not be deployed. For the pattern recognition system to accurately
perform its function, however, the patterns presented to the system
must have the requisite information. That is, a pattern of
reflected waves from an occupying item in a passenger compartment
to various transducers must be uniquely different for cases where
airbag deployment is desired from cases where deployment is not
desired. The theory described above and in more detail below
teaches how to locate transducers within the vehicle passenger
compartment so that the patterns of reflected waves will be easily
distinguishable for cases where airbag deployment is desired from
those where deployment is not desired. In the case presented thus
far, it has been shown that in some implementations the use of only
two transducers can result in the desired pattern differentiation
when the vehicle geometry is such that two transducers can be
placed such that the circles D (airbag enabled) and E (airbag
disabled) fall outside of the transducer field cones except where
they are in the critical regions where positive identification of
the condition occurs. Thus, the aiming and field angles of the
transducers are important factors to determine in adapting a system
to a particular vehicle.
[0164] The use of only two transducers in a system is typically not
acceptable since one or both of the transducers can be rendered
inoperable by being blocked, for example, by a newspaper. Thus, it
is desirable to add a third transducer 112 as shown in FIG. 8 which
now provides a third set of spherical surfaces relative to the
third transducer. Transducer 112 is positioned on the passenger
side of the A-pillar (which is a preferred placement if the system
is designed to operate on the passenger side of the vehicle). Three
spherical surfaces now intersect in only two points and in fact,
usually at one point if the aiming angles and field angles are
properly chosen. Once again, this discussion is only strictly true
for a point object. For a real object, the reflections will come
from different surfaces of the object, which usually are at similar
distances from the object. Thus, the addition of a third transducer
substantially improves system reliability. Finally, with the
addition of a fourth transducer 113 as shown in FIG. 9, even
greater accuracy and reliability is attained. Transducer 113 is
positioned on the ceiling of the vehicle close to the passenger
side door. In FIG. 9, lines connecting the transducers C and D and
the transducers A and B are substantially parallel permitting an
accurate determination of asymmetry and thereby object rotation.
Thus, for example, if the infant seat is placed on an angle as
shown in FIG. 5, this condition can be determined and taken into
account when the decision is made to disable the deployment of the
airbag.
[0165] The discussion above has centered on locating transducers
and designing a system for determining whether the two target
volumes, that adjacent the airbag and that adjacent the upper
portion of the vehicle seat, are occupied. Other systems have been
described in the above referenced patents using a sensor mounted on
or adjacent the airbag module and a sensor mounted high in the
vehicle to monitor the space near the vehicle seat. Such systems
use the sensors as independent devices and do not use the
combination of the two sensors to determine where the object is
located. In fact, the location of such sensors is usually poorly
chosen so that it is easy to blind either or both with a newspaper,
for example. Furthermore, no system is known to have been
disclosed, except in patents and patent applications assigned to
the assignee of this invention, which uses more than two
transducers in such a manner that one or more can be blocked
without causing serious deterioration of the system. Again, the
examples here have been for the purpose of suppressing the
deployment of the airbag when it is necessary to prevent injury.
The sensor system disclosed can be used for many other purposes
such as disclosed in the above-mentioned patent applications
assigned to the same assignee as the instant invention. The ability
to use the sensors for these other applications in generally
lacking in the systems disclosed in the other referenced
patents.
[0166] Considering once again the condition of FIGS. 2-7 where two
transducers are used, a plot can be made showing the reflection
times of the objects which are located in the region of curve E and
curve F of FIG. 7. This plot is shown on FIG. 10 where the c's
represent reflections from rear facing child seats from various
tests where the seats were placed in a variety of different
positions and similarly the s's and h's represent shoulders and
heads respectively of various forward facing human occupants. In
these results from actual experiments, the effect of body thickness
is present and yet the results still show that the basic principles
of separation of key volumes are valid. Note that there is a region
of separation between corridors that house the different object
classes. It is this fact which is used in conjunction with neural
networks, as described in the above referenced patent applications,
which permit the design of a system that provides an accurate
discrimination of rear facing child seats from forward facing
humans. Heretofore before the techniques for locating the
transducers to separate these two zones were discovered, the entire
discrimination task was accomplished using neural networks. There
was significant overlap between the reflections from the various
objects and therefore separation was done based on patterns of the
reflected waves. By using the technology described herein to
carefully position and orient the transducers so as to create this
region of separation of the critical surfaces, wherein all of the
rear facing child seat data falls within a known corridor, the task
remaining for the neural networks is substantially simplified with
the result that the accuracy of identification is substantially
improved.
[0167] Three general classes of child seats exist as well as
several models which are unique. First, there is the infant only
seat as shown in FIG. 5 which is for occupants weighing up to about
20 pounds. This is designed to be only placed in the rear facing
position. The second which is illustrated in FIGS. 2 and 3 is for
children from about 20 to about 40 pounds and can be used in both
the forward and rear facing position and the third is for use only
in the forward facing position and is for children weighing over
about 40 pounds. All of these seats as well as the unique models
are used in test setups according to this invention for adapting a
system to a vehicle. For each child seat, there are several hundred
unique orientations representing virtually every possible position
of that seat within the vehicle. Tests are run, for example, with
the seat tilted 22 degrees, rotated 17 degrees, placed on the front
of the seat with the seat back fully up with the seat fully back
and with the window open as well as all variations of there
parameters. A large number of cases are also run, when practicing
the teachings of this invention, with various accessories, such as
clothing, toys, bottles, blankets etc., added to the child
seat.
[0168] Similarly, wide variations are used for the occupants
including size, clothing and activities such as reading maps or
newspapers, leaning forward to adjust the radio, for example. Also
included are cases where the occupant puts his/her feet on the
dashboard or otherwise assumes a wide variety of unusual positions.
When all of the above configurations are considered along with many
others not mentioned, the total number of configurations which are
used to train the pattern recognition system can exceed 500,000.
The goal is to include in the configuration training set
representations of all occupancy states that occur in actual use.
Since the system is highly accurate in making the correct decision
for cases which are similar to those in the training set, the total
system accuracy increases as the size of the training set increases
providing the cases are all distinct and not copies of other
cases.
[0169] In addition to all of the variations in occupancy states, it
is important to consider environmental effects during the data
collection. Thermal gradients or thermal instabilities are
particularly important for systems based on ultrasound since sound
waves can be significantly diffracted by density changes in air.
There are two aspects of the use of thermal gradients or
instability in training. First, the fact that thermal instabilities
exist and therefore data with thermal instabilities present should
be part of database. For this case, a rather small amount of data
collected with thermal instabilities would be used. A much more
important use of thermal instability comes from the fact that they
add variability to data. Thus, considerably more data is taken with
thermal instability and in fact, in some cases almost the entire
database is taken with time varying thermal gradients in order to
provide variability to the data so that the neural network does not
memorize but instead generalizes from the data. This is
accomplished by taking the data with a cold vehicle with the heater
operating and with a hot vehicle with the air conditioner
operating. Additional data is also taken with a heat lamp in a
closed vehicle to simulate a stable thermal gradient caused by sun
loading.
[0170] To collect data for 500,000 vehicle configurations is not a
formidable task. A trained technician crew can typically collect
data on in excess on 2000 configurations or vectors per hour. The
data is collected typically every 50 to 100 milliseconds. During
this time, the occupant is continuously moving, assuming a
continuously varying position and posture in the vehicle including
moving from side to side, forward and back, twisting his/her head,
reading newspapers and books, moving hands, arms, feet and legs,
until the desired number of different seated state examples are
obtained. In some cases, this process is practiced by confining the
motion of an occupant into a particular zone. In some cases, for
example, the occupant is trained to exercise these different seated
state motions while remaining in a particular zone that may be the
safe zone, the keep out zone, or an intermediate gray zone. In this
manner, data is collected representing the airbag disable,
depowered airbag enabled or full power airbag enabled states. In
other cases, the actual position of the back of the head and/or the
shoulders of the occupant are tracked using string pots, high
frequency ultrasonic transducers, or optically. In this manner, the
position of the occupant can be measured and the decision as to
whether this should be a disable or enable airbag case can be
decided later. By continuously monitoring the occupant, an added
advantage results in that the data can be collected to permit a
comparison of the occupant from one seated state to another. This
is particularly valuable in attempting to project the future
location of an occupant based on a series of past locations as
would be desirable for example to predict when an occupant would
cross into the keep out zone during a panic braking situation prior
to crash.
[0171] It is important to note that it is not necessary to train on
every vehicle produced but rather to train on each platform. A
platform is an automobile manufacturer's designation of a group of
vehicle models that are built on the same vehicle structure.
[0172] A review of the literature on neural networks yields the
conclusion that the use of such a large training set is unique in
the neural network field. The rule of neural networks is that there
must be at least three training cases for each network weight.
Thus, for example, if a neural network has 156 input nodes, 10
first hidden layer nodes, 5 second hidden layer nodes, and one
output node this results in a total of 1,622 weights. According to
conventional theory 5000 training examples should be sufficient. It
is highly unexpected, therefore, that greater accuracy would be
achieved through 100 times that many cases. It is thus not obvious
and cannot be deduced from the neural network literature that the
accuracy of the system will improve substantially as the size of
the training database increases even to tens of thousands of cases.
It is also not obvious looking at the plots of the vectors obtained
using ultrasonic transducers that increasing the number of tests or
the database size will have such a significant effect on the system
accuracy. Each of the vectors is a rather course plot with a few
significant peaks and valleys. Since the spatial resolution of the
system is typically about 3 to 4 inches, it is once again
surprising that such a large database is required to achieve
significant accuracy improvements.
[0173] An example of a combination neural network is shown
generally at 250 in FIG. 12. The process begins at 252 with the
acquisition of new data. This could be from a variety of sources
such as multiple cameras, ultrasonic sensors, capacitive sensors,
other electromagnetic field monitoring sensors, and other
electromagnetic or acoustic-based wave sensors. Additionally, the
data can come from non-wave sources such as weight or other
morphological characteristic detecting sensors, occupant-presence
detecting sensors or seat position sensors. The data is
preprocessed and fed into neural network at 254 where the type of
occupying item is determined. If the network determines that the
type of occupying item is either an empty seat or a rear facing
child seat then control is passed to box 270 via line 256 and the
decision is made to disable the airbag. It is envisioned though
that instead of disabling deployment if a rear-facing child seat is
present, a depowered deployment, a late deployment or a oriented
deployment may be made if it is determined that such would more
likely prevent injury to the child in the child seat than cause
harm.
[0174] In the event that the occupant type classification neural
network 254 has determined that the seat is occupied by something
other than a rear-facing child seat, then control is transferred to
neural network 258, occupant size classification, which has the
task of determining whether the occupant is a small, medium or
large occupant. It has been found that the accuracy of the position
determination is improved if the occupant size is first classified
and then a special occupant position neural network is used to
monitor the position of the occupant relative to airbag module.
Nevertheless, the order of applying the neural networks, e.g., the
size classification prior to the position classification, is not
critical to the practice of the invention. Once the size of the
occupant has been classified by a neural network at 258, control is
then passed to neural networks 260, 262, or 264 depending on the
output size determination from neural network 258. The chosen
network then determines the position of the occupant and that
position determination is fed to the feedback delay algorithm 266
and to the decision to disable algorithm 270. The feedback delay
266 can be a function of occupant size as well as the rate at which
data is acquired. The results of the feedback delay algorithm 266
are fed to the appropriate large, medium or small occupant position
neural networks 260, 262 or 264. It has been found that if the
previous position of the occupant is used as input to the neural
network that a more accurate estimation of the present position
results. In some cases, multiple previous position values are fed
instead of only the most recent value. This is determined for a
particular application and programmed as part as of the feedback
delay algorithm 266. After the decision to disable has been made in
algorithm 270, control is returned to algorithm 252 via line 272 to
acquire new data.
[0175] FIG. 12 is a singular example of an infinite variety
combination neural networks that can be employed. This case
combines a modular neural network structure with serial and
parallel architectures. Feedback has also been used. Other examples
include situations where imprecise data requires the input data to
be divided into subsets and fed to a series of neural networks
operating in parallel. The output of these neural networks can then
be combined in a voting or another analytical manner to determine
the final decision, e.g., whether and how to deploy the occupant
protection apparatus. In other cases, particular transducers are
associated with particular neural networks and the data combined
after initial process by those dedicated neural networks. In still
other cases, as discussed above, an initial neural network is used
to determine whether the data to be analyzed is part of the same
universe of data that has been used to train the networks.
Sometimes transducers provide erroneous data and sometimes the
wiring in the vehicle can be a source of noise that can corrupt the
data. Similarly, a neural network in sometimes used as part of the
decision to disable activity to compare results over time to again
attempt to eliminate spurious false decisions. Thus, an initial
determination as to whether the data is consistent with data on
which the neural network is trained is often an advisable step.
[0176] In each of the boxes in FIG. 12, with the exception of the
decision to disable box 270 and the feedback delay box 266, it has
been assumed that each box would be a neural network. In many
cases, a deterministic algorithm can be used, and in other cases
correlation analysis, fuzzy logic or neural fuzzy systems are
appropriate. Therefore, a combination neural network can include
non-neural network analytical tasks.
