U.S. patent application number 14/547814 was filed with the patent office on 2016-05-19 for gps based learned control event prediction.
The applicant listed for this patent is Robert Bosch GmbH. Invention is credited to Kenneth Brochu, Hirak Chanda, Anthony Farrell, Troy McCormick, Ankit Shah.
Application Number | 20160137173 14/547814 |
Document ID | / |
Family ID | 54705872 |
Filed Date | 2016-05-19 |
United States Patent
Application |
20160137173 |
Kind Code |
A1 |
Shah; Ankit ; et
al. |
May 19, 2016 |
GPS BASED LEARNED CONTROL EVENT PREDICTION
Abstract
A global positioning signal based learned control event
prediction method and apparatus includes a learning auxiliary
module connected to a communication bus of a vehicle. The
arrangement stores events and event locations using global
positioning signals for a vehicle traveling along a path. When the
vehicle travels the same path a second time, the detected events
and event locations are determined. When the events match at the
same event locations, a predictive action is determined for a
future occurrence of the vehicle approaching the event location.
Thus, as the vehicle approaches the event location, the predictive
action, for example pre-filling of the vehicle brakes or
pre-tensioning of the seat belts occurs.
Inventors: |
Shah; Ankit; (Canton,
MI) ; McCormick; Troy; (Milford, MI) ;
Farrell; Anthony; (Clinton, MI) ; Chanda; Hirak;
(Troy, MI) ; Brochu; Kenneth; (Commerce,
MI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Robert Bosch GmbH |
Stuttgart |
|
DE |
|
|
Family ID: |
54705872 |
Appl. No.: |
14/547814 |
Filed: |
November 19, 2014 |
Current U.S.
Class: |
701/40 ; 701/45;
701/48; 701/71 |
Current CPC
Class: |
B60T 2201/04 20130101;
B60T 8/174 20130101; G01C 21/32 20130101; B60T 2201/16 20130101;
B60W 30/0956 20130101; B60W 2710/182 20130101; B60W 2756/10
20200201; B60G 2400/824 20130101; B60R 21/16 20130101; B60W 2556/50
20200201; B60W 2552/00 20200201; B60W 10/22 20130101; B60W 50/14
20130101; B60W 10/184 20130101; B60T 2201/122 20130101; B60W
2556/65 20200201; B60R 22/36 20130101; G01C 21/3697 20130101; B60W
2050/143 20130101; B60W 30/085 20130101; B60T 2201/06 20130101;
B60W 2710/22 20130101 |
International
Class: |
B60T 8/174 20060101
B60T008/174; B60R 21/16 20060101 B60R021/16; B60W 10/184 20060101
B60W010/184; B60W 10/22 20060101 B60W010/22; B60R 22/36 20060101
B60R022/36; B60W 50/14 20060101 B60W050/14 |
Claims
1. A method for identifying and predicting events for a vehicle
that routinely drives a known path comprising: determining
particular locations of the vehicle along the known path based on
global positioning signals; sensing events along the known path
with sensors on the vehicle; matching and storing the events for
particular locations of the vehicle as determined from the global
positioning signals; learning a particular event location from
repeated sensing of the same event at the same particular location;
determining and storing a predictive action for the vehicle based
on the events for the particular event location; and providing a
control signal to begin performance of the predictive action when
the vehicle is at least one of a certain time and predetermined
distance from the event location.
2. The method according to claim 1, wherein the predictive action
comprises at least one of: a) pre-filling of brakes, b)
pre-tensioning of seat belts, c) enabling a stability control to an
ON state when the stability control is initially in an OFF state,
d) providing a warning to a vehicle operator, e) selecting
thresholds and control parameters for a specific terrain, and f)
adjusting or modifying a threshold.
3. The method according to claim 1, including the step of receiving
wireless signals from nearby vehicles that include particular event
locations and corresponding predictive actions.
4. The method according to claim 3, including the step of
transmitting wireless signals to nearby vehicles that include
particular event locations and corresponding predictive
actions.
5. The method according to claim 1, wherein the particular event
location is a blind curve and the predictive action is providing a
warning to a vehicle operator before the particular event location
is approached.
