U.S. patent application number 16/775772 was filed with the patent office on 2021-07-29 for systems and methods for compensating for driver speed-tracking error.
The applicant listed for this patent is Toyota Motor Engineering & Manufacturing North America, Inc.. Invention is credited to Kyungtae Han, Prashant Tiwari, Ziran Wang.
Application Number | 20210233400 16/775772 |
Document ID | / |
Family ID | 1000004666057 |
Filed Date | 2021-07-29 |
United States Patent
Application |
20210233400 |
Kind Code |
A1 |
Wang; Ziran ; et
al. |
July 29, 2021 |
SYSTEMS AND METHODS FOR COMPENSATING FOR DRIVER SPEED-TRACKING
ERROR
Abstract
Systems and methods for compensating for driver speed-tracking
error are disclosed herein. One embodiment computes a recommended
speed for a vehicle; classifies a driver of the vehicle as a
particular type of driver among a plurality of driver types based
on measured speed-tracking error, wherein the speed-tracking error
is a difference between the recommended speed and an actual speed
of the vehicle; predicts the speed-tracking error at a future time
increment based on the speed-tracking error at one or more past
time increments using a nonlinear autoregressive (NAR) neural
network associated with the particular type of driver; computes a
compensated recommended speed for the vehicle based on the
recommended speed and the predicted speed-tracking error at the
future time increment; and communicates the compensated recommended
speed to the driver.
Inventors: |
Wang; Ziran; (Sunnyvale,
CA) ; Han; Kyungtae; (Palo Alto, CA) ; Tiwari;
Prashant; (Santa Clara, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Toyota Motor Engineering & Manufacturing North America,
Inc. |
Plano |
TX |
US |
|
|
Family ID: |
1000004666057 |
Appl. No.: |
16/775772 |
Filed: |
January 29, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G08G 1/096775 20130101;
G08G 1/052 20130101; G06N 3/04 20130101; G06N 3/08 20130101 |
International
Class: |
G08G 1/0967 20060101
G08G001/0967; G06N 3/04 20060101 G06N003/04; G08G 1/052 20060101
G08G001/052; G06N 3/08 20060101 G06N003/08 |
Claims
1. A system for compensating for driver speed-tracking error, the
system comprising: one or more processors; and a memory
communicably coupled to the one or more processors and storing: a
motion control module including instructions that when executed by
the one or more processors cause the one or more processors to
compute a recommended speed for a vehicle; a driver-classification
module including instructions that when executed by the one or more
processors cause the one or more processors to classify a driver of
the vehicle as a particular type of driver among a plurality of
driver types based on measured speed-tracking error, wherein the
speed-tracking error is a difference between the recommended speed
and an actual speed of the vehicle; a speed-tracking-error
prediction and compensation module including instructions that when
executed by the one or more processors cause the one or more
processors to: predict the speed-tracking error at a future time
increment based on the speed-tracking error at one or more past
time increments using a nonlinear autoregressive (NAR) neural
network associated with the particular type of driver; and compute
a compensated recommended speed for the vehicle based on the
recommended speed and the predicted speed-tracking error at the
future time increment; and a human-machine interface (HMI) module
including instructions that when executed by the one or more
processors cause the one or more processors to communicate the
compensated recommended speed to the driver.
2. The system of claim 1, further comprising a training module
including instructions that when executed by the one or more
processors cause the one or more processors to: cluster historical
speed-tracking-error data from a plurality of drivers into the
plurality of driver types; identify one or more principal variables
in the clustered historical speed-tracking-error data; and train,
for each driver type in the plurality of driver types, a
corresponding NAR neural network using the clustered historical
speed-tracking-error data and the one or more principal
variables.
3. The system of claim 1, wherein the plurality of driver types
correspond to varying degrees of accuracy in tracking a recommended
speed.
4. The system of claim 1, wherein the motion control module
includes instructions to compute the recommended speed for the
vehicle based, at least in part, on information received from one
or more of a traffic information server and another vehicle.
5. The system of claim 1, wherein the instructions in the HMI
module to communicate the compensated recommended speed to the
driver include instructions to display the compensated recommended
speed.
6. The system of claim 5, wherein the HMI module includes further
instructions to display the actual speed of the vehicle.
7. The system of claim 1, wherein the instructions in the HMI
module include instructions to communicate the compensated
recommended speed to the driver audibly.
8. A non-transitory computer-readable medium for compensating for
driver speed-tracking error and storing instructions that when
executed by one or more processors cause the one or more processors
to: compute a recommended speed for a vehicle; classify a driver of
the vehicle as a particular type of driver among a plurality of
driver types based on measured speed-tracking error, wherein the
speed-tracking error is a difference between the recommended speed
and an actual speed of the vehicle; predict the speed-tracking
error at a future time increment based on the speed-tracking error
at one or more past time increments using a nonlinear
autoregressive (NAR) neural network associated with the particular
type of driver; compute a compensated recommended speed for the
vehicle based on the recommended speed and the predicted
speed-tracking error at the future time increment; and communicate
the compensated recommended speed to the driver.