[0177] FIG. 12 illustrates the use of a combination neural network
to determine whether and how to deploy or disable an airbag. It
must be appreciated that the same architecture may be used to
determine whether and how to deploy any type of occupant protection
apparatus which is defined herein as any device, apparatus, system
or component which is actuatable or deployable or includes a
component which is actuatable or deployable for the purpose of
attempting to minimize injury to the occupant in the event of a
crash involving the vehicle. More generally, the architecture shown
in FIG. 12 may be used simply to determine the occupancy state of
the vehicle, e.g., the type, size and position of the occupant. A
determination of the occupancy state of the vehicle includes a
determination of any or all of the occupant's type, identification,
size, position, etc. The occupancy state can then be used to in the
control of any vehicular component, system or subsystem. FIG. 13
shows a more general schematic illustration of the use of a
combination neural network designated 309 in accordance with the
invention. Data is acquired at 300 and input into the occupancy
state determination unit, i.e., the combination neural network,
which provides an indication of the occupancy state of the seat.
Once the occupancy state is determined at 302, it is provided to
the component control unit 304 to effect control of the component.
A feedback delay 306 is provided to enable the determination of the
occupancy state from one instance to be used by the combination
neural network at a subsequent instance. After the component
control 304 is effected, the process begins anew by acquiring new
data via line 308.
[0178] FIG. 14 shows a schematic illustration of the use of a
combination neural network in accordance with the invention
designated 324 in which the occupancy state determination entails
an identification of the occupying item by one neural network and a
determination of the position of the occupying item by another
neural network. Data is acquired at 310 and input into the
identification neural network 312 which is trained to provide the
identification of the occupying item of the seat based on at least
some of the data, i.e., data from one or more transducers might
have been deemed of nominal relevance for the identification
determination and thus the identification neural network 312 was
not trained on such data. Once the identification of the occupying
item is determined at 312, it is provided to the position neural
network 314 which is trained to provide an indication of the
position of the occupying item, e.g., relative to the occupant
protection apparatus, based on at least some of the data. That is,
data from one or more transducers, although possibly useful for the
identification neural network 312, might have been deemed of
nominal relevance for the position neural network 314 and thus the
position neural network was not trained on such data. Once the
identification and position of the occupying item are determined,
they are provided to the component control unit 316 to effect
control of the component based on one of these determinations or
both. A feedback delay 318 is provided for the identification
neural network 312 to enable the determination of the occupying
item's identification from one instance to be used by the
identification neural network 312 at a subsequent instance. A
feedback delay 320 is provided for the position neural network 314
to enable the determination of the occupying item's position from
one instance to be used by the position neural network 314 at a
subsequent instance. After the component control 316 is effected,
the process begins anew by acquiring new data via line 322. The
identification neural network 312, the position determination
neural network 314 and feedback delays 318,320 combine to
constitute the combination neural network 324 in this embodiment
(shown in dotted lines).
[0179] The data used by the identification neural network 312 to
determine the identification of the occupying item may be different
than the data used by the position determination neural network 314
to determine the position of the occupying item. That is, data from
a different set of transducers may be applied by the identification
neural network 312 than by the position determination neural
network. Instead of a single position determination neural network
as schematically shown in FIG. 14, a plurality of position
determination neural networks may be used depending on the
identification of the occupying item. Also, a size determination
neural network may be incorporated into the combination neural
network after the identification neural network 312 and then
optionally, a plurality of the position determination neural
networks as shown in the embodiment of FIG. 12.
[0180] With respect to the feedback delays 318,320, it is possible
to use the position determination from position neural network 314
as input into the identification neural network 312.
[0181] FIG. 15 shows a schematic illustration of the use of a
combination neural network in accordance with the invention
designated 338 in which the occupancy state determination entails
an initial determination as to the quality of the data obtained by
the transducers and intended for input into a main occupancy state
determination neural network. Data from the transducers is acquired
at 326 and input into a gating neural network 328 which is trained
to allow only data which agrees with or is similar to data on which
a main neural network 330 is trained.
[0182] If the data provided by transducers has been corrupted and
thus deviates from data on which the main neural network 330 has
been trained, the gating neural network 328 will reject it and
request new data via line 342 from the transducers. Thus, gating
neural network 328 serves as a gate to prevent data which might
cause an incorrect occupancy state determination from entering as
input to the main neural network 330. If the gating neural network
328 determines that the data is reasonable, it allows the data to
pass as input to the main neural network 330 which is trained to
determine the occupancy state. Once the occupancy state is
determined, it is provided to the component control unit 332 to
effect control of the component. A feedback delay 336 is provided
for the gating neural network 328 to enable the indication of
unreasonable data from one instance to be used by the gating neural
network 328 at a subsequent instance. A feedback delay 334 is
provided for the main neural network 330 to enable the
determination of the occupancy state from one instance to be used
by the main neural network 330 at a subsequent instance. After the
component control 332 is effected, the process begins anew by
acquiring new data via line 340. The gating neural network 328, the
main neural network 330 and optional feedback delays 334,336
combine to constitute the combination neural network 338 in this
embodiment (shown in dotted lines).
[0183] Instead of a single occupancy state neural network as
schematically shown in FIG. 15, the various combinations of neural
networks disclosed herein for occupancy state determination may be
used. Similarly, the use of a gating neural network may be
incorporated into any of the combination neural networks disclosed
herein to prevent unreasonable data from entering into any of the
neural networks in any of the combination neural networks.
[0184] FIG. 16 shows a schematic illustration of the use of a
combination neural network in accordance with the invention
designated 344 with a particular emphasis on determining the
orientation and position of a child seat. Data is acquired at 346
and input into the identification neural network 348 which is
trained to provide the identification of the occupying item of the
seat based on at least some of the data. If the occupying item is
other than a child seat, the process is directed to size/position
determination neural network 350 which is trained to determine the
size and position of the occupying item and pass this determination
to the component control 352 to enable control of the component to
be effected based on the identification, size and/or position of
the occupying item. Note that the size/position determination
neural network may itself be a combination neural network.
[0185] When the occupying item is identified as a child seat, the
process passes to orientation determination neural network 354
which is trained to provide an indication of the orientation of the
child seat, i.e., whether it is rear-facing or forward-facing,
based on at least some of the data. That is, data from one or more
transducers, although possibly useful for the identification neural
network 348, might have been deemed of nominal relevance for the
orientation determination neural network 354 and thus the
orientation neural network was not trained on such data. Once the
orientation of the child seat is determined, control is then passed
to position determination neural networks 356,358 depending on the
orientation determination from neural network 354. The chosen
network then determines the position of the child seat and that
position determination is passed to component control 352 to effect
control of the component.
[0186] A feedback delay 360 is provided for the identification
neural network 348 to enable the determination of the occupying
item's identification from one instance to be used by the
identification neural network 348 at a subsequent instance. A
feedback delay 362 is provided for the orientation determination
neural network 354 to enable the determination of the child seat's
orientation from one instance to be used by the orientation
determination neural network 354 at a subsequent instance. A
feedback delay 364 is provided for the position determination
neural networks 356,358 to enable the position of the child seat
from one instance to be used by the respective position
determination neural networks 356,358 at a subsequent instance.
After the component control 352 is effected, the process begins
anew by acquiring new data via line 366. The identification neural
network 348, the position/size determination neural network 350,
the child seat orientation determination neural network 354, the
position determination neural networks 356,358 and the feedback
delays 360,362,364 combine to constitute the combination neural
network 344 in this embodiment (shown in dotted lines).
[0187] The data used by the identification neural network 348 to
determine the identification of the occupying item, the data used
by the position/size determination neural network 350 to determine
the position of the occupying item, the data used by the
orientation determination neural network 354, the data used by the
position determination neural networks 356,358 may all be different
from one another. For example, data from a different set of
transducers may be applied by the identification neural network 348
than by the position/size determination neural network 350. As
mentioned above, instead of a single position/size determination
neural network as schematically shown in FIG. 14, a plurality of
position determination neural networks may be used depending on the
identification of the occupying item.
[0188] With respect to the feedback delays 360,362,364, it is
possible to provide either upstream or downstream feedback from any
of the neural networks to any of the other neural networks.
[0189] FIG. 17 shows a schematic illustration of the use of an
ensemble type of combination neural network in accordance with the
invention designated 368. Data from the transducers is acquired at
370 and three streams of data are created. Each stream of data
contains data from a different subset of transducers. Each stream
of data is input into a respective occupancy determination neural
network 372,374,376, each of which is trained to determine the
occupancy state based on the data from the respective subset of
transducers. Once the occupancy state is determined by each neural
network 372,374,376, it is provided to a voting determination
system 378 to consider the determination of the occupancy states
from the occupancy determination neural networks 372,374,376 and
determine the most reasonable occupancy state which is passed to
the component control unit 380 to effect control of the component.
Ideally, the occupancy state determined by each neural network
372,374,376 will be the same and such would be passed to the
component control. However, in the event they differ, the voting
determination system 378 weighs the occupancy states determined by
each neural network 372,374,376 and "votes" for one. For example,
if two neural networks 372,374 provided the same occupancy state
while neural network 376 provides a different occupancy state, the
voting determination system 378 could be designed to accept the
occupancy state from the majority of neural networks, in this case,
that of neural networks 372,374. A feedback delay may be provided
for each neural network 372,374,376 as well as from the voting
determination system 378 to each neural network 372,374,376. The
voting determination system 378 may itself be a neural network.
After the component control 380 is effected, the process begins
anew by acquiring new data via line 382. Instead of the single
occupancy state neural networks 372,374,376 as schematically shown
in FIG. 17, the various combinations of neural networks disclosed
herein for occupancy state determination may be used.
[0190] Process For Training A Vehicle
[0191] The process for adapting an ultrasonic system to a vehicle
will now be described. A more detailed list of steps is provided in
Appendix 3. Although the pure ultrasonic system is described here,
a similar or analogous set of steps applies when other technologies
such as weight and optical or other electromagnetic wave systems
such as capacitance and field monitoring systems are used. This
description is thus provided to be exemplary and not limiting:
[0192] 1. Select transducer, horn and grill designs to fit the
vehicle. At this stage, usually full horns are used which are
mounted so that they project into the passenger compartment. No
attempt is made at this time to achieve an esthetic matching of the
transducers to the vehicle surfaces. An estimate of the desired
transducer fields is made at this time either from measurements in
the vehicle directly or from CAD drawings.
[0193] 2. Make polar plots of the transducer sonic fields.
Transducers and candidate horns and grills are assembled and tested
to confirm that the desired field angles have been achieved. This
frequently requires some adjustment of the transducers in the horn
and of the grill. A properly designed grill for ultrasonic systems
can perform a similar function as a lens for optical systems.
[0194] 3. Check to see that the fields cover the required volumes
of the vehicle passenger compartment and do not impinge on adjacent
flat surfaces that may cause multipath effects. Redesign horns and
grills if necessary.
[0195] 4. Install transducers into vehicle.
[0196] 5. Map transducer fields in the vehicle and check for
multipath effects and proper coverage.
[0197] 6. Adjust transducer aim and re-map fields if necessary.
[0198] 7. Install daily calibration fixture and take standard setup
data.
[0199] 8. Acquire 50,000 to 100,000 vectors
[0200] 9. Adjust vectors for volume considerations by removing some
initial data points if cross talk is present and some final points
to keep data in the desired passenger compartment volume.
[0201] 10. Normalize vectors.
[0202] 11. Run neural network algorithm generating software to
create algorithm for vehicle installation.
[0203] 12. Check the accuracy of the algorithm. If not sufficiently
accurate collect more data where necessary and retrain. If still
not sufficiently accurate, add additional transducers to cover
holes.
[0204] 13. When sufficient accuracy is attained, proceed to collect
.about.500,000 training vectors varying:
[0205] Occupancy (see Appendices 1 and 3):
[0206] Occupant size, position (zones), clothing etc
[0207] Child seat type, size, position etc.
[0208] Empty seat
[0209] Vehicle configuration:
[0210] Seat position
[0211] Window position
[0212] Visor and armrest position
[0213] Presence of other occupants in adjoining seat or rear
seat
[0214] Temperature
[0215] Temperature gradient--stable
[0216] Temperature turbulence--heater and air conditioner
[0217] Wind turbulence--High speed travel with windows open, top
down etc
[0218] 14. Collect .about.100,000 vectors of Independent data using
other combinations of the above
[0219] 15. Collect .about.50,000 vectors of "real world data" to
represent the acceptance criteria and more closely represent the
actual seated state probabilities in the real world.
[0220] 16. Train network and create an algorithm using the training
vectors and the Independent data vectors.
[0221] 17. Validate the algorithm using the real world vectors.
[0222] 18. Install algorithm into the vehicle and test.