6. The method according to claim 1, including the steps of: sensing
stationary objects near the vehicle with radar; and learning the
presence of stationary objects at a particular location from
repeatedly sensing the same stationary object at the same location,
wherein accuracy of the particular location of the vehicle along
the known path is based on the global positioning signals and the
presence of learned stationary objects.
7. The method according to claim 1, wherein the event comprises at
least one of an anti-lock brake system event, a traction control
event, a stability control event, an electronic brake force
distribution event, a hill hold control event, a hill descent
control event, a trailer sway control event, a banked curve event,
a wheel lift control event, a sharp turn event, an axle deflection
event and an off road terrain event.
8. The method according to claim 1, including determining and
storing a direction that the vehicle is traveling on the known
path, and providing the control signal to begin performance of the
predictive action when the vehicle is approaching the event
location in the stored direction.
9. A global positioning signal based learned control event
prediction system comprising a learning auxiliary module secured on
a vehicle, the learning auxiliary module configured to: receive
stability information signals from a stability control module and
for sending stability control signals to the stability control
module; receive engine information signals from an engine control
module and to transmit engine control signals to engine control
module; receive transmission information signals from a
transmission module and to transmit transmission control signals to
the transmission module; receive information signals from an
airbag/ORC control module and to transmit airbag/ORC control
signals to the airbag/ORC control module; and receive global
positioning signals from a global positioning signal module for
determining a location of the vehicle, wherein the learning
auxiliary module identifies significant events at particular
locations along a known path and when the same significant event
occurs multiple times at a particular location, the learning
auxiliary module stores the event location for the learned event,
the learned event, and determines and stores a predictive action
corresponding to the learned event in a memory of the learning
auxiliary module, and wherein as the vehicle approaches one of the
particular event locations, the learning auxiliary module provides
an output to cause a corresponding predictive action before the
vehicle is at the particular event location.
10. The prediction system according to claim 9, wherein the memory
storing the event location and storing the corresponding predictive
action comprises a non-volatile memory.
11. The prediction system according to claim 9, wherein the
predictive action comprises at least one of: a) pre-filling of
brakes, b) pre-tensioning of seat belts, c) enabling a stability
control to an ON state when the stability control is initially in
an OFF state, d) providing a warning to a vehicle operator, e)
selecting thresholds and control parameters for a specific terrain,
and f) adjusting or modifying a threshold.
12. The prediction system according to claim 9, wherein the
learning auxiliary module is configured to receive and transmit
wireless signals via a vehicle-to-vehicle transceiver module to
other vehicles, the wireless signals including information provided
from respective learning auxiliary modules.
13. The prediction system according to claim 9, wherein the event
comprises at least one of an anti-lock brake system event, a
traction control event, a stability control event, an electronic
brake force distribution event, a hill hold control event, a hill
descent control event, a trailer sway control event, a banked curve
event, a wheel lift control event, a sharp turn event, an axle
deflection event and an off road terrain event.
14. The prediction system according to claim 9, wherein the
learning auxiliary module is configured to determine and store a
direction that the vehicle is traveling on the known path, and to
provide the control signal to begin performance of the predictive
action when the vehicle is approaching the event location in the
stored direction.
15. A global positioning signal based learned control event
prediction system provided with a vehicle, comprising: a learning
auxiliary module; a stability control module for providing
stability information signals to the learning auxiliary module and
for receiving stability control signals from the learning auxiliary
module; an engine control module for providing engine information
signals to the learning auxiliary module and for receiving engine
control signals; a transmission module for providing transmission
information signals to the learning auxiliary module and for
receiving transmission control signals from the learning auxiliary
module; an airbag/ORC control module for providing information
signals to the learning auxiliary module and for receiving
airbag/ORC control signals from the learning auxiliary module; and
a global position signal module for receiving global positioning
signals and providing location signals to the learning auxiliary
module, wherein the learning auxiliary module stores significant
events at particular locations along a known path and when the same
significant event occurs multiple times, a learned event is stored
in a memory of the learning auxiliary module, and wherein the
learning auxiliary module determines and stores a predictive action
for the learned event at the event location.