9. The non-transitory computer-readable medium of claim 8, wherein
the instructions include further instructions that when executed by
one or more processors cause the one or more processors to: cluster
historical speed-tracking-error data from a plurality of drivers
into the plurality of driver types; identify one or more principal
variables in the clustered historical speed-tracking-error data;
and train, for each driver type in the plurality of driver types, a
corresponding NAR neural network using the clustered historical
speed-tracking-error data and the one or more principal
variables.
10. The non-transitory computer-readable medium of claim 8, wherein
the plurality of driver types correspond to varying degrees of
accuracy in tracking a recommended speed.
11. The non-transitory computer-readable medium of claim 8, wherein
the instructions include instructions to compute the recommended
speed for the vehicle based, at least in part, on information
received from one or more of a traffic information server and
another vehicle.
12. The non-transitory computer-readable medium of claim 8, wherein
the instructions to communicate the compensated recommended speed
to the driver include instructions to display the compensated
recommended speed.
13. The non-transitory computer-readable medium of claim 12,
wherein the instructions include further instructions to display
the actual speed of the vehicle.
14. A method of compensating for driver speed-tracking error, the
method comprising: computing a recommended speed for a vehicle;
classifying a driver of the vehicle as a particular type of driver
among a plurality of driver types based on measured speed-tracking
error, wherein the speed-tracking error is a difference between the
recommended speed and an actual speed of the vehicle; predicting
the speed-tracking error at a future time increment based on the
speed-tracking error at one or more past time increments using a
nonlinear autoregressive (NAR) neural network associated with the
particular type of driver; computing a compensated recommended
speed for the vehicle based on the recommended speed and the
predicted speed-tracking error at the future time increment; and
communicating the compensated recommended speed to the driver.
15. The method of claim 14, further comprising: clustering
historical speed-tracking-error data from a plurality of drivers
into the plurality of driver types; identifying one or more
principal variables in the clustered historical
speed-tracking-error data; and training, for each driver type in
the plurality of driver types, a corresponding NAR neural network
using the clustered historical speed-tracking-error data and the
one or more principal variables.
16. The method of claim 14, wherein the plurality of driver types
correspond to varying degrees of accuracy in tracking a recommended
speed.
17. The method of claim 14, wherein computing the recommended speed
for the vehicle is based, at least in part, on information received
from one or more of a traffic information server and another
vehicle.
18. The method of claim 14, wherein communicating the compensated
recommended speed to the driver includes displaying the compensated
recommended speed.
19. The method of claim 18, further comprising displaying the
actual speed of the vehicle.
20. The method of claim 14, wherein communicating the compensated
recommended speed to the driver is performed audibly.
Description
TECHNICAL FIELD
[0001] The subject matter described herein generally relates to
vehicles and, more particularly, to systems and methods for
compensating for driver speed-tracking error.
BACKGROUND
[0002] For a variety of reasons, including promoting smoother flow
of traffic and fuel-efficient eco-driving, applications arise in
which a driver is provided with a recommended speed. Regardless of
how diligently a driver strives to track (follow) the recommended
speed, at least some speed-tracking error inevitably results. For
example, the driver may overshoot or undershoot the recommended
speed at least some of the time. This speed-tracking error
diminishes the benefits that would otherwise be gained by providing
the driver with a recommended speed.
SUMMARY
[0003] An example of a system for compensating for driver
speed-tracking error is presented herein. The system comprises one
or more processors and a memory communicably coupled to the one or
more processors. The memory stores a motion control module
including instructions that when executed by the one or more
processors cause the one or more processors to compute a
recommended speed for a vehicle. The memory also stores a
driver-classification module including instructions that when
executed by the one or more processors cause the one or more
processors to classify a driver of the vehicle as a particular type
of driver among a plurality of driver types based on measured
speed-tracking error, wherein the speed-tracking error is a
difference between the recommended speed and an actual speed of the
vehicle. The memory also stores a speed-tracking-error prediction
and compensation module including instructions that when executed
by the one or more processors cause the one or more processors to
predict the speed-tracking error at a future time increment based
on the speed-tracking error at one or more past time increments
using a nonlinear autoregressive (NAR) neural network associated
with the particular type of driver. The speed-tracking-error
prediction and compensation module also includes instructions that
when executed by the one or more processors cause the one or more
processors to compute a compensated recommended speed for the
vehicle based on the recommended speed and the predicted
speed-tracking error at the future time increment. The memory also
stores a human-machine interface (HMI) module including
instructions that when executed by the one or more processors cause
the one or more processors to communicate the compensated
recommended speed to the driver.