[0223] 19. Decide on post processing methodology to remove final
holes (areas of inaccuracy) in system
[0224] 20. Implement post-processing methods into the algorithm
[0225] 21. Final test. The process up until step 13 involves the
use of transducers with full horns mounted on the surfaces of the
interior passenger compartment. At some point, the actual
transducers which are to be used in the final vehicle must be
substituted for the trial transducers. This is either done prior to
step 13 or at this step. This process involves designing transducer
holders that blend with the visual surfaces of the passenger
compartment so that they can be covered with a properly designed
grill that helps control the field and also serves to retain the
esthetic quality of the interior. This is usually a lengthy process
and involves several consultations with the customer. Usually,
therefore, the steps from 13 through 20 are repeated at this point
after the final transducer and holder design has been selected. The
initial data taken with full horns gives a measure of the best
system that can be made to operate in the vehicle. Some degradation
in performance is expected when the esthetic horns and grills are
substituted for the full horns. By conducting two complete data
collection cycles an accurate measure of this accuracy reduction
can be obtained.
[0226] 22. Up until this point, the best single neural network
algorithm has been developed. The final step is to implement the
principles of a combination neural network in order to remove some
remaining error sources such as bad data and to further improve the
accuracy of the system. It has been found that the implementation
of combination neural networks can reduce the remaining errors by
up to 50 percent. A combination neural network CAD optimization
program provided by Consultants International Ltd. can now be used
to derive the neural network architecture. Briefly, the operator
lays out a combination neural network involving many different
neural networks arranged in parallel and in series and with
appropriate feedbacks which the operator believes could be
important. The software then optimizes each neural network and also
provides an indication of the value of the network. The operator
can then selectively eliminate those networks with little or no
value and retrain the system. Through this combination of pruning,
retraining and optimizing the final candidate combination neural
network results.
[0227] 23. Ship to customers to be used in production vehicles.
[0228] 24. Collect additional real world validation data for
continuous improvement.
[0229] More detail on the operation of the transducers and control
circuitry as well as the neural network is provided in the above
referenced patents and patent applications and is included herein
as if the entire text of the same were reproduced here. One
particular example of a successful neural network for the two
transducer case had 78 input nodes, 6 hidden nodes and one output
node and for the four transducer case had 176 input nodes 20 hidden
layer nodes on hidden layer one, 7 hidden layer nodes on hidden
layer 2 and one output node. The weights of the network were
determined by supervised training using the back propagation method
as described in the referenced patent applications and in more
detail in the references cited therein. Naturally other neural
network architectures are possible including RCE, Logicon
Projection, Stochastic etc. An example of a combination neural
network system is shown in FIG. 12. Any of the network
architectures mention here can be used for any of the boxes in FIG.
12.
[0230] Finally, the system is trained and tested with situations
representative of the manufacturing and installation tolerances
that occur during the production and delivery of the vehicle as
well as usage and deterioration effects. Thus, for example, the
system is tested with the transducer mounting positions shifted by
up to one inch in any direction and rotated by up to 5 degrees,
with a simulated accumulation of dirt and other variations. This
tolerance to vehicle variation also sometimes permits the
installation of the system onto a different but similar model
vehicle with, in many cases, only minimal retraining of the
system.
[0231] The speed of sound varies with temperature, humidity, and
pressure. This can be compensated for by using the fact that the
geometry between the transducers is known and the speed of sound
can therefore be measured. Thus, on vehicle startup and as often as
desired thereafter, the speed of sound can be measured by one
transducer, such as transducer 110 in FIG. 5, sending a signal
which is directly received by another transducer.
[0232] Since the distance separating them is known, the speed of
sound can be calculated and the system automatically adjusted to
remove the variation due to the change in the speed of sound.
Therefore, the system operates with same accuracy regardless of the
temperature, humidity or atmospheric pressure. It may even be
possible to use this technique to also automatically compensate for
any effects due to wind velocity through an open window. An
additional benefit of this system is that it can be used to
determine the vehicle interior temperature for use by other control
systems within the vehicle since the variation in the velocity of
sound is a strong function of temperature and a weak function of
pressure and humidity.
[0233] The problem with the speed of sound measurement described
above is that some object in the vehicle may block the path from
one transducer to another. This of course could be checked and a
correction not be made if the signal from one transducer does not
reach the other transducer. The problem, however, is that the path
might not be completely blocked but only slightly blocked. This
would cause the ultrasonic path length to increase, which would
give a false indication of a temperature change. This can be solved
by using more than one transducer. All of the transducers can
broadcast signals to all of the other transducers. The problem
here, of course, is which transducer pair does one believe if they
all give different answers. The answer is the one that gives the
shortest distance or the greatest calculated speed of sound. By
this method, there are a total of 6 separate paths for four
ultrasonic transducers.
[0234] An alternative method of determining the temperature is to
use the transducer circuit to measure some parameter of the
transducer that changes with temperature. For example the natural
frequency of ultrasonic transducers changes in a known manner with
temperature and therefore by measuring the natural frequency of the
transducer the temperature can be determined. Since this method
does not require communication between transducers, it would also
work in situations where each transducer has a different resonant
frequency.
[0235] The process by which all of the distances are carefully
measured from each transducer to the other transducers and the
algorithm developed to determine the speed of sound, is a
significant part of the teachings of the instant invention. Prior
to this, the speed of sound calculation was based on a single
transmission from one transducer to a known second transducer. This
resulted in an inaccurate system design and degraded the accuracy
of systems in the field.
[0236] If the electronic control module that is part of the system
is located in generally the same environment as the transducers,
another method of determining the temperature is available. This
method utilizes a device and whose temperature sensitivity is known
and which is located in the same box as the electronic circuit. In
fact, in many cases, an existing component on the printed circuit
board can be monitored to give an indication of the temperature.
For example, the diodes in the log comparison circuit have
characteristics that their resistance changes in a known manner
with temperature. It can be expected that the electronic module
will generally be at a higher temperature than the surrounding
environment, however, the temperature difference is a known and
predictable amount. Thus, a reasonably good estimation of the
temperature in the passenger compartment can also be obtained in
this manner. Naturally, thermisters or other temperature
transducers can be used.
[0237] Another important feature of a system, developed in
accordance with the teachings of this invention, is the realization
that motion of the vehicle can be used in a novel manner to
substantially increase the accuracy of the system. Ultrasonic waves
reflect on most objects as light off a mirror. This is due to the
relatively long wavelength of ultrasound as compared with light. As
a result, certain reflections can overwhelm the receiver and reduce
the available information. When readings are taken while the
occupant and/or the vehicle is in motion, and these readings
averaged over several transmission/reception cycles, the motion of
the occupant and vehicle causes various surfaces to change their
angular orientation slightly but enough to change the reflective
pattern and reduce this mirror effect. The net effect is that the
average of several cycles gives a much clearer image of the
reflecting object than is obtainable from a single cycle. This then
provides a better image to the neural network and significantly
improves the identification accuracy of the system. The choice of
the number of cycles to be averaged depends on the system
requirements. For example, if dynamic out-of-position is required
then each vector must be used alone and averaging in the simple
sense cannot be used. This will be discussed more detail below.
[0238] When an occupant is sitting in the vehicle during normal
vehicle operation, the determination of the occupancy state can be
substantially improved by using successive observations over a
period of time. This can either be accomplished by averaging the
data prior to insertion into a neural network, or alternately the
decision of the neural network can be averaged. This is known as
the categorization phase of the process. During categorization the
occupancy state of the vehicle is determined. Is the vehicle
occupied by the forward facing human, an empty seat, a rear facing
child seat, or an out-of-position human? Typically many seconds of
data can be accumulated to make the categorization decision.
[0239] When a driver senses an impending crash, on the other hand,
he or she will typically slam on the brakes to try to slow vehicle
prior to impact. If an occupant is unbelted, he or she will begin
moving toward the airbag during this panic braking. For the
purposes of determining the position of the occupant, there is not
sufficient time to average data as in the case of categorization.
Nevertheless, there is information in data from previous vectors
that can be used to partially correct errors in current vectors,
which may be caused by thermal effects, for example. One method is
to determine the location of the occupant using the neural network
based on previous training. The motion of the occupant can then be
compared to a maximum likelihood position based on the position
estimate of the occupant at previous vectors. Thus, for example,
perhaps the existence of thermal gradients in the vehicle caused an
error in the current vector leading to a calculation that the
occupant has moved 12 inches since the previous vector. Since this
could be a physically impossible move during ten milliseconds, the
measured position of the occupant can be corrected based on his
previous positions and known velocity. Naturally, if an
accelerometer is present in the vehicle and if the acceleration
data is available for this calculation, a much higher accuracy
prediction can be made. Thus, there is information in the data in
previous vectors as well as in the positions of the occupant
determined from the latest data that can be used to correct
erroneous data in the current vector and, therefore, in a manner
not too dissimilar from the averaging method for categorization,
the position accuracy of the occupant can be known with higher
accuracy.
[0240] Returning to the placement of ultrasonic transducers for the
ultrasonic occupant position sensor system, as to the more novel
features of the invention for the placement of ultrasonic
transducers, this application discloses (1) the application of two
sensors to single-axis monitoring of target volumes; (2) the method
of locating two sensors spanning a target volume to sense object
positions, that is, transducers are mounted along the sensing axis
beyond the objects to be sensed; (3) the method of orientation of
the sensor axis for optimal target discrimination parallel to the
axis of separation of distinguishing target features; and (4) the
method of defining the head and shoulders and supporting surfaces
as defining humans for rear facing child seat detection and forward
facing human detection.
[0241] A similar set of observations is available for the use of
electromagnetic sensors. Such rules however must take into account
that such sensors typically are more accurate in measuring lateral
and vertical dimensions relative to the sensor than distances
perpendicular to the sensor. This is particularly the case for CMOS
and CCD based transducers.
[0242] Considerable work is ongoing to improve the resolution of
the ultrasonic transducers. To take advantage of higher resolution
transducers, more closer together data points should be obtained.
This means that after the envelope has been extracted from the
returned signals, the sampling rate should be increased from
approximately 1000 samples per second to perhaps 2000 samples per
second or even higher. By doubling or tripling the amount data
required to be analyzed, the system which is mounted on the vehicle
will require greater computational power. This results in a more
expensive electronic system. Not all of the data is of equal
importance, however. The position of the occupant in the normal
seating position does not need to be known with great accuracy
whereas as that occupant is moving toward the keep out zone
boundary during pre-crash braking, the spatial accuracy
requirements become more important. Fortunately, the neural network
algorithm generating system has the capability of indicating to the
system designer the relative value of each of the data points used
by the neural network. Thus, as many as, for example, 500 data
points per vector may be collected and fed to the neural network
during the training stage and, after careful pruning, the final
number of data points to be used by the vehicle mounted system may
be reduced to 150, for example. This technique of using the neural
network algorithm-generating program to prune the input data is an
important teaching of the present invention. By this method, the
advantages of higher resolution transducers can be optimally used
without increasing the cost of the electronic vehicle mounted
circuits. Also, once the neural network has determined the spacing
of the data points, this can be fine-tuned, for example, by
acquiring more data points at the edge of the keep out zone as
compared to positions well into the safe zone. The initial
technique is done by collecting the full 500 data points, for
example, while in the system installed in the vehicle the data
digitization spacing can be determined by hardware or software so
that only the required data is acquired.
[0243] The technique that was described above for the determination
of the location of an occupant during panic or braking pre-crash
situations involved the use of a modular neural network. In that
case, one neural network was used to determine the occupancy state
of the vehicle and the second neural network was used to determine
the location of the occupant within the vehicle. The method of
designing a system utilizing multiple neural networks is a key
teaching of the present invention. When this idea is generalized,
many potential combinations of multiple neural network
architectures become possible. Some of these will now be
discussed.
[0244] One of the earliest attempts to use multiple neural networks
was to combine different networks trained differently but on
substantially the same data under the theory that the errors which
affect the accuracy of one network would be independent of the
errors which affect the accuracy of another network. For example,
for a system containing four ultrasonic transducers, four neural
networks could be trained each using a different subset of the four
transducer data. Thus, if the transducers are arbitrarily labeled
A, B, C and D the then the first neural network would be trained on
data from A, B and C. The second neural network would be trained on
data from B, C, and D etc. This technique has not met with a
significant success since it is an attempt to mask errors in the
data rather than to eliminate them. Nevertheless, such a system
does perform marginally better in some situations compared to a
single network using data from all four transducers. The penalty
for using such a system is that the computational time is increased
by approximately a factor of three. This significantly affects the
cost of the system installed in a vehicle.
[0245] An alternate method of obtaining some of the advantages of
the parallel neural network architecture described above, is to
form a single neural network but where the nodes of one or more of
the hidden layers are not all connected to all of the input nodes.
Alternately, if the second hidden layer is chosen, all of the notes
from the previous hidden layer are not connected to all of the
nodes of the subsequent layer. The alternate groups of hidden layer
nodes can then be fed to different output notes and the results of
the output nodes combined, either through a neural network training
process into a single decision or a voting process. This latter
approach retains most of the advantages of the parallel neural
network while substantially reducing the computational
complexity.