16. The prediction system of claim 15, further comprising a
vehicle-to-vehicle transceiver module for receiving information
from the learning auxiliary module and transmitting the information
via wireless signals to the learning auxiliary modules of other
vehicles, the vehicle-to-vehicle transceiver module receiving
wireless signals with information from other vehicles and providing
the information to the learning auxiliary module.
17. The prediction system of claim 15, wherein the predictive
action comprises at least one of: a) pre-filling of brakes, b)
pre-tensioning of seat belts, c) enabling a stability control to an
ON state when the stability control is initially in an OFF state,
d) providing a warning to a vehicle operator, e) selecting
thresholds and control parameters for a specific terrain, and f)
adjusting or modifying a threshold.
18. The prediction system of claim 15, wherein the event comprises
at least one of an anti-lock brake system event, a traction control
event, a stability control event, an electronic brake force
distribution event, a hill hold control event, a hill descent
control event, a trailer sway control event, a banked curve event,
a wheel lift control event, a sharp turn event, an axle deflection
event and an off road terrain event.
19. The prediction system of claim 15, the system further
comprising an audio module, and wherein the particular event
location is a blind curve and the learning auxiliary module
provides a control signal to the audio module, wherein the audio
module performs the predictive action of providing warning to a
vehicle operator before the particular event location is
approached.
20. The prediction system of claim 15, wherein the learning
auxiliary module is configured to determine and store a direction
that the vehicle is traveling on the known path, and to provide the
control signal to begin performance of the predictive action when
the vehicle is approaching the event location in the stored
direction.
Description
BACKGROUND
[0001] The present invention relates to a method and apparatus for
predicting future events at an event location along a known path.
Events along the path are recorded and when the same event occurs
at the same event location, a predictive action occurs when the
vehicle is approaching the event location.
SUMMARY
[0002] In one embodiment, the invention provides a method for
identifying and predicting events for a vehicle that routinely
drives a known path that includes determining particular locations
of the vehicle along the known path based on global positioning
signals; sensing events along the known path with sensors on the
vehicle; matching and storing the events for particular locations
of the vehicle as determined from the global positioning signals;
learning a particular event location from repeated sensing of the
same event at the same particular location; determining and storing
a predictive action for the vehicle based on the events for the
particular event location; and providing a control signal to begin
performance of the predictive action when the vehicle is at least
one of a certain time and predetermined distance from the event
location.
[0003] In one embodiment, the predictive action comprises at least
one of: a) pre-filling of brakes, b) pre-tensioning of seat belts,
c) enabling a stability control to an ON state when the stability
control is initially in an OFF state, d) providing a warning to a
vehicle operator, e) selecting thresholds and control parameters
for a specific terrain, and f) adjusting or modifying a
threshold.
[0004] In another embodiment, a vehicle-to-vehicle transceiver
receives wireless signals from nearby vehicles that include
particular event locations and corresponding predictive actions.
Further, the vehicle-to-vehicle transceiver transmits wireless
signals that include particular event locations and corresponding
predictive actions determined by the learning auxiliary module.
[0005] In one embodiment, the particular event location is a blind
curve and the predictive action is providing a warning to a vehicle
operator before the particular event location is approached.
[0006] Another embodiment includes sensing stationary objects near
the vehicle with radar and learning the presence of stationary
objects at a particular location from repeatedly sensing the same
stationary object at the same location, wherein accuracy of the
particular location of the vehicle along the known path is based on
the global positioning signals and the presence of learned
stationary objects.
[0007] In one embodiment the event comprises at least one of an
anti-lock brake system event, a traction control event, a stability
control event, an electronic brake force distribution event, a hill
hold control event, a hill descent control event, a trailer sway
control event, a banked curve event, a wheel lift control event, a
sharp turn event, an axle deflection event and an off road terrain
event.
[0008] In another embodiment, the auxiliary module determines and
stores a direction that the vehicle is traveling on the known path,
and provides a control signal to begin performance of a predictive
action when the vehicle is approaching the event.