[0004] Another embodiment is a non-transitory computer-readable
medium for compensating for driver speed-tracking error and storing
instructions that when executed by one or more processors cause the
one or more processors to compute a recommended speed for a
vehicle. The instructions also cause the one or more processors to
classify a driver of the vehicle as a particular type of driver
among a plurality of driver types based on measured speed-tracking
error, wherein the speed-tracking error is a difference between the
recommended speed and an actual speed of the vehicle. The
instructions also cause the one or more processors to predict the
speed-tracking error at a future time increment based on the
speed-tracking error at one or more past time increments using a
nonlinear autoregressive (NAR) neural network associated with the
particular type of driver. The instructions also cause the one or
more processors to compute a compensated recommended speed for the
vehicle based on the recommended speed and the predicted
speed-tracking error at the future time increment. The instructions
also cause the one or more processors to communicate the
compensated recommended speed to the driver.
[0005] Another embodiment is a method of compensating for driver
speed-tracking error. The method includes computing a recommended
speed for a vehicle. The method also includes classifying a driver
of the vehicle as a particular type of driver among a plurality of
driver types based on measured speed-tracking error, wherein the
speed-tracking error is a difference between the recommended speed
and an actual speed of the vehicle. The method also includes
predicting the speed-tracking error at a future time increment
based on the speed-tracking error at one or more past time
increments using a nonlinear autoregressive (NAR) neural network
associated with the particular type of driver. The method also
includes computing a compensated recommended speed for the vehicle
based on the recommended speed and the predicted speed-tracking
error at the future time increment. The method also includes
communicating the compensated recommended speed to the driver.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] So that the manner in which the above-recited features of
the present disclosure can be understood in detail, a more
particular description of the disclosure, briefly summarized above,
may be had by reference to the implementations, some of which are
illustrated in the appended drawings. It is to be noted, however,
that the appended drawings illustrate only possible implementations
of this disclosure and are therefore not to be considered limiting
of its scope. The disclosure may admit to other
implementations.
[0007] FIG. 1 illustrates one embodiment of a vehicle within which
systems and methods disclosed herein may be implemented.
[0008] FIG. 2 illustrates one embodiment of a speed recommendation
system.
[0009] FIG. 3A is a block diagram of a training phase of a speed
recommendation system, in accordance with an illustrative
embodiment of the invention.
[0010] FIG. 3B is a block diagram of a learning phase of a speed
recommendation system, in accordance with an illustrative
embodiment of the invention.
[0011] FIG. 3C is a block diagram of a calculation phase of a speed
recommendation system, in accordance with an illustrative
embodiment of the invention.
[0012] FIG. 4 illustrates an example of compensating for driver
speed-tracking error, in accordance with an illustrative embodiment
of the invention.
[0013] FIG. 5 is a flowchart of a method of compensating for driver
speed-tracking error, in accordance with an illustrative embodiment
of the invention.
[0014] To facilitate understanding, identical reference numerals
have been used, wherever possible, to designate identical elements
that are common to the figures. Additionally, elements of one or
more embodiments may be advantageously adapted for utilization in
other embodiments described herein.
DETAILED DESCRIPTION
[0015] In various embodiments described herein, a
machine-learning-based approach is used to model driver behavior in
tracking a recommended speed, and the speed-tracking error is
predicted and compensated for in real time for a particular driver
in a customized way.
[0016] In some embodiments, in a training phase, a system that
compensates for driver speed-tracking error clusters historical
speed-tracking-error data from a plurality of drivers into a
plurality of driver types (e.g., based on how accurately the
drivers track a recommended speed). The system can identify one or
more principal variables in the clustered historical
speed-tracking-error data and train, for each driver type in the
plurality of driver types, a corresponding NAR neural network using
the clustered historical speed-tracking-error data and the one or
more identified principal variables. In some embodiments, a
different type of neural network may be employed.
[0017] In some embodiments, in learning and calculation phases, the
system computes a recommended speed for a vehicle. The system also
classifies a driver of the vehicle as a particular type of driver
among the plurality of driver types mentioned above based on the
driver's measured speed-tracking error. In this context, the
speed-tracking error is the difference between the recommended
speed and the actual speed of the vehicle. The system predicts the
speed-tracking error at a future time increment based on the
speed-tracking error at one or more past time increments using a
nonlinear autoregressive (NAR) neural network (or other neural
network) associated with the particular type of driver into which
the driver of the vehicle has been classified. The system can then
compute a compensated recommended speed for the vehicle based on
the recommended speed and the predicted speed-tracking error at the
future time increment. The system communicates this compensated
recommended speed to the driver. In some embodiments, the
compensated recommended speed is communicated to the driver via a
visual display (e.g., a display that shows the vehicle's actual
speed and the compensated recommended speed side by side). In other
embodiments, the compensated recommended speed can be communicated
to the driver audibly (e.g., "Recommended speed is 38 miles per
hour") via an in-vehicle sound system. In still other embodiments,
the compensated recommended speed is communicated to the driver
both visually and audibly.
[0018] In some embodiments, the system computes the recommended
speed for the vehicle based, at least in part, on information the
system receives from other vehicles via vehicle-to-vehicle (V2V)
communication, from a traffic information server, or both.