[0246] The fundamental problem with parallel networks is that they
focus on achieving reliability or accuracy by redundancy rather
than by improving the neural network architecture itself or the
quality of the data being used. They also increase the cost of the
final vehicle installed systems. Alternately, modular neural
networks improve the accuracy of the system by dividing up the
tasks. For example, if a system is to be designed to determine the
type of tree and the type of animal in a particular scene, the
modular approach would be to first determine whether the object of
interest is an animal or a tree and then use separate neural
networks to determine type of tree and the type of animal. When a
human looks at a tree he is not ask himself is that a tiger or a
monkey. Modular neural network systems are efficient since once the
categorization decision is made, the seat is occupied by forward
facing human, for example, the location of that object can be
determined more accurately and without requiring increased
computational resources.
[0247] Another example where modular neural networks have proven
valuable is to provide a means for separating "normal" from
"special cases". It has been found that in some cases, the vast
majority of the data falls into what might be termed "normal" cases
that are easily identified with a neural network. The balance of
the cases cause the neural network considerable difficulty,
however, there are identifiable characteristics of the special
cases that permits them to be separated from the normal cases and
dealt with separately. Various types of human intelligence rules
can be used, in addition to a neural network, to perform this
separation including fuzzy logic, statistical filtering using the
average class vector of normal cases, the vector standard
deviation, and threshold where a fuzzy logic network is used to
determine chance of a vector belonging to a certain class. If the
chance is below a threshold, the standard neural network is used
and if above the special one is used. Mean-Variance connections,
Fuzzy Logic, Stochastic, and Genetic Algorithm networks, and
combinations thereof such as Neuro-Fuzzy systems are other
technologies considered in designing an appropriate system. During
the process of designing a system to be adapted to a particular
vehicle, many different neural network architectures are considered
including those mentioned above. The particular choice of
architecture is frequently determined on a trial and error basis by
the system designer in many cases using the combination neural
network CAD software from Consultants International Ltd. Although
the parallel architecture system described above has not proven to
be in general beneficial, one version of this architecture has
shown some promise. It is known that when training a neural
network, that as the training process proceeds the accuracy of the
decision process improves for the training and independent
databases. It is also known that the ability of the network to
generalize suffers. That is, when the network is presented with a
system which is similar to some case in the database but still with
some significant differences, the network may make the proper
decision in the early stages of training, but the wrong decisions
after the network has become fully trained. This is sometimes
called the young network vs. old network dilemma. In some cases,
therefore, using an old network in parallel with a young network
can retain some of the advantages of both networks, that is, the
high accuracy of the old network coupled with the greater
generality of the young network. Once again, the choice of any of
these particular techniques is part of the process of designing a
system to be adapted to a particular vehicle and is the prime
subject of this invention. The particular combination of tools used
depends on the particular application and the experience of the
system designer.
[0248] The methods above have been described in connection with the
use of ultrasonic transducers. Many of the methods, however, are
also applicable to optical, radar, capacitive and other sensing
systems and where applicable, this invention is not limited to
ultrasonic systems. In particular, an important feature of this
invention is the proper placement of three or more separately
located receivers such that the system still operates with high
reliability if one of the receivers is blocked by some object such
as a newspaper. This feature is also applicable to systems using
electromagnetic radiation instead of ultrasonic, however the
particular locations will differ based on the properties of the
particular transducers. Optical sensors based on two-dimensional
cameras or other image sensors, for example, are more appropriately
placed on the sides of a rectangle surrounding the seat to be
monitored rather than at the comers of such a rectangle as is the
case with ultrasonic sensors. This is because ultrasonic sensors
measure an axial distance from the sensor where the camera is most
appropriate for measuring distances up and down and across its
field view rather than distances to the object. With the use of
electromagnetic radiation and the advances which have recently been
made in the field of very low light level sensitivity, it is now
possible, in some implementations, to eliminate the transmitters
and use background light as the source of illumination along with
using a technique such as auto-focusing to obtain the distance from
the receiver to the object. Thus, only receivers would be required
further reducing the complexity of the system.
[0249] Optical sensors can be used to obtain a three dimensional
measurement of the object through a variety of methods that use
time of flight, modulated light and phase measurement, quantity of
light received within a gated window, structured light and
triangulation etc. Some of these techniques are discussed in U.S.
patent application Ser. No. 09/389,947 filed Sep. 3, 1999, which is
incorporated herein by reference.
[0250] Although implicit in the above discussion, an important
feature of this invention which should be emphasized is the method
of developing a system having distributed transducer mountings.
Other systems which have attempted to solve the rear facing child
seat (RFCS) and out-of-position problems have relied on a single
transducer mounting location or at most, two transducer mounting
locations. Such systems can be easily blinded by a newspaper or by
the hand of an occupant, for example, which is imposed between the
occupant and the transducers. This problem is almost completely
eliminated through the use of three or more transducers which are
mounted so that they have distinctly different views of the
passenger compartment volume of interest. If the system is adapted
using four transducers as illustrated in the distributed system of
FIG. 9, for example, the system suffers only a slight reduction in
accuracy even if two of the transducers are covered so as to make
them inoperable.
[0251] It is important in order to obtain the full advantages of
the system when a transducer is blocked, that the training and
independent databases contains many examples of blocked
transducers. If the pattern recognition system, the neural network
in this case, has not been trained on a substantial number of
blocked transducer cases, it will not do a good job in recognizing
such cases later. This is yet another instance where the makeup of
the databases is crucial to the success of designing the system
that will perform with high reliability in a vehicle and is an
important aspect of the instant invention.
[0252] Other techniques which may or may not be part of the process
of designing a system for a particular application include the
following:
[0253] 1. Fuzzy logic. As discussed above, neural networks
frequently exhibit the property that when presented with a
situation that is totally different from any previously encounter,
an irrational decision can result. Frequently when the trained
observer looks at input data, certain boundaries to the data become
evident and cases that fall outside of those boundaries are
indicative of either corrupted data or data from a totally
unexpected situation. It is sometimes desirable for the system
designer to add rules to handle these cases. These can be fuzzy
logic based rules or rules based on human intelligence. One example
would be that when certain parts of the data vector fall outside of
expected bounds that the system defaults to an airbag enable
state.
[0254] 2. Genetic algorithms. When developing a neural network
algorithm for a particular vehicle, there is no guarantee that the
best of all possible algorithms has been selected. One method of
improving the probability that the best algorithm has been selected
is to incorporate some of the principles of genetic algorithms. In
one application of this theory, the network architecture and/or the
node weights are varied pseudo-randomly to attempt to find other
combinations which have higher success rates. The discussion of
such genetic algorithms systems appears in the book Computational
Intelligence referenced above.
[0255] 3. Pre-processing. For military target recognition is common
to use the Fourier transform of the data rather than the data
itself. This can be especially valuable for categorization as
opposed to location of the occupant and the vehicle. When used with
a modular network, for example, the Fourier transform of the data
may be used for the categorization neural network and the
non-transformed data used for the position determination neural
network. Recently wavelet transforms have also been considered as a
preprocessor.
[0256] 4. Occupant position determination comparison. Above, under
the subject of dynamic out-of-position, it was discussed that the
position of the occupant can be used as a filter to determine the
quality of the data in a particular vector. This technique can also
be used in general as a method to improve the quality of a vector
of data based on the previous positions of the occupant. This
technique can also be expanded to help differentiate live objects
in the vehicle from inanimate objects. For example, a forward
facing human will change his position frequently during the travel
of the vehicle whereas a box will tend to show considerably less
motion. This is also useful, for example, in differentiating a
small human from an empty seat. The motion of a seat containing a
small human will be significantly different from that of an empty
seat even though the particular vector may not show significant
differences. That is, a vector formed from the differences from two
successive vectors is indicative of motion and thus of an
occupant.
[0257] 5. Blocked transducers. It is sometimes desirable to
positively identify a blocked transducer and when such a situation
is found to use a different neural network which has only been
trained on the subset of unblocked transducers. Such a network,
since it has been trained specifically on three transducers, for
example, will generally perform more accurately than a network
which has been trained on four transducers with one of the
transducers blocked some of the time. Once a blocked transducer has
been identified the occupant can be notified if the condition
persists for more than a reasonable time.
[0258] 6. Other Basic Architectures. The back propagation neural
network is a very successful general-purpose network. However, for
some applications, there are other neural network architectures
that can perform better. If it has been found, for example, that a
parallel network as described above results in a significant
improvement in the system, then, it is likely that the particular
neural network architecture chosen has not been successful in
retrieving all of the information that is present in the data. In
such a case, an RCE, Stochastic, Logicon Projection, or one of the
other approximately 30 types of neural network architectures can be
tried to see if the results improve. This parallel network test,
therefore, is a valuable tool for determining the degree to which
the current neural network is capable of using efficiently the
available data.
[0259] 7. Transducer Geometry. Another technique, which is
frequently used in designing a system for a particular vehicle, is
to use a neural network to determine the optimum mounting
locations, aiming or orientation directions and field angles of
transducers. For particularly difficult vehicles, it is sometimes
desirable to mount a large number of ultrasonic transducers, for
example, and then use the neural network to eliminate those
transducers which are least significant. This is similar to the
technique described above where all kinds of transducers are
combined initially and later pruned.
[0260] 8. Data quantity. Since it is very easy to take large
amounts data and yet large databases require considerably longer
training time for a neural network, a test of the variability of
the database can be made using a neural network. If, for example,
after removing half of the data in the database, the performance of
a trained neural network against the validation database does not
decrease, then the system designer suspects that the training
database contains a large amount of redundant data. Techniques such
as similarity analysis can then be used to remove data that is
virtually indistinguishable from other data. Since it is important
to have a varied database, it is undesirable generally to have
duplicate or essentially duplicate vectors in the database since
the presence of such vectors can bias the system and drive the
system more toward memorization and away from generalization.
[0261] 9. Environmental factors. An evaluation can be made of the
beneficial effects of using varying environmental influences, such
as temperature, during data collection on the accuracy of the
system using neural networks along with a technique such as design
of experiments.
[0262] 10. Database makeup. It is generally believed that the
training database must be flat, meaning that all of the occupancy
states that the neural network must recognize must be approximately
equally represented in the training database. Typically, the
independent database has approximately the same makeup as the
training database. The validation database, on the other hand,
typically is represented in a non-flat basis with representative
cases from real world experience. Since there is no need for the
validation database to be flat, it can include many of the extreme
cases as well as being highly biased towards the most common cases.
This is the theory that is currently being used to determine the
makeup of the various databases. The success of this theory
continues to be challenged by the addition of new cases to the
validation database. When significant failures are discovered in
the validation database, the training and independent databases are
modified in an attempt to remove the failure.
[0263] 11. Biasing. All seated state occupancy states are not
equally important. The final system must be nearly 100% accurate
for forward facing "in-position" humans. Since that will comprise
the majority of the real world situations, even a small loss in
accuracy here will cause the airbag to be disabled in a situation
where it otherwise would be available to protect an occupant. A
small decrease in accuracy will thus result in a large increase in
deaths and injuries. On the other hand, there are no serious
consequences if the airbag is deployed occasionally when the seat
is empty. Various techniques are used to bias the data in the
database to take this into account. One technique is to give a much
higher value to the presence of a forward facing human during the
supervised learning process than to an empty seat. Another
technique is to include more data for forward facing humans than
for empty seats. This, however, can be dangerous as an unbalanced
network leads to a loss of generality.
[0264] 12. Screening. It is important that the loop be closed on
data acquisition. That is, the data must be checked at the time the
data is acquired to the sure that it is good data. Bad data can
happen because of electrical disturbances on the power line,
sources of ultrasound such as nearby welding equipment, or due to
human error. If the data remains in the training database, for
example, then it will degrade the performance of the network.
Several methods exist for eliminating bad data. The most successful
method is to take an initial quantity of data, such as 30,000 to
50,000 vectors, and create an interim network. This is normally
done anyway as an initial check on the system capabilities prior to
engaging in an extensive data collection process. The network can
be trained on this data and, as the real training data is acquired,
the data can be tested against the neural network created on the
initial data set. Any vectors that fail are examined for
reasonableness.
[0265] 13. Vector normalization method. Through extensive research
it has been found that the vector should be normalized based on all
of the data in the vector, that is have all its data values range
from 0 to 1. For particular cases, however, it has been fond
desirable to apply the normalization process selectively,
eliminating or treating differently the data at the early part of
the data from each transducer. This is especially the case when
there is significant ringing on the transducer or cross talk when a
separate send and receive transducer is used. There are times when
other vector normalization techniques are required and the neural
network system can be used to determine the best vector
normalization technique for a particular application.
[0266] 14. Feature extraction. The success of a neural network
system can frequently be aided if additional data is inputted into
the network. One example can be the number of 0 data points before
the first peak is experience. Alternately, the exact distance to
the first peak can be determined prior to the sampling of the data.
Other features can include the number of peaks, the distance
between the peaks, the width of the largest peak, the normalization
factor, the vector mean or standard deviation, etc. These
normalization techniques are frequently used at the end of the
adaptation process to slightly increase the accuracy of the
system.
[0267] 15. Noise. It has been frequently reported in the literature
that adding noise to the data that is provided to a neural network
can improve the neural network accuracy by leading to better
generalization and away from memorization. However, the training of
the network in the presence of thermal gradients has been shown to
substantially eliminate the need to artificially add noise to the
data. Nevertheless, in some cases, improvements have been observed
when random arbitrary noise of a rather low level is superimposed
on the training data.