[0009] In another embodiment the invention provides a global
positioning signal based learned control event prediction system
comprising a learning auxiliary module secured on a vehicle, the
learning auxiliary module configured to: receive stability
information signals from a stability control module and for sending
stability control signals to the stability control module; receive
engine information signals from an engine control module and to
transmit engine control signals to engine control module; receive
transmission information signals from a transmission module and to
transmit transmission control signals to the transmission module;
receive information signals from an airbag/ORC control module and
to transmit airbag/ORC control signals to the airbag/ORC control
module; and receive global positioning signals from a global
positioning signal module for determining a location of the
vehicle, wherein the learning auxiliary module identifies
significant events at particular locations along a known path and
when the same significant event occurs multiple times at a
particular location, the learning auxiliary module stores the event
location for the learned event, the learned event, and determines
and stores a predictive action corresponding to the learned event
in a memory of the learning auxiliary module, and wherein as the
vehicle approaches one of the particular event locations, the
learning auxiliary module provides an output to cause a
corresponding predictive action before the vehicle is at the
particular event location.
[0010] In one embodiment, a global positioning signal based learned
control event prediction system provided with a vehicle, includes a
learning auxiliary module; a stability control module for providing
stability information signals to the learning auxiliary module and
for receiving stability control signals from the learning auxiliary
module; an engine control module for providing engine information
signals to the learning auxiliary module and for receiving engine
control signals; a transmission module for providing transmission
information signals to the learning auxiliary module and for
receiving transmission control signals from the learning auxiliary
module; an airbag/ORC control module for providing information
signals to the learning auxiliary module and for receiving
airbag/ORC control signals from the learning auxiliary module; and
a global position signal module for receiving global positioning
signals and providing location signals to the learning auxiliary
module, wherein the learning auxiliary module stores significant
events at particular locations along a known path and when the same
significant event occurs multiple times, a learned event is stored
in a memory of the learning auxiliary module, and wherein the
learning auxiliary module determines and stores a predictive action
for the learned event at the event location.
[0011] Other aspects of the invention will become apparent by
consideration of the detailed description and accompanying
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] FIG. 1 shows a block diagram of vehicle modules connected
via a communication bus.
[0013] FIG. 2 a vehicle-to-vehicle communication arrangement.
[0014] FIG. 3 shows a block diagram of a learning auxiliary
module.
[0015] FIG. 4 shows a flowchart representing operation of the
learned control event prediction system.
[0016] FIG. 5 shows a map of a known path for a vehicle.
DETAILED DESCRIPTION
[0017] Before any embodiments of the invention are explained in
detail, it is to be understood that the invention is not limited in
its application to the details of construction and the arrangement
of components set forth in the following description or illustrated
in the following drawings. The invention is capable of other
embodiments and of being practiced or of being carried out in
various ways.
[0018] FIG. 1 shows a block diagram of a GPS based learned control
event prediction system 8 that includes electronic control modules
connected via a network or communication bus 10 within a vehicle.
The control modules include an electronic stability control (ESC)
module 12, an engine control module 14, an air bag/occupant
restraint control (ORC) module 16 and a learning auxiliary module
20 connected to the communication bus 10. The vehicle also includes
a brake control module 22, an audio module 24, a vehicle-to-vehicle
transceiver module 26, a radar/video module 28, a global
positioning system (GPS) module 30 and a transmission module 32
connected to the communication bus 10. The learning auxiliary
module 20 and the other modules are secured on a vehicle.
[0019] The learning auxiliary module 20 is configured to receive
stability information signals from the ESC module 12 and to send
stability control signals to the ESC module 12 over the
communication bus 10. Further, the learning auxiliary module 20 is
configured to receive engine information signals from the engine
control module 14 and to transmit engine control signals to the
engine control module over the bus 10. The learning auxiliary
module 20 also receives transmission information signals from the
transmission module 32 and transmits transmission control signals
to the transmission module 32 over the bus 10. Further, the
learning auxiliary module 20 receives information signals from an
airbag/ORC control module and transmits airbag/ORC control signals
to the airbag/ORC control module over the bus 10. Finally, the
learning auxiliary module receives global positioning signals from
a global position signal module for determining a location of the
vehicle.
[0020] FIG. 2 illustrates the arrangement wherein
vehicle-to-vehicle transceiver modules 26 disposed in vehicles 34,
35 transmit wireless signals to each other and receive
corresponding wireless signals via antennas 36. The wireless
signals include information provided from respective learning
auxiliary modules 20 as discussed below. While vehicles 34, 35 are
illustrated, any type of conveyance is contemplated.