[0019] Referring to FIG. 1, an example of a vehicle 100, in which
systems and methods disclosed herein can be implemented, is
illustrated. The vehicle 100 can include a speed recommendation
system 170 or components and/or modules thereof. As used herein, a
"vehicle" is any form of motorized transport. In one or more
implementations, the vehicle 100 can be an automobile. In some
implementations, the vehicle 100 may be any other form of motorized
transport. The vehicle 100 can include the speed recommendation
system 170 or capabilities to support or interact with the speed
recommendation system 170 and thus benefits from the functionality
discussed herein. While arrangements will be described herein with
respect to automobiles, it will be understood that implementations
are not limited to automobiles. Instead, implementations of the
principles discussed herein can be applied to any kind of vehicle,
as discussed above. Instances of vehicle 100, as used herein, are
equally applicable to any device capable of incorporating the
systems or methods described herein.
[0020] The vehicle 100 also includes various elements. It will be
understood that, in various implementations, it may not be
necessary for the vehicle 100 to have all of the elements shown in
FIG. 1. The vehicle 100 can have any combination of the various
elements shown in FIG. 1. Further, the vehicle 100 can have
additional elements to those shown in FIG. 1. In some arrangements,
the vehicle 100 may be implemented without one or more of the
elements shown in FIG. 1, including speed recommendation system
170. While the various elements are shown as being located within
the vehicle 100 in FIG. 1, it will be understood that one or more
of these elements can be located external to the vehicle 100 or be
part of a system that is separate from vehicle 100. Further, the
elements shown may be physically separated by large distances.
[0021] As shown in FIG. 1, vehicle 100 may communicate with one or
more other connected vehicles 180 via a network 190. Also, in some
embodiments, as shown in FIG. 1, vehicle 100 may communicate with
other network nodes 185 such as users' mobile devices, cloud
servers (e.g., traffic-information servers), and roadside units
(RSUs) or other infrastructure such as traffic signals via network
190.
[0022] In FIG. 1, network 190 represents any of a variety of wired
and wireless networks. For example, in communicating directly with
another vehicle, sometimes referred to as vehicle-to-vehicle (V2V)
communication, vehicle 100 can employ a technology such as
dedicated short-range communication (DSRC) or Bluetooth Low Energy
(BLE). In communicating with a user's mobile device (e.g., a
smartphone) or a remote server, vehicle 100 can use a technology
such as cellular data. In some embodiments, network 190 includes
the Internet.
[0023] Some of the possible elements of the vehicle 100 are shown
in FIG. 1 and will be described in connection with subsequent
figures. However, a description of many of the elements in FIG. 1
will be provided after the discussion of FIGS. 2-5 for purposes of
brevity of this description. Additionally, it will be appreciated
that for simplicity and clarity of illustration, where appropriate,
reference numerals have been repeated among the different figures
to indicate corresponding or analogous elements. In addition, the
discussion outlines numerous specific details to provide a thorough
understanding of the embodiments described herein. Those skilled in
the art, however, will understand that the embodiments described
herein may be practiced using various combinations of these
elements.
[0024] The speed recommendation system 170 is shown as including
one or more processors 110 from the vehicle 100 of FIG. 1.
Accordingly, the one or more processors 110 may be a part of the
speed recommendation system 170, the speed recommendation system
170 may include a separate processor from the one or more
processors 110 of the vehicle 100, or the speed recommendation
system 170 may access the one or more processors 110 through a data
bus or another communication path. In one embodiment, the speed
recommendation system 170 includes a memory 210 that stores a
motion control module 220, a driver-classification module 230, a
speed-tracking-error prediction and compensation module 240, a
human-machine interface (HMI) module 250, and a training module
260. The memory 210 is a random-access memory (RAM), read-only
memory (ROM), a hard-disk drive, a flash memory, or other suitable
memory for storing the modules 220, 230, 240, 250, and 260. The
modules 220, 230, 240, 250, and 260 are, for example,
computer-readable instructions that when executed by the one or
more processors 110, cause the one or more processors 110 to
perform the various functions disclosed herein. In performing
functions such training a set of nonlinear autoregressive (NAR)
neural networks and various computational tasks, the modules of
speed recommendation system 170 may store historical
speed-tracking-error data 280 and various kinds of model data 290
in a database 270.
[0025] As shown in FIG. 2, speed recommendation system 170 can
communicate with one or more connected vehicles 180 and with other
network nodes 185 via network 190. In some embodiments, speed
recommendation system 170 communicates with one or more connected
vehicles 180 via V2V connections, as discussed above. In some
embodiments, speed recommendation system 170 communicates with one
or more other network nodes 185 (e.g., users' mobile devices,
traffic-information servers, RSUs, other infrastructure systems,
etc.) via a cellular-data connection, as discussed above. In some
embodiments, speed recommendation system 170 may communicate with
other systems or subsystems of vehicle 100 such as sensor system
120 and communication system 130.