[0268] 16. Photographic recording of the setup. After all of the
data has been collected and used to train a neural network, it is
common to find a significant number of vectors which, when analyzed
by the neural network, give a weak or wrong decision. These vectors
must be carefully studied especially in comparison with adjacent
vectors to see if there is an identifiable cause for the weak or
wrong decision. Perhaps the occupant was on the borderline of the
keep out zone and strayed into the keep out zone during a
particular data collection event. For this reason, it is desirable
to photograph each setup simultaneous with the collection of the
data. This can be done using a camera mounted in a position whereby
it obtains a good view of the seat occupancy. Sometimes several
cameras are necessary to minimize the effects of blockage by a
newspaper, for example. Having the photographic record of the data
setup is also useful when similar results are obtained when the
vehicle is subjected to road testing. During road testing, the
camera is also present and the test engineer is required to
initiate data collection whenever the system does not provide the
correct response. The vector and the photograph of this real world
test can later be compared to similar setups in the laboratory to
see whether there is data that was missed in deriving the matrix of
vehicle setups for training the vehicle.
[0269] 17. Automation. When collecting data in the vehicle it is
desirable to automate the motion of the vehicle seat, seatback,
windows, visors etc. in this manner the positions of these items
can be controlled and distributed as desired by the system
designer. This minimizes the possibility of taking too much data at
one configuration and thereby unbalancing the network.
[0270] 18. Automatic setup parameter recording. To achieve an
accurate data set, the key parameters of the setup should be
recorded automatically. These include the temperatures at various
positions inside the vehicle, the position of the vehicle seat, and
seatback, the position of the headrest, visor and windows and,
where possible, the position of the vehicle occupants. The
automatic recordation of these parameters minimizes the effects of
human errors.
[0271] 19. Laser Pointers. During the initial data collection with
full homs mounted on the surface of the passenger compartment, care
must the exercised so that the transducers are not accidentally
moved during the data collection process. In order to check for
this possibility, a small laser diode is incorporated into each
transducer holder. The laser is aimed so that it illuminates some
other surface of the passenger compartment at a known location.
Prior to each data taking session, each of the transducer aiming
points is checked.
[0272] 20. Multi-frequency transducer placement. When data is
collected for dynamic out-of-position, each of the ultrasonic
transducers must operate at a different frequency so that all
transducers can transmit simultaneously. By this method data can be
collected every 10 milliseconds, which is sufficiently fast to
approximately track the motion of an occupant during pre-crash
braking prior to an impact. A problem arises in the spacing of the
frequencies between the different transducers. If the spacing is
too close, it becomes very difficult to separate the signals from
different transducers and it also affects the sampling rate of the
transducer data and thus the resolution of the transducers. If an
ultrasonic transducer operates at a frequency much below about 35
kHz, it can be sensed by dogs and other animals. If the transducer
operates at a frequency much above 70 kHz, it is very difficult to
make the open type of ultrasonic transducer which produces the
highest sound pressure. If the multiple frequency system is used
for both the driver and passenger-side, eight separate frequencies
are required. In order to find eight frequencies between 35 and 70
kHz, a frequency spacing of 5 kHz is required. In order to use
conventional electronic filters and to provide sufficient spacing
to permit the desired resolution at the keep out zone border, a 10
kHz spacing is desired. These incompatible requirements can be
solved through a careful judicious placement of the transducers
such that transducers that are within 5 kHz of each other are
placed in such a manner that there is no direct path between the
transducers and any indirect path is sufficiently long so that it
can be filtered temporally. An example of such an arrangement is
shown on FIG. 11. For this example, the transducers operate at the
following frequencies A 65 kHz, B 55 kHz, C 35 kHz, D 45 kHz, E 50
kHz, F 40 kHz, G 60 kHz, H 70 kHz. Actually, other arrangements
adhering to the principle described above would also work.
[0273] 21. Use of a PC in data collection. When collecting data for
the training, independent, and validation databases, it is
frequently desirable to test the data using various screening
techniques and to display the data on a monitor. Thus, during data
collection the process is usually monitored using a desktop PC for
data taken in the laboratory and a laptop PC for data taken on the
road.
[0274] 22. Use of referencing markers and gages. In addition to and
sometimes as a substitution for, the automatic recording of the
positions of the seats, seatbacks, windows etc. as described above,
a variety of visual markings and gages are frequently used. This
includes markings to show the angular position of the seatback, the
location of the seat on the seat track, the openness of the window,
etc. Also in those cases where automatic tracking of the occupant
is not implemented, visual markings are placed such that a
technician can observe that the test occupant remains within the
required zone for the particular data taking exercise. Sometimes, a
laser diode is used to create a visual line in the space that
represents the boundary of the keep out zone or other desired zone
boundary.
[0275] It is important to realize that the adaptation process
described herein applies to any combination of transducers that
provide information about the vehicle occupancy. These include
weight sensors, capacitive sensors, inductive sensors, moisture
sensors, ultrasonic, optic, infrared, radar among others. The
adaptation process begins with a selection of candidate transducers
for a particular vehicle model. This selection is based on such
considerations as cost, alternate uses of the system other than
occupant sensing, vehicle interior passenger compartment geometry,
desired accuracy and reliability, vehicle aesthetics, vehicle
manufacturer preferences, and others. Once a candidate set of
transducers has been chosen, these transducers are mounted in the
test vehicle according to the teachings of this invention. The
vehicle is then subjected to an extensive data collection process
wherein various objects are placed in the vehicle at various
locations as described below and an initial data set is collected.
A pattern recognition system is then developed using the acquired
data and an accuracy assessment is made. Further studies are made
to determine which, if any, of the transducers can be eliminated
from the design. In general the design process begins with a
surplus of sensors plus an objective as to how many sensors are to
be in the final vehicle installation. The adaptation process can
determine which of the transducers are most important and which are
least important and the least important transducers can be
eliminated to reduce system cost and complexity.
[0276] Although several preferred methods are illustrated and
described above, there are other possible combinations using
different sensors located at different positions within the
automobile passenger compartment which measure either the same or
different characteristics of an occupying object to accomplish the
same or similar goals as those described herein. There are also
numerous additional applications in addition to those described
above including, but not limited to, monitoring the driver seat,
the center seat or the rear seat of the vehicle or for controlling
other vehicle systems in addition to the airbag system. This
invention is not limited to the above embodiments and should be
determined by the following claims.
Appendix 1
[0277]
1 Subject Classification Class Instances Weight Category State ES
Empty Seat <10 lb Empty FFA Normally Seated Adult >105 lb
Enable FFC Normally Seated Child <10,105> lb Enable FFC
Normally Positioned Forward Facing Child Seat <10,45> lb
Enable OOP Out-of-position Adult >105 lb Disable OOP
Out-of-position Child <105 lb Disable OOP Out-of-position
Forward Facing Child Seat <10,45> lb Disable RFS Rearward
Facing Child Seat <10,45> lb Disable RFS Rearward Facing
Infant Seat <10,45> lb Disable
[0278]
2 Categorization of Human Subjects Weight Range Height Range kg
(lb) m(in) Child <0.95,1.15>(<3'1",3'9">)
<1.10,1.30>(<3'7",4'3">)
<1.25,1.45>(<4'1",4'9">) <11,25>(<24,55>)
C11 C12 C13 <22,36>(<48,79>) C21 C22 C23
<33,47>(<73,103>) C31 C32 C33 Adult
<1.45,1.65>(<4'9",5'5">)
<1.60,1.80>(<5'3",5'11">- )
<1.75,1.95>(<5'9",6'5">)
<45,70>(<99,154>- ;) A11 A12 A13
<65,90>(<143,198>) A21 A22 A23
<85,110>(<187,242>) A31 A32 A33 All Human Subjects are
to wear light clothes (typically slacks and T-shirt) on entry.
Other types of clothing to be provided by ATI
[0279]
3 Child Surrogates Doll Baby 0.50 m (approx. 20") Infant 0.75 m
(approx. 30") Child = 1.20 m (approx. 48")
[0280]
4 Designation Child Seat Attributes Rearward Facing Infant Seats
Training Arriva base, hood Independent Assura 565 hood Training
Baby-Safe -- Training Century 590 base, hood Training Evenflo
Discovery base, Tbar Training Evenflo Joyride (new) hood
Independent Evenflo Joyride (old) -- Training Geny Guard base
Validation Kolcraft Travelabout base, Tbar Training Rock-n-Ride --
Training TLC -- Rearward Facing Child Seat Training Century 1000 --
Validation Century 2000 STE -- Training Century Ovation -- Training
Century Smartmove table 5T Training Champion table Training Fisher
Price Child Seat table Training Touriva -- Training Ultara table
Training Vario Exclusive table Forward Facing Child and Booster
Seats Training Century 1000 -- Validation Century 2000 STE --
Training Century Ovation -- Validation Century Smartmove table 5T
Training Champion table Validation Fisher Price Booster -- Training
Fisher Price Child Seat table Training Gerry Booster table Training
Touriva -- Training Ultara table Training Vario Exclusiv table
[0281]
5 Vehicle Configuration Series Seat Track (+/- 0.5") Seatback
Recline (+/- 2.sup.0) Configuration 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5
6 7 8 9 10 A 0 0 2 2 2 4 4 6 6 6 0 18 4 12 20 2 20 0 8 16 B 1 1 3 3
3 5 5 7 7 7 2 20 0 8 16 0 18 4 12 20 C 0 0 2 2 4 4 4 6 6 6 5 15 4
16 0 15 20 2 10 18 D 1 1 3 3 5 5 5 7 7 7 4 16 5 15 2 10 18 0 15 20
E 0 0 0 2 2 2 4 4 6 6 0 8 16 4 12 20 2 20 0 18 F 1 1 1 3 3 3 5 5 7
7 4 12 20 0 8 16 0 18 2 20 G 0 0 2 2 2 4 4 4 6 6 4 16 2 20 2 10 18
0 15 20 H 1 1 3 3 3 5 5 5 7 7 2 20 4 16 0 15 20 2 10 18 Windows
Visor Configuration 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 A D D
U U D D U U D D U D U D S U D U D S B U U D D U U D D U U D U D U S
D U D U S C U U D D U U D D U U U D S U D U D S U D D D D U U D D U
U D D D U S D U D U S D U E D D U U D D U U D D D U D S U D U S D U
F U U D D U U D D U U U D U S D U D S U D G U U D D U U D D U U U S
D U D U D U S D H D D U U D D U U D D D S U D U D U D S U
Convertible Top Configuration 1 2 3 4 5 6 7 8 9 10 A U U U U U D D
D D D B D D D D D U U U U U C D D D D D U U U U U D U U U U U D D D
D D E U U U U U D D D D D F D D D D D U U U U U G D D D D D U U U U
U H U U U U U D D D D D Sequence for Child Seat Training Data
Collection Start object in center of the seat. Trainer has both
hands on the steering wheel; With a smooth motion, push the object
fully outboard, then pull it fully inboard, then push it to center
position, then put hands back on the steering wheel, With a smooth
motion, rotate the object 45 degrees outboard, the rotate 45
degrees inboard, then rotate back to center, then put hands back on
the wheel Sequence for Out-of-Position Forward Facing Child Seat
Training Data Collection Start with object in the center line,
leaning onto the Instrument Panel; With a smooth motion, push the
object fully outboard, then pull it fully inboard, then push it to
the center, Repeat this sequence with a 150 mm (6") gap between the
object and the Instrument Panel, Apply small (+/- 10.sup.0)
rotations Repeat this sequence with a 300 mm (12") gap between the
object and the Instrument Panel; Apply small (+/- 10.sup.0)
rotations. Sequence for Human Subject Training Data Collection.