[0021] FIG. 3 shows the learning auxiliary module 20 including a
processor 37 and a memory 38. The memory 38 is a non-volatile
memory in some embodiments. The memory stores events, event
locations, predictive actions and additional information as
discussed below.
[0022] Operation
[0023] FIG. 4 shows a flowchart or program 40 that illustrates
operation of the GPS based learned control event prediction system
8. In a beginning step 44, a control or processor 37 of the
learning auxiliary module 20 receives global positioning signals
from GPS module 30 and determines location coordinates for the
vehicle 34 traveling along a known path or route. Further, a
sequence of GPS signals or other sensors determine a direction of
travel for the vehicle at step 44. Subsequently, the learning
auxiliary module 20 advances to decision step 46.
[0024] At decision step 46 shown in FIG. 4, the processor 37 of the
control module 20 identifies whether the vehicle is approaching an
event location by searching for a nearby event location in a
look-up virtual table stored in memory 38. One example of a lookup
virtual table with values is as follows.
TABLE-US-00001 Latitude Longitude Event Control ECU Module -119.47
39.02 ABS Event ESC -82.68 40.1 ESP Event ESC -110.34 45.09 TCS
Event ESC -164.03 10.45 Off Road Event ESC
[0025] The latitude and longitude define an event location stored
in memory 38, the type of event (ABS, ESP, TCS, Off Road)
previously was stored in the memory 38 for the location. Further,
the direction of travel is stored. The module in the above virtual
table, is the ESC module 12 in each instance. In other instances,
the brake control module 22 or the airbag/ORC module 16 is stored.
Any of the modules shown in FIG. 1 and additional modules (not
shown) are capable of being controlled by the learning auxiliary
module 20. A specific feature/predictive action to be controlled by
the ESC module 12 is also stored in the memory 38, although not
illustrated in the virtual table.
[0026] Each of the event locations, events and module shown in the
above table corresponds to a type of event that previously occurred
at least two times at the event location along a known path. Thus,
the event locations and the corresponding predictive actions are
provided for use in the global positioning signal based learned
control event prediction system 8.
[0027] Returning to the flowchart in FIG. 4, after approaching an
event location along a known path is determined by the processor
37, the processor advances to step 48. At step 48, the learning
auxiliary module 20 outputs control signals to cause one or more of
the other modules, such as the ESC module 12 taken from the above
virtual table, to perform one or more predictive actions before the
vehicle advances to the event location. More specifically, the
learning auxiliary module 20 provides a control signal so that the
selected control module begins performance of the predictive action
when the vehicle is traveling along the known path at least one of
a certain time and a predetermined distance from the corresponding
event location.
[0028] After performing the predictive action, the learning
auxiliary module 20 advances to decision step 50. Returning to
decision step 46, in the instance when the control module 20
determines the vehicle is not approaching an event location, the
processor advances to decision step 50.
[0029] At decision step 50 shown in FIG. 4, the learning auxiliary
module 20 determines whether a sensing event is occurring. If no
event is occurring, the processor returns to step 44. If a sensing
event is occurring, the processor 37 advances to step 54. At step
54, the processor stores the type of event and the event location
with the GPS coordinates. The term "event" is directed to a
significant event that has a significant outcome on the operation
of the vehicle, the comfort of the operator, and other purposes.
Not all detected events are recorded or stored. After step 54, the
learning auxiliary module 20 then advances to decision step 56.
[0030] At decision step 56, the processor determines whether the
event has occurred at the same location previously. If the event
matches an event at the same location, the processor 37 stores or
flags the learned event for the particular location of the vehicle
as determined from the GPS signals in memory 38. The particular
location is learned and stored as a particular learned event
location. If no matching occurs, the learning auxiliary module 20
returns to step 44 and repeats the process shown in the flowchart
40 of FIG. 4.
[0031] At decision step 56, when the same event has occurred
previously at the same location, the processor 37 advances to step
60. At step 60, the location is set as a predictive control event
at the event location. Further, a predictive action to occur in
view of the predictive control event, before a vehicle advances to
the event location is also determined and stored. Thereafter, the
processor returns to step 44. An event location corresponds to
longitude and latitude values as shown in the virtual table above.