[0026] The description of speed recommendation system 170 that
follows is divided into three sections corresponding to (1) a
training phase in which a set of NAR neural networks are trained
using historical speed-tracking-error data 280, (2) a learning
phase in which a driver of a vehicle 100 is classified, in real
time, as a particular type of driver--one of n driver types, and
(3) a calculation phase in which the speed-tracking error at a
future time increment is predicted and a compensated recommended
speed is calculated and communicated to the driver. These three
phases are diagrammed in, respectively, FIGS. 3A, 3B, and 3C.
[0027] Referring to FIG. 2, training module 260 generally includes
instructions that cause the one or more processors 110 to cluster
historical speed-tracking-error data 280 from a plurality of
drivers into a plurality of driver types, identify one or more
principal variables in the clustered historical
speed-tracking-error data, and train, for each driver type in the
plurality of driver types, a corresponding NAR neural network using
the clustered historical speed-tracking-error data and the one or
more principal variables. In some embodiments, a different type of
neural network may be employed. These functions are discussed in
greater detail in connection with FIG. 3A.
[0028] FIG. 3A is a block diagram of a training phase of speed
recommendation system 170, in accordance with an illustrative
embodiment of the invention. In FIG. 3A, training module 260
performs a clustering process 305 on historical
speed-tracking-error data 280 gathered from a plurality of drivers.
In some embodiments, training module 260 employs hierarchical
cluster analysis (HCA) in clustering the historical
speed-tracking-error data 280. In other embodiments, a different
clustering algorithm is employed. During clustering process 305,
training module 260 clusters the historical speed-tracking-error
data 280 into n driver types 310(1), 310(2), . . . 310(n). In one
embodiment, the plurality of driver types 310(1)-(n) correspond to
varying degrees of accuracy in tracking a recommended speed. For
example, in one embodiment, Driver Type 1 (driver type 310(1) in
FIG. 3A) corresponds to a type of driver who tracks a recommended
speed the most accurately among the population of sampled drivers.
The other driver types correspond to drivers who track a
recommended speed less accurately, with Driver Type n (driver type
310(n) in FIG. 3A) being a type of driver who tracks a recommended
speed the least accurately among the population of sampled drivers.
The numbering scheme for the various driver types is arbitrary,
however, and can differ, depending on the embodiment.
[0029] Referring to Element 315 in FIG. 3A, training module 260
also includes instructions that cause the one or more processors
110 to identify one or more principal variables in the clustered
historical speed-tracking-error data. In one embodiment, training
module 260 uses a technique known as principal component analysis
(PCA) to identify the one or more principal variables. In other
embodiments, a different variable-identification technique can be
employed. The one or more identified principal variables are fed to
a process (Element 320 in FIG. 3) in which training module 260
trains n different NAR neural networks, each NAR neural network
325(i), i=1, 2, . . . , n, corresponding to a specific one of the n
driver types 310(1)-(n). In other words, NAR neural network 325(1)
corresponds to driver type 310(1), NAR neural network 325(2)
corresponds to driver type 310(2), etc., and NAR neural network
325(n) corresponds to driver type 310(n). The trained neural
networks 325(1)-(n) are deployed in the learning and calculation
phases that are described further below. As mentioned above, in
some embodiments, a different type of neural network may be
employed.
[0030] In some embodiments, training module 260 is not part of
speed recommendation system 170 but is, instead, part of a separate
off-line training system that is separate from vehicle 100. For
example, such a separate off-line training system might be
associated with a research center or vehicle manufacturer's
research and development operations.
[0031] Referring again to FIG. 2, driver-classification module 230
generally includes instructions that cause the one or more
processors 110 to classify a driver of a vehicle 100 as a
particular type of driver among a plurality of driver types (see
driver types 310(1)-(n) in FIG. 3A) based on the driver's measured
driver speed-tracking error 330. In one embodiment, the
speed-tracking error is defined as the difference (S.sub.R-S.sub.A)
between the recommended speed and an actual speed of vehicle 100,
where S.sub.R denotes the recommended speed, and S.sub.A denotes
the actual speed, of vehicle 100. These functions are discussed in
greater detail in connection with FIG. 3B.