Lean forward and outboard such that head and/or shoulders touch the
Fire line; Gently traverse inboard while carefully following the
Fire line until the center of the vehicle is reached; Lean halfway
back towards the seatback and traverse outboard up against the side
window. Rotate torso while doing so, Lean back into the seat and
traverse inboard towards the center Rotate torso while doing so;
Sit back in the seat; "operate" radio controls, glove box, window,
or seat controls; assume a brace posture; Do not cross the Fire
line with head and/or shoulders at any time. Sequence for
Out-of-Position Human Subject Training Data Collection: Lean
forward and outboard such that head and/or shoulders touch the
Instrument Panel; Gently traverse inboard towards the center
console; Move back 150 mm (6") and gently traverse back to the most
outboard position; Move back 300 mm (12") and gently traverse back
to the center console, "Operate" radio controls and glovebox while
head and/or shoulders remain in front of the Fire line
[0282]
6 Network Training Set Collection Matrix (Vehicle E) Rev 1.1 #
Class Subject/Object Attributes Actions Config. Belt Conditions 1
ES None None Motions of track and (A) N.A. Ambient recline 2 FFA
A22 Medium Clothes, Motions in safe seating B Yes Ambient Magazine
area 3 OOP A22 Medium Clothes Motions in NFZ C No Ambient 4 FFC
Century 1000 Infant Doll Motions in safe seating D No Ambient area
5 RFS Century 1000 Baby Doll Motions in entire E No Ambient seating
area 6 ES None Beaded Cover Motions of track and (F) N.A. Ambient
recline 7 FFA A11 Medium Clothes Motions in safe seating G Yes
Ambient area 8 OOP Touriva Infant Doll, Blanket Motions in NFZ H No
Ambient 9 FFC Touriva Infant Doll, Blanket Motions in safe seating
A No Ambient area 10 RFS Century 590 Baby Doll, Hood Motions in
entire B No Ambient seating area 11 ES None Fabric Cover Motions of
track and (C) N.A. Ambient recline 12 FFA A33 Medium Clothes,
Motions in safe seating D No Ambient Newspaper area 13 OOP A33
Medium Clothes Motions in NFZ E Yes Ambient 14 FFC C22 Medium
Clothes Motions in safe seating F No Ambient area 15 RFS Touriva
Baby Doll, Blanket Motions in entire G No Ambient seating area 16
ES None Blanket Motions of track and (H) N.A. Ambient recline 17
FFA A21 Heavy Clothes Motions in safe seating A No Ambient area 18
OOP C11 Heavy Clothes Motions in NFZ B No Ambient (standing) 19 FFC
C11 Heavy Clothes Motions in safe seating C No Ambient area 20 RFS
TLC Baby Doll Motions in entire D No Ambient seating area 21 ES
None None Motions of track and (E) N.A. Solar Heat recline 22 FFA
A12 Light Clothes, Motions in safe seating F Yes Solar Heat
Magazine area 23 OOP A12 Light Clothes Motions in NFZ G No Solar
Heat 24 FFC Champion Infant Doll Motions in safe seating H No Solar
Heat area 25 RFS Champion Baby Doll Motions in entire A No Solar
Heat seating area 26 ES None Beaded Cover Motions of track and (B)
N.A. Solar Heat recline 27 FFA A23 Light Clothes Motions in safe
seating C Yes Solar Heat area 28 OOP Vario Exclusive Child Doll
Motions in NFZ D No Solar Heat 29 FFC Vario Exclusive Child Doll,
Blanket Motions in safe seating E No Solar Heat area 30 RFS Joyride
(new) Baby Doll Motions in entire F No Solar Heat seating area 31
ES None Fabric Cover Motions of track and (G) N.A. Solar Heat
recline 32 FFA A32 Light Clothes, Motions in safe seating H No
Solar Heat Newspaper area 33 OOP A32 Light Clothes Motions in NFZ A
Yes Solar Heat 34 FFC C33 Light Clothes Motions in safe seating B
No Solar Heat area 35 RFS Ultara Baby Doll, Blanket Motions in
entire C No Solar Heat seating area 36 ES None Blanket Motions of
track and (D) N.A. Solar Heat recline 37 FFA A22 Medium Clothes
Motions in safe seating E No Solar Heat area 38 OOP C21 Medium
Clothes Motions in NFZ F No Solar Heat 39 FFC C21 Medium Clothes
Motions in safe seating G No Solar Heat area 40 RFS Arriva Baby
Doll, Hood Motions in entire H No Solar Heat seating area 41 ES
None Handbag Motions of track and (H) N.A. Ambient recline 42 FFA
A11 Heavy Clothes, Motions in safe seating G Yes Ambient Magazine
area 43 OOP A11 Heavy Clothes Motions in NFZ F No Ambient 44 FFC
Gerry Booster Infant Doll Motions in safe seating E No Ambient area
45 RFS Fisher Price CS Baby Doll Motions in entire D No Ambient
seating area 46 ES None Beaded Cover, Handbag Motions of track and
(C) N.A. Ambient recline 47 FFA A33 Heavy Clothes Motions in safe
seating B Yes Ambient area 48 OOP Ultara Infant Doll, Blanket
Motions in NFZ A No Ambient 49 FFC Ultara Infant Doll, Blanket
Motions in safe seating H No Ambient area 50 RFS Baby Safe Baby
Doll, Handle up Motions in entire G No Ambient seating area 51 ES
None Fabric Cover, Handbag Motions of track and (F) N.A. Ambient
recline 52 FFA A21 Heavy Clothes, Motions in safe seating E No
Ambient Newspaper area 53 OOP A21 Heavy Clothes Motions in NFZ D
Yes Ambient 54 FFC C12 Heavy Clothes Motions in safe seating C No
Ambient area 55 RFS Vario Exclusive Baby Doll, Blanket Motions in
entire B No Ambient seating area 56 ES None Blanket, Handbag
Motions of track and (A) N.A. Ambient recline 57 FFA A12 Rain
Clothes Motions in safe seating H No Ambient area 58 OOP C23 Rain
Clothes Motions in NFZ G No Ambient 59 FFC C23 Rain Clothes Motions
in safe seating F No Ambient area 60 FFS Rock'n'Ride Baby Doll
Motions in entire E No Ambient seating area 61 ES None None Motions
of track and (D) N.A. Air Conditioner recline 62 FFA A23 Light
Clothes, Motions in safe seating C Yes Air Conditioner Magazine
area 63 OOP A23 Light Clothes Motions in NFZ B No Air Conditioner
64 FFC Century Infant Doll Motions in safe seating A No Air
Conditioner Ovation area 65 RFS Century Baby Doll Motions in entire
H No Air Conditioner Ovation seating area 66 ES None Beaded Cover
Motions of track and (G) N.A. Air Conditioner recline 67 FFA A32
Light Clothes Motions in safe seating F Yes Air Conditioner area 68
OOP Fisher Price CS Child Doll Motions in NFZ E No Air Conditioner
69 FFC Fisher Price CS Child Doll, Blanket Motions in safe seating
D No Air Conditioner area 70 RFS Gerry Guard Baby Doll Motions in
entire C No Air Conditioner seating area 71 ES None Fabric Cover
Motions of track and (B) N.A. Air Conditioner recline 72 FFA A22
Light Clothes, Motions in safe seating A No Air Conditioner
Newspaper area 73 OOP A22 Light Clothes Motions in NFZ H Yes Air
Conditioner 74 FFC C32 Light Clothes Motions in safe seating G No
Air Conditioner area 75 RFS Smartmove 5T Baby Doll, Blanket Motions
in entire F No Air Conditioner seating area 76 ES None Blanket
Motions of track and (E) N.A. Air Conditioner recline 77 FFA A11
Medium Clothes Motions in safe seating D No Air Conditioner area 78
OOP C22 Medium Clothes Motions in NFZ C No Air Conditioner 79 FFC
C22 Medium Clothes Motions in safe seating B No Air Conditioner
area 80 RFS Discovery Baby Doll, Handle up Motions in entire A No
Air Conditioner seating area 81 ES None Pizza Box Motions of track
and (B) N.A. Ambient recline 82 FFA A33 Rain Clothes, Magazine
Motions in safe seating A Yes Ambient area 83 OOP A33 Rain Clothes
Motions in NFZ D Yes Ambient 84 FFC Champion Infant Doll Motions in
safe seating C No Ambient area 85 RFS Champion Baby Doll Motions in
entire F No Ambient seating area 86 ES None Beaded Cover, Pizza
Motions of track and (E) N.A. Ambient Box recline 87 FFA A21 Rain
Clothes Motions in safe seating H Yes Ambient area 88 OOP Vario
Exclusive Child Doll, Blanket Motions in NFZ G No Ambient 89 FFC
Vario Exclusive Child Doll, Blanket Motions in safe seating B No
Ambient area 90 RFS Joyride (new) Baby Doll, Hood Motions in entire
A No Ambient seating area 91 ES None Fabric Cover, Pizza Box
Motions of track and (D) N.A. Ambient recline 92 FFA A12 Rain
Clothes, Motions in safe seating C No Ambient Newspaper area 93 OOP
A12 Rain Clothes Motions in NFZ F No Ambient 94 FFC C23 Rain
Clothes Motions in safe seating E No Ambient area 95 RFS Ultara
Baby Doll, Blanket Motions in entire H No Ambient seating area 96
ES None Blanket, Pizza Box Motions of track and (G) N.A. Ambient
recline 97 FFA A23 Light Clothes Motions in safe seating B No
Ambient area 98 OOP C32 Light Clothes Motions in NFZ A No Ambient
99 FFC C32 Light Clothes Motions in safe seating D No Ambient area
100 RFS Arriva Baby Doll, Hood Motions in entire C No Ambient
seating area 101 ES None None Motions of track and (F) N.A. Car
Heat recline 102 FFA A32 Light Clothes, Motions in safe seating E
Yes Car Heat Magazine area 103 OOP A32 Light Clothes Motions in NFZ
H Yes Car Heat 104 FFC Century 1000 Infant Doll Motions in safe
seating G No Car Heat area 105 RFS Century 1000 Baby Doll Motions
in entire B No Car Heat seating area 106 ES None Beaded Cover
Motions of track and (A) N.A. Car Heat recline 107 FFA A22 Rain
Clothes Motions in safe seating D Yes Car Heat area 108 OOP Vario
Exclusive Infant Doll Motions in NFZ C No Car Heat 109 FFC Touriva
Infant Doll, Blanket Motions in safe seating F No Car Heat area 110
RFS Century 590 Baby Doll Motions in entire E No Car Heat seating
area 111 ES None Fabric Cover Motions of track and (H) N.A. Car
Heat recline 112 FFA A11 Light Clothes, Motions in safe seating G
No Car Heat Newspaper area 113 OOP A11 Light Clothes Motions in NFZ
B No Car Heat 114 FFC C32 Light Clothes Motions in safe seating A
No Car Heat area 115 RFS Touriva Baby Doll, Blanket Motions in
entire D No Car Heat seating area 116 ES None Blanket Motions of
track and (C) N.A. Car Heat recline 117 FFA A33 Heavy Clothes
Motions in safe seating F No Car Heat area 118 OOP C22 Heavy
Clothes Motions in NFZ E No Car Heat 119 FFC C22 Heavy Clothes
Motions in safe seating H No Car Heat area 120 RFS TLC Baby Doll
Motions in entire G No Car Heat seating area 121 ES None Attached
Case (flat) Motions of track and (G) N.A. Ambient recline 122 FFA
A21 Heavy Clothes, Motions in safe seating H Yes Ambient Magazine
area 123 OOP A21 Heavy Clothes Motions in NFZ E Yes Ambient 124 FFC
Century Infant Doll Motions in safe seating F No Ambient Ovation
area 125 RFS Century Baby Doll Motions in entire C No Ambient
Ovation seating area 126 ES None Beaded Cover, Attache Motions of
track and (D) N.A. Ambient Case recline 127 FFA A12 Rain Clothes
Motions in safe seating A Yes Ambient area 128 OOP Fisher Price CS
Infant Doll, Blanket Motions in NFZ B No Ambient 129 FFC Fisher
Price CS Infant Doll Motions in safe seating G No Ambient area 130
RFS Gerry Guard Baby Doll, Handle up Motions in entire H No Ambient
seating area 131 ES None Fabric Cover, Attache Motions of track and
(E) N.A. Ambient Case recline 132 FFA A23 Heavy Clothes, Motions in
safe seating F No Ambient Newspaper area 133 OOP A23 Heavy Clothes
Motions in NFZ C No Ambient 134 FFC C11 Heavy Clothes Motions in
safe seating D No Ambient area 135 RFS Smartmove 5T Baby Doll,
Blanket Motions in entire A No Ambient seating area 136 ES None
Blanket, Attached Case Motions of track and (B) N.A. Ambient
recline 137 FFA A32 Rain Clothes Motions in safe seating G No
Ambient area 138 OOP C33 Rain Clothes Motions in NFZ H No Ambient
139 FFC C33 Rain Clothes Motions in safe seating E No Ambient area
140 RFS Discovery Baby Doll, Handle up Motions in entire F No
Ambient seating area 141 ES None Hand Bag Motions of track and (C)
N.A. Solar Heat recline 142 FFA A22 Medium Clothes, Motions in safe
seating D Yes Solar Heat Magazine area 143 OOP A22 Heavy Clothes
Motions in NFZ A Yes Solar Heat 144 FFC Gerry Booster Child Doll
Motions in safe seating B No Solar Heat area 145 RFS Fisher Price
CS Baby Doll Motions in entire G No Solar Heat seating area 146 ES
None Beaded Cover, Hand Motions of track and (H) N.A. Solar Heat
Bag recline 147 FFA A11 Medium Clothes Motions in safe seating E
Yes Solar Heat area 148 OOP Vario Exclusive Infant Doll Motions in
NFZ F No Solar Heat 149 FFC Ultara Infant Doll, Blanket Motions in
safe seating C No Solar Heat area 150 RFS Baby Safe Baby Doll
Motions in entire D No Solar Heat seating area 151 ES None Fabric
Cover, Hand Bag Motions of track and (A) N.A. Solar Heat recline
152 FFA A33 Medium Clothes, Motions in safe seating B No Solar Heat
Newspaper area 153 OOP A33 Medium Clothes Motions in NFZ G No Solar
Heat 154 FFC C33 Medium Clothes Motions in safe seating H No Solar
Heat area 155 RFS Vario Exclusive Baby Doll, Blanket Motions in
entire E No Solar Heat seating area 156 ES None Blanket, Hand Bag
Motions of track and (F) N.A. Solar Heat recline 157 FFA A21 Light
Clothes Motions in safe seating C No Solar Heat area 158 OOP C21
Light Clothes Motions in NFZ D No Solar Heat 159 FFC C21 Light
Clothes Motions in safe seating A No Solar Heat area 160 RFS
Rock'n'Ride Baby Doll Motions in entire B No Solar Heat seating
area
[0283]
7 Network Independent Test Set Collection Matrix (Vehicle E) Rev
1.1 (Under Construction) # Class Subject/Object Attributes Actions
Config. Belt Conditions 1 ES Motions of track and (A) N.A. Ambient
recline 2 FFA Motions in safe seating B Yes Ambient area 3 OOP
Motions in NFZ C No Ambient 4 FFC Motions in safe seating D No
Ambient area 5 RFS Motions in entire E No Ambient seating area 6 ES
Motions of track and (F) N.A. Ambient recline 7 FFA Motions in safe
seating G Yes Ambient area 8 OOP Motions in NFZ H No Ambient 9 FFC
Motions in safe seating A No Ambient area 10 RFS Motions in entire
B No Ambient seating area 11 ES Motions of track and (C) N.A.