Elevation may also be stored for an event location. Thereafter, the
processor 37 returns to step 44 and repeats the process shown in
the flowchart of FIG. 4.
[0032] While not shown in FIG. 4, in the event that vehicle 34 is
in vehicle-to-vehicle communication as shown in FIG. 2, the
flowchart shown in FIG. 4 would include the extra step of receiving
and storing event information, including the type of event and the
event location from another vehicle. The event information is
stored as shown in step 54 and compared to determine an event
location. In some embodiments, event locations and the information
shown in the above virtual table and discussed herein is provided
from the other vehicle. Finally, the host vehicle 34 wirelessly
transmits information regarding sensed events and set event
locations to another vehicle 35 in some embodiments.
[0033] Specific examples of events and locations that are measured,
determined and stored are as follows. Sensed active and passive
events include an anti-lock brake system (ABS) event, a traction
control event, a stability control event, for example for a banked
curve event or a change in elevation event, a hill hold control
event, a hill descent control event, an electronic brake force
distribution (EBD) event, a trailer sway control event for an
attached trailer, a wheel lift control event or sharp turn event,
an axle deflection event, an off road terrain event, the detection
of stationary objects and the detection of other vehicles.
[0034] The learning auxiliary module 20 provides outputs to other
modules to perform various predictive actions, including brake
pre-fill, seat belt pre-tensioning, turning on or off the ESC
module, provide an audible or visual warning, and finally choosing
or selecting thresholds and control parameters for a specific
terrain. For example, a transmission module may adjust or modify a
selected gear for an off road event location, depending on a
specific terrain of sand, mud or snow. Failsafe adjustments or
modifications to thresholds of other devices and modules are also
contemplated. In one embodiment, the learning auxiliary module 20
enables the ESC module 12 to an ON state when the stability control
is initially in an OFF state.
Examples
[0035] The radar/video module 28, which represents either a radar
module, or both of radar and a video sensing module, detects the
presence of stationary objects at specific locations near the
vehicle. The location and information including the exact location
of the stationary object are stored in the memory 38 by processor
37 of the learning auxiliary module 20. After the event of
detecting the stationary object has been detected at least two
times, the location is set as an event location. Thus, when the
vehicle approaches the event location in the future, the stationary
object is ignored for purposes of collision avoidance detection or
other purposes. Therefore, false collision alerts are prevented and
collision avoidance is improved.
[0036] In another instance, the beginning of driving a vehicle
off-road is detected. After detection, the predictive action
includes providing a control signal to the transmission module 32
to switch a transmission to a lower gear in view of the predicted
change in terrain. Therefore, vehicle operation is improved.
[0037] In another instance, the particular event location is a
blind curve and the predictive action is providing a control signal
to the audio module 24 to warn a vehicle operator before the
particular event location is approached.
[0038] In another instance, when the vehicle approaches a blind
intersection location, the learning auxiliary module 20 provides 1)
a control signal to the air bag/ORC module 16 to automatically
pre-tension the seatbelts, 2) a control signal to the brake control
module 22 to pre-fill the brakes, and 3) a control signal to the
audio module 24 to provide an audible warning.
[0039] The map 70 shown in FIG. 5 illustrates a known path or route
72 that a vehicle routinely drives to a destination 74. Along the
path three event locations where control events have occurred are
illustrated by arrows. As discussed above, before advancing to an
event location for a control event, the auxiliary module 20
performs predictive actions corresponding to the particular event
as discussed above.
[0040] Each of the modules shown in FIG. 1 may include a digital
processor, application specific integrated circuit (ASIC) or
another control circuit, along with various sensors in some
instances.
[0041] In some embodiments, accuracy of the particular location of
the vehicle along the known path is based on the global positioning
signals, and further based on the presence of learned stationary
objects.
[0042] While the learning auxiliary module 20 is illustrated as a
separate module, in some embodiments the functions thereof can be
combined with a processor of a different one of the modules shown
in FIG. 1. In this manner, modules may perform multiple
operations.
[0043] Thus, the invention provides, among other things, an
approach for enhancing driving experience of a vehicle 34
travelling along known or commonly traveled paths. Various features
and advantages of the invention are set forth in the following
claims.
* * * * *