[0032] FIG. 3B is a block diagram of a learning phase of a speed
recommendation system 170, in accordance with an illustrative
embodiment of the invention. Though this phase of speed
recommendation system 170 is herein referred to as a "learning"
phase, it is distinct from the "training" phase described above in
that it coincides with real-time deployment, in a vehicle 100, of
the speed-tracking-error-compensation aspects of speed
recommendation system 170. Therefore, it may be termed an "on-line
learning phase" (as opposed to an "off-line training phase"). In
FIG. 3B, driver-classification module 230 classifies the driver of
a vehicle 100 based on the driver speed-tracking error 330 over a
predetermined time horizon (Element 335 in FIG. 3B). The result is
a classification of the driver as one of the n driver types,
310(1)-(n), discussed above in connection with FIG. 3A (driver-type
classification 340 in FIG. 3B). For example, in one embodiment,
driver-classification module 230 performs k-NN (k-nearest-neighbor)
classification over a time horizon of a few time increments' worth
of driver speed-tracking error data 330. The duration of a time
increment can vary, depending on the embodiment, (e.g., 0.1 s, 0.5
s, 1 s, etc.). In some embodiments, the driver-type classification
340 is updated every five minutes or another predetermined
interval. That is, a particular driver can be classified
differently over time depending on how the driver's speed-tracking
error 330 profile changes. A driver's speed-tracking error 330 can
change due to factors such as fatigue, level of attentiveness,
traffic density, environmental conditions (condition of the
roadway, weather, time of day, etc.), and the driver's
mood/emotions.
[0033] In some embodiments, a driver's driver-type classification
340 can be saved for later use by speed recommendation system 170.
In some embodiments, speed recommendation system 170 determines the
identity of the driver of vehicle 100 through facial images,
biometrics, identifying an associated mobile device, or other
techniques and automatically loads into memory 210 a profile for
that particular driver that includes the driver's usual or default
driver-type classification 340 as a starting point.
[0034] Speed-tracking-error prediction and compensation module 240
(discussed more fully below), based on the driver's driver-type
classification 340 output by driver-classification module 230,
selects the NAR neural network 325(i) corresponding to the driver
type 310(i) into which the driver of vehicle 100 has been
classified. The driver speed-tracking error 330 is routed to the
selected NAR neural network 325(i) for as long as the corresponding
driver type 310(i) remains in effect (e.g., until the driver's
speed-tracking-error classification is updated to a different
driver type 310). The output of the selected NAR neural network
325(i) is discussed below in connection with the calculation phase
of speed recommendation system 170.
[0035] Referring again to FIG. 2, speed-tracking-error prediction
and compensation module 240 generally includes instructions that
cause the one or more processors 110 to predict the speed-tracking
error at a future time increment based on the speed-tracking error
at one or more past time increments using the NAR neural network
325(i) associated with the driver type 310(i) into which the driver
of vehicle 100 has been classified (refer to Element 335 in FIG.
3B). Speed-tracking-error prediction and compensation module 240
also includes instructions to compute a compensated recommended
speed for the vehicle based on the recommended speed S.sub.R and
the predicted speed-tracking error at the future time increment.
This is part of the calculation phase of speed recommendation
system 170 discussed below in connection with FIG. 3C.
[0036] Motion control module 220 generally includes instructions
that cause the one or more processors 110 to compute a recommended
speed for a vehicle. In some embodiments, motion control module 220
relies on sensor data 119 from sensor system 120 (images, LIDAR,
radar, etc.) to compute the recommended speed. In other
embodiments, motion control module 220 includes instructions to
compute the recommended speed for the vehicle 100 based, at least
in part, on information received from one or more
traffic-information servers, one or more other vehicles (e.g., via
V2V communication), or both. The functions performed by motion
control module 220 and speed-tracking-error prediction and
compensation module 240 are discussed further below in connection
with FIG. 3C.
[0037] FIG. 3C is a block diagram of a calculation phase of speed
recommendation system 170, in accordance with an illustrative
embodiment of the invention. This phase may also be termed an
"on-line calculation phase," since speed recommendation system 170,
in this phase, can predict and compensate for speed-tracking error
in real time. In FIG. 3C, a motion control algorithm 370 associated
with motion control module 220 computes a recommended speed 360, as
discussed above. Motion control module 220 obtains the actual speed
350 of vehicle 100 from, for example, the Controller Area Network
(CAN bus) of vehicle 100 and inputs the actual speed 350 to the
motion control algorithm 370. Speed-tracking-error prediction and
compensation module 240 subtracts the actual speed 350 from the
recommended speed 360 to obtain the driver speed-tracking error
330. Speed-tracking-error prediction and compensation module 240
inputs the driver speed-tracking error 330 to the NAR neural
network 325(i) corresponding to the driver type 310(i) into which
the driver of vehicle 100 has been classified, as discussed above
in connection with FIG. 3B. The NAR neural network 325(i) outputs a
predicted speed-tracking error 375. In the embodiment shown in FIG.
3C, speed-tracking-error prediction and compensation module 240
adds the predicted speed-tracking error 375 to the recommended
speed 360 to compute the compensated recommended speed 380.