Ambient recline 12 FFA Motions in safe seating D No Ambient area 13
OOP Motions in NFZ E Yes Ambient 14 FFC Motions in safe seating F
No Ambient area 15 RFS Motions in entire G No Ambient seating area
16 ES Motions of track and (H) N.A. Ambient recline 17 FFA Motions
in safe seating A No Ambient area 18 OOP Motions in NFZ B No
Ambient (standing) 19 FFC Motions in safe seating C No Ambient area
20 RFS Motions in entire D No Ambient seating area
Appendix 2
[0284] Analysis of Neural Network Training and Data Preprocessing
Methods--An Example
[0285] 1. Introduction
[0286] The Artificial Neural Network that forms the "brains" of the
Occupant Spatial Sensor needs to be trained to recognize airbag
enable and disable patterns. The most important part of this
training is the data that is collected in the vehicle, which
provides the patterns corresponding to these respective
configurations. Manipulation of this data (such as filtering) is
appropriate if this enhances the information contained in the data.
Important too, are the basic network architecture and training
methods applied, as these two determine the learning and
generalization capabilities of the neural network. The ultimate
test for all methods and filters is their effect on the network
performance against real world situations.
[0287] The Occupant Spatial Sensor (OSS) uses an artificial neural
network (ANN) to recognize patterns that it has been trained to
identify as either airbag enable or airbag disable conditions. The
pattern is obtained from four ultrasonic transducers that cover the
front passenger seating area. This pattern consists of the
ultrasonic echoes from the objects in the passenger seat area. The
signal from each of the four transducers consists of the electrical
image of the return echoes, which is processed by the electronics.
The electronic processing comprises amplification (logarithmic
compression), rectification, and demodulation (band pass
filtering), followed by discretization (sampling) and digitization
of the signal. The only software processing required, before this
signal can be fed into the artificial neural network, is
normalization (i.e. mapping the input to numbers between 0 and 1).
Although this is a fair amount of processing, the resulting signal
is still considered "raw", because all information is treated
equally.
[0288] It is possible to apply one or more software preprocessing
filters to the raw signal before it is fed into the artificial
neural network. The purpose of such filters is to enhance the
useful information going into the ANN, in order to increase the
system performance. This document describes several preprocessing
filters that were applied to the ANN training of a particular
vehicle.
[0289] 2. Data Description
[0290] The performance of the artificial neural network is
dependent on the data that is used to train the network. The amount
of data and the distribution of the data within the realm of
possibilities are known to have a large effect on the ability of
the network to recognize patterns and to generalize. Data for the
OSS is made up of vectors. Each vector is a combination of the
useful parts of the signals collected from four ultrasonic
transducers. A typical vector could comprise on the order of 100
data points, each representing the (time displaced) echo level as
recorded by the ultrasonic transducers.
[0291] Three different sets of data are collected. The first set,
the training data, contains the patterns that the ANN is being
trained on to recognize as either an airbag deploy or non-deploy
scenario. The second set is the independent test data. This set is
used during the network training to direct the optimization of the
network weights. The third set is the validation (or real world)
data. This set is used to quantify the success rate (or
performance) of the finalized artificial neural network.
[0292] Table 1 shows the main characteristics of these three data
sets, as collected for the vehicle. Three numbers characterize the
sets. The number of configurations characterizes how many different
subjects and objects were used. The number of setups is the product
of the number of configurations and the number of vehicle interior
variations (seat position and recline, roof and window state, etc.)
performed for each configuration. The total number of vectors is
then made up of the product of the number of setups and the number
of patterns collected while the subject or object moves within the
passenger volume.
8TABLE 1 Characteristics of the Data Sets Data Set Configurations
Setups Vectors Training 130 1300 650,000 Independent Test 130 1300
195,000 Validation 100 100 15,000
[0293] 1.1 Training Data Set Characteristics
[0294] The training data set can be split up in various ways into
subsets that show the distribution of the data. Table 2 shows the
distribution of the training set amongst three classes of passenger
seat occupancy: Empty Seat, Human Occupant, and Child Seat. All
human occupants were adults of various sizes. No children were part
of the training data set other then those seated in Forward Facing
Child Seats. Table 3 shows a further breakup of the Child Seats
into Forward Facing Child Seats, Rearward Facing Child Seats,
Rearward Facing Infant Seats, and out-of-position Forward Facing
Child Seats. Table 4 shows a different type of distribution; one
based on the environmental conditions inside the vehicle.
9TABLE 2 Distribution of Main Training Subjects Occupancy
Representation Empty Seat 10% Human Occupant 32% Child Seat 58%
[0295]
10TABLE 3 Child Seat Distribution Child Seat Configuration
Representation Forward Facing Child Seat 40% Forward Facing Child
Seat Out-of-Position 4% Rearward Facing Child Seat 27% Rearward
Facing Infant Seat 29%
[0296]
11TABLE 4 Distribution of Environmental Conditions Environmental
Condition Representation Ambient 56% Static Heat (Solar Lamp) 25%
Dynamic Heat (Car Heat) 13% Dynamic Cooling (Car A C) 6%
[0297] 1.2 Independent Test Data Characteristics
[0298] The independent test data is created using the same
configurations, subjects, objects, and conditions as used for the
training data set. Its makeup and distributions are therefore the
same as those of the training data set.
[0299] 1.3 Validation Data Characteristics
[0300] The distribution of the validation data set into its main
subsets is shown in Table 5. This distribution is close to that of
the training data set. However, the human occupants comprised both
children (12% of total) as well as adults (27% of total). Table 6
shows the distribution of human subjects. Contrary to the training
and independent test data sets, data was collected on children ages
3 and 6 that were not seated in a child restraint of any kind.
Table 7 shows the distribution of the child seats used. On the
other hand, no data was collected on Forward Facing Child Seats
that were out-of-position. The child and infant seats used in this
data set are different from those used in the training and
independent test data sets. The validation data was collected with
varying environmental conditions as shown in Table 8.
12TABLE 5 Validation Data Distribution Occupancy Representation
Empty Seat 8% Human Occupant 39% Child Seat 53%
[0301]
13TABLE 6 Human Subject Distribution Normally Out-of- Human
Occupant Representation Seated Position Child age 3 15% 50% 50%
Child age 6 15% 50% 50% Adult 5.sup.th percentile Female 23% 67%
33% Adult 50.sup.th percentile Male 23% 67% 33% Adult 95.sup.th
percentile Male 23% 67% 33%
[0302]
14TABLE 7 Child Seat Distribution Child Seat Configuration
Representation Forward Facing Child Seat 11% Forward Facing Booster
Seat 11% Rearward Facing Child Seat 38% Rearward Facing Infant Seat
40%
[0303]
15TABLE 8 Distribution of Environmental Conditions Environmental
Condition Representation Ambient 63% Static Heat (Solar Lamp) 13%
Dynamic Heat (Car Heat) 12% Dynamic Cooling (Car Air Conditioner)
12%
[0304] 3. Network Training
[0305] The baseline network consisted of a four layer
back-propagation network with 117 input layer nodes, 20 and 7 nodes
respectively in the two hidden layers, and 1 output layer node. The
input layer is made up of inputs from four ultrasonic transducers.
These were located in the vehicle on the rear quarter panel (A),
the A-pillar (B), and the over-head console (C, H). Table 9 shows
the number of points, taken from each of these channels that make
up one vector.
16TABLE 9 Transducer Volume Starting Point End Point Transducer
Sample Time (ms) Distance (mm) Sample Time (ms) Distance (mm) A 5
0.83 142 29 4.84 822 B 3 0.50 85 35 5.84 992 C 7 1.17 198 34 5.67
964 H 2 0.33 57 32 5.34 907
[0306] The artificial neural network is implemented using the
NeuralWorks Professional II/Plus software. The method used for
training the decision mathematical model was back-propagation with
Extended Delta-Bar-Delta learning rule and sigmoid transfer
function. The Extended DBD paradigm uses past values of the
gradient to infer the local curvature of the error surface. This
leads to a learning rule in which every connection has a different
learning rate and a different momentum term, both of which are
automatically calculated.
[0307] The network was trained using the above-described training
and independent test data sets. An optimum (against the independent
test set) was found after 3,675,000 training cycles. Each training
cycle uses 30 vectors (known as the epoch), randomly chosen from
the 650,000 available training set vectors. Table 10 shows the
performance of the baseline network.
17TABLE 10 Baseline Network Performance Self Test Success Rate
95.3% Independent Test Success Rate 94.5% Validation Test Success
Rate 92.7%
[0308] The network performance has been further analyzed by
investigating the success rates against subsets of the independent
test set. The success rate against the airbag enable conditions at
94.6% is virtually equal to that against the airbag disable
conditions at 94.4%. Table 11 shows the success rates for the
various occupancy subsets. Table 12 shows the success rates for the
environmental conditions subsets. Although the distribution of this
data was not entirely balanced throughout the matrix, it can be
concluded that the system performance is not significantly degraded
by heat sources.
18TABLE 11 Performance per Occupancy Subset Occupancy Independent
Test Empty Seat 96.1% Normally Seated Adult 92.1% Rearward Facing
Child/Infant Seat 94.1% Forward Facing Child Seat 96.9%
Out-of-Position Human/FFCS 93.0%
[0309]
19TABLE 12 Performance per Environmental Conditions Subset
Environmental Condition Independent Test Ambient 95.4% Long Term
Heat (Lamp Heat) 95.2% Sort Term Heating/Cooling (HVAC) 93.5%
[0310] 3.1 Normalization
[0311] Normalization is used to scale the real world data range
into a range acceptable for the network training. The NeuralWorks
software requires the use of a scaling factor to bring the input
data into a range of 0 to 1, inclusive. Several normalization
methods have been explored for their effect on the system
performance.
[0312] The real world data consists of 12 bit, digitized signals
with values between 0 and 4095. Chart 1 shows a typical raw signal.
A raw vector consists of combined sections of four signals.
[0313] The results of the normalization study are summarized in
Table 13.
20TABLE 13 Normalization Study Results Normalization Method Self
Test Independent Test Validation Test a. Whole Vector (base) 95.3%
94.5% 92.7% b. Per Channel 94.9% 93.8% 90.3% c. Fixed Range
[0,4095] 95.6% 90.3% 88.3%
[0314] A higher performance results from normalizing across the
entire vector versus normalizing per channel. This can be explained
from the fact that the baseline method retains the information
contained in the relative strength of the signal from one
transducer compared to another. This information is lost when using
the second method.
[0315] Normalization using a fixed range retains the information
contained in the relative strength of one vector compared to the
next. From this it could be expected that the performance of the
network trained with fixed range normalization would increase over
that of the baseline method. However, without normalization, the
input range is, as a rule, not from zero to the maximum value (see
FIG. 1). The absolute value of the data at the input layer affects
the network weight adjustment (see equations [1] and [2]). During
network training, vectors with a smaller input range will affect
the weights calculated for each processing element (neuron)
differently than vectors that do span the full range.
.DELTA.w.sub.ij.sup.[s]=lcoef.multidot.e.sub.j.sup.[s].multidot.x.sub.I.su-
p.[s-1][1]
e.sub.j.sup.[s]=x.sub.j.sup.[s].multidot.(1.0-x.sub.j.sup.[s]).multidot..D-
ELTA..sub.k(e.sub.k.sup.[s+1].multidot.w.sub.kj.sup.[s+1]) [2]
[0316] .DELTA.w.sub.ij.sup.[s] is the change in the network
weights; lcoef is the learning coefficient; e.sub.j.sup.[s] is the
local error at neuron j in layer s; x.sub.I.sup.[s] is the current
output state of neuron j in layer s.
[0317] Variations in the highest and lowest values in the input
layer, therefore, have a negative effect on the training of the
network. This is reflected in a lower performance against the
validation data set.
[0318] A secondary effect of normalization is that it increases the
resolution of the signal by stretching it out over the full range
of 0 to 1, inclusive. As the network predominantly learns from
higher peaks in the signal, this results in better generalization
capabilities and therefore in a higher performance.