[0038] Referring again to FIG. 2, HMI module 250 generally includes
instructions that cause the one or more processors 110 to
communicate the compensated recommended speed 380 (see FIG. 3C) to
the driver of a vehicle 100. Communicating the compensated
recommended speed 380 to the driver can take different forms,
depending on the particular embodiment. In one embodiment, HMI
module 250 outputs the compensated recommended speed 380 to a
display device 133 in communication system 130. In other
embodiments, HMI module 250 outputs the compensated recommended
speed 380 to a mobile communication device (e.g., a smartphone)
associated with the driver or other occupant of vehicle 100 (see
other network nodes 185 in FIGS. 1 and 2). For example, in some
embodiments, the driver mounts a smartphone on the dashboard of
vehicle 100, and HMI module 250 communicates with the smartphone
via Bluetooth or another type of network connection to output the
compensated recommended speed 380, which is frequently updated
(re-calculated) by speed-tracking-error prediction and compensation
module 240 in real time (refer to FIG. 3C). In some embodiments,
HMI module 250 also includes instructions to display the actual
speed 350 of vehicle 100 (e.g., next to the compensated recommended
speed 380).
[0039] In other embodiments, HMI module 250 communicates the
compensated recommended speed 380 to the driver audibly. For
example, HMI module 250 can interface with audio device(s) 134 of
communication system 130 to output pre-recorded or
computer-synthesized speech such as, "Recommended speed is 41 miles
per hour." In some embodiments, audible indication of the
compensated recommended speed 380 is combined with a visual
indication on a display device 133.
[0040] FIG. 4 illustrates an example of compensating for driver
speed-tracking error 330, in accordance with an illustrative
embodiment of the invention. The left side of FIG. 4, for contrast,
illustrates the case of no compensation for driver speed-tracking
error 330. In this case, the actual speed 350 (35 mph) and the
recommended speed 360 (33 mph, labeled "Target Speed" in FIG. 4)
are shown on display device 133. The driver speed-tracking error
330 (-2.5, -2.2, -3.0, -3.2, and -2.8 mph) at the past
discrete-time increments 17, 18, 19, 20, and 21 seconds,
respectively, is input to the NAR neural network 325(i)
corresponding to the driver type 310(i) into which the driver has
been classified by driver-classification module 230. Note that, in
accordance with the definition of speed-tracking error 330, as
discussed above and as shown in FIG. 3C, the past speed-tracking
error values are negative, in this example. Discrete time
increments of one second are shown in FIG. 4 for simplicity. In
some embodiments, the discrete time increments are smaller than one
second.
[0041] The right side of FIG. 4 illustrates the case of
compensation by speed-tracking-error prediction and compensation
module 240, as discussed above. In this example, the NAR neural
network 325(i) predicts a driver speed-tracking error 330 of -3 mph
at discrete-time increment 22 seconds (the next discrete-time
increment). This represents an overshoot of 3 mph by the driver.
Speed-tracking-error prediction and compensation module 240
computes a compensated recommended speed 380 of 30 mph by adding
the predicted speed-tracking error 375 (-3 mph) to the recommended
speed 360 (33 mph). When the driver, who is currently tending to
overshoot by 3.0 mph attempts to track the compensated recommended
speed 380 of 30 mph, vehicle 100 will end up traveling at a speed
at or close to the recommended speed 360 of 33 mph. Over time,
vehicle 100 will travel at a speed that more closely tracks the
recommended speed 360 due to the frequently updated compensation
described herein. Note that, in the example of FIG. 4, the
recommended speed 360 remains 33 mph and is stored internally by
speed recommendation system 170, but that value is not communicated
to the driver. Instead, the compensated recommended speed 380 (30
mph) is communicated to the driver of vehicle 100.
[0042] FIG. 5 is a flowchart of a method 500 of compensating for
driver speed-tracking error 330, in accordance with an illustrative
embodiment of the invention. Method 500 will be discussed from the
perspective of the speed recommendation system 170 shown in FIG. 2
with further reference to FIGS. 3A-3C. While method 500 is
discussed in combination with speed recommendation system 170, it
should be appreciated that method 500 is not limited to being
implemented within speed recommendation system 170, but speed
recommendation system 170 is instead one example of a system that
may implement method 500.
[0043] At block 510, motion control module 220 computes a
recommended speed 360 for a vehicle 100. As discussed above, in
some embodiments, motion control module 220 relies on sensor data
119 from sensor system 120 (images, LIDAR, radar, etc.) to compute
the recommended speed. In other embodiments, motion control module
220 includes instructions to compute the recommended speed for the
vehicle 100 based, at least in part, on information received from
one or more traffic-information servers, one or more other vehicles
(e.g., via V2V communication), or both.
[0044] At block 520, driver-classification module 230 classifies
the driver of vehicle 100 as a particular type of driver 310(i)
among a plurality of driver types, 310(1), 310(2), . . . , 310(n),
based on the driver's measured speed-tracking error 330. As
discussed above, in some embodiments, the speed-tracking error is
the difference (S.sub.R-S.sub.A) between the recommended speed and
the actual speed of the vehicle 100. As also discussed above, in
one embodiment, the plurality of driver types 310(1)-(n) correspond
to varying degrees of accuracy in tracking a recommended speed.
[0045] At block 530, speed-tracking-error prediction and
compensation module 240 predicts the driver speed-tracking error
330 at a future time increment based on the speed-tracking error
330 at one or more past time increments using a NAR neural network
325(i) associated with the particular type of driver 310(i) into
which the driver has been classified by driver-classification
module 230.