[0319] It must be concluded that the effects of the fixed range of
input values and the increased resolution resulting from the
baseline normalization method have a stronger effect on the network
training than retaining the information contained in the relative
vector strength.
[0320] 3.2 Low Threshold Filters
[0321] Not all information contained in the raw signals can be
considered useful for network training. Low amplitude echoes are
received back from objects on the outskirts of the ultrasonic field
that should not be included in the training data. Moreover, low
amplitude noise, from various sources, is contained within the
signal. This noise shows up strongest where the signal is weak. By
using a low threshold filter, the signal to noise ratio of the
vectors can be improved before they are used for network
training.
[0322] Three cutoff levels were used: 5%, 10%, and 20% of the
signal maximum value (4095). The method used, brings the values
below the threshold up to the threshold level. Subsequent vector
normalization (baseline method) stretches the signal to the full
range of [0,1].
[0323] The results of the low threshold filter study are summarized
in Table 14.
21TABLE 14 Low Threshold Filter Study Results Threshold Level Self
Test Independent Test Validation Test none (base) 95.3% 94.5% 92.7%
5% of 4095 95.3% 94.4% 91.9% 10% of 4095 95.3% 94.3% 92.5% 20% of
4095 95.1% 94.2% 86.4%
[0324] The performance of the networks trained with 5% and 10%
threshold filter is similar to that of the baseline network. A
small performance degradation is observed for the network trained
with a 20% threshold filter. From this it is concluded that the
noise level is sufficiently low to not affect the network training.
At the same time it can be concluded that the lower 10% of the
signal can be discarded without affecting the network performance.
This allows the definition of demarcation lines on the outskirts of
the ultrasonic field where the signal is equal to 10% of the
maximum field strength.
[0325] 4. Network Types
[0326] The baseline network is a back-propagation type network.
Back-propagation is a general-purpose network paradigm that has
been successfully used for prediction, classification, system
modeling, and filtering as well as many other general types of
problems. Back propagation learns by calculating an error between
desired and actual output and propagating this error information
back to each node in the network. This back-propagated error is
used to drive the learning at each node. Some of the advantages of
a back-propagation network are that it attempts to minimize the
global error and that it can provide a very compact distributed
representation of complex data sets. Some of the disadvantages are
its slow learning and the irregular boundaries and unexpected
classification regions due to the distributed nature of the network
and the use of a transfer functions that is unbounded. Some of
these disadvantages can be overcome by using a modified
back-propagation method such as the Extended Delta-Bar-Delta
paradigm. The EDBD algorithm automatically calculates the learning
rate and momentum for each connection in the network, which
facilitates optimization of the network training.
[0327] Many other network architectures exist that have different
characteristics than the baseline network. One of these is the
Logicon Projection Network. This type of network combines the
advantages of closed boundary networks with those of open boundary
networks (to which the back-propagation network belongs). Closed
boundary networks are fast learning because they can immediately
place prototypes at the input data points and match all input data
to these prototypes. Open boundary networks, on the other hand,
have the capability to minimize the output error through gradient
decent.
[0328] 5. Conclusions
[0329] The baseline artificial neural network trained to a success
rate of 92.7% against the validation data set. This network has a
four-layer back-propagation architecture and uses the Extended
Delta-Bar-Delta learning rule and sigmoid transfer function.
Pre-processing comprised vector normalization while post-processing
comprised a "five consistent decision" filter.
[0330] The objects and subjects used for the independent test data
were the same as those used for the training data. This may have
negatively affected the network's classification generalization
abilities.
[0331] The spatial distribution of the independent test data was as
wide as that of the training data. This has resulted in a network
that can generalize across a large spatial volume. A higher
performance across a smaller volume, located immediately around the
peak of the normal distribution, combined with a lower performance
on the outskirts of the distribution curve, might be
preferable.
[0332] To achieve this, the distribution of the independent test
set needs to be a reflection of the normal distribution for the
system (a.k.a. native population).
[0333] Modifying the pre-processing method or applying additional
pre-processing methods did not show a significant improvement of
the performance over that of the baseline network. The baseline
normalization method gave the best results as it improves the
learning by keeping the input values in a fixed range and increases
the signal resolution. The lower threshold study showed that the
network learns from the larger peaks in the echo pattern.
Pre-processing techniques should be aimed at increasing the signal
resolution to bring out these peaks.
[0334] A further study could be performed to investigate combining
a lower threshold with fixed range normalization, using a range
less than full scale. This would force each vector to include at
least one point at the lower threshold value and one value in
saturation, effectively forcing each vector into a fixed range that
can be mapped between 0 and 1, inclusive. This would have the
positive effects associated with the baseline normalization, while
retaining the information contained in the relative vector
strength. Raw vectors points that, as a result of the scaling,
would fall outside the range of 0 to 1 would then be mapped to 0
and 1 respectively.
[0335] Post-processing should be used to enhance the network
recognition ability with a memory function. The possibilities for
such are currently frustrated by the necessity of one network
performing both object classification as well as spatial locating
functions. Performing the spatial locating function requires
flexibility to rapidly update the system status. Object
classification, on the other hand, benefits from decision rigidity
to nullify the effect of an occasional pattern that is incorrectly
classified by the network.
Appendix 3
Process for Training an OPS System DOOP Network for a Specific
Vehicle
[0336] 1. Define customer requirements and deliverables
[0337] 1.1. Number of zones
[0338] 1.2. Number of outputs
[0339] 1.3. At risk zone definition
[0340] 1.4. Decision definition i.e. empty seat at risk, safe
seating, or not critical and undetermined
[0341] 1.5. Determine speed of DOOP decision
[0342] 2. Develop PERT chart for the program
[0343] 3. Determine viable locations for the transducer mounts
[0344] 3.1. Manufacturability
[0345] 3.2. Repeatability
[0346] 3.3. Exposure (not able to damage during vehicle life)
[0347] 4. Evaluate location of mount logistics
[0348] 4.1. Field dimensions
[0349] 4.2. Multipath reflections
[0350] 4.3. Transducer Aim
[0351] 4.4. Obstructions/Unwanted data
[0352] 4.5. Objective of view
[0353] 4.6. Primary DOOP transducers requirements
[0354] 5. Develop documentation logs for the program (vehicle
books)
[0355] 6. Determine vehicle training variables
[0356] 6.1. Seat track stops
[0357] 6.2. Steering wheel stops
[0358] 6.3. Seat back angles
[0359] 6.4. DOOP transducer blockage during crash
[0360] 6.5. Etc. . . .
[0361] 7. Determine and mark at risk zone in vehicle
[0362] 8. Evaluate location physical impediments
[0363] 8.1. Room to mount/hide transducers
[0364] 8.2. Sufficient hard mounting surfaces
[0365] 8.3. Obstructions
[0366] 9. Develop matrix for training, independent, validation, and
DOOP data sets
[0367] 10. Determine necessary equipment needed for data
collection
[0368] 10.1. Child/booster/infant seats
[0369] 10.2. Maps/razors/makeup
[0370] 10.3. Etc. . . .
[0371] 11. Schedule sled tests for initial and final DOOP
networks
[0372] 12. Design test buck for DOOP
[0373] 13. Design test dummy for DOOP testing
[0374] 14. Purchase any necessary variables
[0375] 14.1. Child/booster/infant seats
[0376] 14.2. Maps/razors/makeup
[0377] 14.3. Etc. . . .
[0378] 15. Develop automated controls of vehicle accessories
[0379] 15.1. Automatic seat control for variable empty seat
[0380] 15.2. Automatic seat back angle control for variable empty
seat
[0381] 15.3. Automatic window control for variable empty seat
[0382] 15.4. Etc. . . .
[0383] 16. Acquire equipment to build automated controls
[0384] 17. Build & install automated controls of vehicle
variables
[0385] 18. Install data collection aides
[0386] 18.1. Thermometers
[0387] 18.2. Seat track gauge
[0388] 18.3. Seat angle gauge
[0389] 18.4. Etc. . . .
[0390] 19. Install switched and fused wiring for:
[0391] 19.1. Transducer pairs
[0392] 19.2. Lasers
[0393] 19.3. Decision Indicator Lights
[0394] 19.4. System box
[0395] 19.5. Monitor
[0396] 19.6. Power automated control items
[0397] 19.7. Thermometers, potentiometers
[0398] 19.8. DOOP occupant ranging device
[0399] 19.9. DOOP ranging indicator
[0400] 19.10. Etc. . . .
[0401] 20. Write DOOP operating software for OPS system box
[0402] 21. Validate DOOP operating software for OPS
[0403] 22. Build OPS system control box for the vehicle with
special DOOP operating software
[0404] 23. Validate & document system control box
[0405] 24. Write vehicle specific DOOP data collection software
(pollbin)
[0406] 25. Write vehicle specific DOOP data evaluation program
(picgraph)
[0407] 26. Evaluate DOOP data collection software
[0408] 27. Evaluate DOOP data evaluation software
[0409] 28. Load DOOP data collection software on OPS system box and
validate
[0410] 29. Load DOOP data evaluation software on OPS system box and
validate
[0411] 30. Train technicians on DOOP data collection techniques and
use of data collection software
[0412] 31. Design prototype mounts based on known transducer
variables
[0413] 32. Prototype mounts
[0414] 33. Pre-build mounts
[0415] 33.1. Install transducers in mounts
[0416] 33.2. Optimize to eliminate crosstalk
[0417] 33.3. Obtain desired field
[0418] 33.4. Validate performance of DOOP requirements for
mounts
[0419] 34. Document mounts
[0420] 34.1. Polar plots of fields
[0421] 34.2. Drawings with all mount dimensions
[0422] 34.3. Drawings of transducer location in the mount
[0423] 35. Install mounts in the vehicle
[0424] 36. Map fields in the vehicle using ATI designed apparatus
and specification
[0425] 37. Map performance in the vehicle of the DOOP transducer
assembly
[0426] 38. Determine sensor volume
[0427] 39. Document vehicle mounted transducers and fields
[0428] 39.1. Mapping per ATI specification
[0429] 39.2. Photographs of all fields
[0430] 39.3. Drawing and dimensions of installed mounts
[0431] 39.4. Document sensor volume
[0432] 39.5. Drawing and dimensions of aim & field
[0433] 40. Using data collection software and OPS system box
collect initial 16 sheets of training, independent, and validation
data
[0434] 41. Determine initial conditions for training the ANN
[0435] 41.1. Normalization method
[0436] 41.2. Training via back propagation or ?
[0437] 41.3. Weights
[0438] 41.4. Etc. . . .
[0439] 42. Pre-process data
[0440] 43. Train an ANN on above data
[0441] 44. Develop post processing strategy if necessary
[0442] 45. Develop post processing software
[0443] 46. Evaluate ANN with validation data and in vehicle
analysis
[0444] 47. Perform sled tests to confirm initial DOOP results
[0445] 48. Document DOOP testing results and performance
[0446] 49. Rework mounts and repeat steps 31 through 48 if
necessary
[0447] 50. Meet with customer and review program
[0448] 51. Develop strategy for customer directed outputs
[0449] 51.1. Develop strategy for final ANN multiple decision
networks if necessary
[0450] 51.2. Develop strategy for final ANN multiple layer networks
if necessary
[0451] 51.3. Develop strategy for DOOP layer/network
[0452] 52. Design daily calibration jig
[0453] 53. Build daily calibration jig
[0454] 54. Develop daily calibration test
[0455] 55. Document daily calibration test procedure & jig
[0456] 56. Collect daily calibration tests
[0457] 57. Document daily calibration test results
[0458] 58. Rework vehicle data collection markings for customer
directed outputs
[0459] 58.1. Multiple zone identifiers for data collection
[0460] 59. Schedule subjects for all data sets
[0461] 60. Train subjects for data collection procedures
[0462] 61. Using DOOP data collection software and OPS system box
collect initial 16 sheets of training, independent, and validation
data
[0463] 62. Collect total amount of vectors deemed necessary by
program directives, amount will vary as outputs and complexity of
ANN varies
[0464] 63. Determine initial conditions for training the ANN
[0465] 63.1. Normalization method
[0466] 63.2. Training via back propagation or ?
[0467] 63.3. Weights
[0468] 63.4. Etc. . . .
[0469] 64. Pre-process data
[0470] 65. Train an ANN on above data
[0471] 66. Develop post processing strategy
[0472] 66.1. Weighting
[0473] 66.2. Averaging
[0474] 66.3. Etc. . . .
[0475] 67. Develop post processing software
[0476] 68. Evaluate ANN with validation data
[0477] 69. Perform in vehicle hole searching and analysis
[0478] 70. Perform in vehicle non sled mounted DOOP tests
[0479] 71. Determines need for further training or processing
[0480] 72. Repeat steps 58 through 71 if necessary
[0481] 73. Perform sled tests to confirm initial DOOP results
[0482] 74. Document DOOP testing results and performance
[0483] 75. Repeat steps 58 through 74 if necessary
[0484] 76. Write summary performance report
[0485] 77. Presentation of vehicle to the customer
[0486] 78. Delivered an OPS equipped vehicle to the customer
* * * * *