[0046] At block 540, speed-tracking-error prediction and
compensation module 240 computes a compensated recommended speed
380 for the vehicle 100 based on the recommended speed 360 and the
predicted speed-tracking error 375 at the future time increment. As
discussed above, in one embodiment, the compensated recommended
speed 380 is computed by adding the recommended speed 360 to the
predicted speed-tracking error 375.
[0047] At block 550, HMI module 250 communicates the compensated
recommended speed 380 to the driver of vehicle 100. As discussed
above, HMI module 250 can communicate the compensated recommended
speed 380 to the driver via a display device 133 that is integrated
with vehicle 100 or via a vehicle occupant's mobile device (e.g., a
smartphone). In other embodiments, HMI module 250 communicates the
compensated recommended speed 380 to the driver audibly. In still
other embodiments, HMI module 250 communicates compensated
recommended speed 380 to the driver both visually and audibly. As
mentioned above, in some embodiments, HMI module 250 also
communicates (e.g., displays) the actual speed 350 of the vehicle
100 to the driver, in addition to the compensated recommended speed
380.
[0048] In some embodiments, method 500 includes additional actions
(not shown in FIG. 5) that are part of the training phase of speed
recommendation system 170 discussed above. In the training phase,
training module 260 clusters historical speed-tracking-error data
280 from a plurality of drivers into a plurality of driver types
310(1), 310(2), . . . 310(n). In some embodiments, training module
260 performs clustering by employing hierarchical cluster analysis
(HCA). Training module 260 also identifies one or more principal
variables in the clustered historical speed-tracking-error data. In
some embodiments, training module 260 employs principal component
analysis (PCA) to identify the one or more principal variables.
Training module 260 also trains, for each driver type 310(i) in the
plurality of driver types, 310(1), 310(2), . . ., 310(n), a
corresponding NAR neural network 325(i) using the clustered
historical speed-tracking-error data and the one or more identified
principal variables. As discussed above, in some embodiments, a
different type of neural network may be employed.
[0049] FIG. 1 will now be discussed in full detail as an example
vehicle environment within which the systems and methods disclosed
herein may be implemented. The vehicle 100 can include one or more
processors 110. In one or more arrangements, the one or more
processors 110 can be a main processor of the vehicle 100. For
instance, the one or more processors 110 can be an electronic
control unit (ECU). The vehicle 100 can include one or more data
stores 115 for storing one or more types of data. The data store(s)
115 can include volatile and/or non-volatile memory. Examples of
suitable data stores 115 include RAM, flash memory, ROM, PROM
(Programmable Read-Only Memory), EPROM, EEPROM (Electrically
Erasable Programmable Read-Only Memory), registers, magnetic disks,
optical disks, hard drives, or any other suitable storage medium,
or any combination thereof. The data store(s) 115 can be a
component(s) of the one or more processors 110, or the data
store(s) 115 can be operatively connected to the one or more
processors 110 for use thereby. The term "operatively connected,"
as used throughout this description, can include direct or indirect
connections, including connections without direct physical
contact.
[0050] In one or more arrangements, the one or more data stores 115
can include map data 116. The map data 116 can include maps of one
or more geographic areas. In some instances, the map data 116 can
include information or data on roads, traffic control devices, road
markings, structures, features, and/or landmarks in the one or more
geographic areas. In one or more arrangement, the map data 116 can
include one or more terrain maps 117. The terrain map(s) 117 can
include information about the ground, terrain, roads, surfaces,
and/or other features of one or more geographic areas. In one or
more arrangement, the map data 116 can include one or more static
obstacle maps 118. The static obstacle map(s) 118 can include
information about one or more static obstacles located within one
or more geographic areas.
[0051] The one or more data stores 115 can include sensor data 119.
In this context, "sensor data" means any information about the
sensors that a vehicle is equipped with, including the capabilities
and other information about such sensors. As will be explained
below, the vehicle 100 can include the sensor system 120. The
sensor data 119 can relate to one or more sensors of the sensor
system 120. As an example, in one or more arrangements, the sensor
data 119 can include information on one or more LIDAR sensors 124
of the sensor system 120. As discussed above, in some embodiments,
vehicle 100 can receive sensor data from other connected vehicles,
from devices associated with other road users (ORUs), or both.
[0052] As noted above, the vehicle 100 can include the sensor
system 120. The sensor system 120 can include one or more sensors.
"Sensor" means any device, component and/or system that can detect,
and/or sense something. The one or more sensors can be configured
to detect, and/or sense in real-time. As used herein, the term
"real-time" means a level of processing responsiveness that a user
or system senses as sufficiently immediate for a particular process
or determination to be made, or that enables the processor to keep
up with some external process.
[0053] In arrangements in which the sensor system 120 includes a
plurality of sensors, the sensors can function independently from
each other. Alternatively, two or more of the sensors can work in
combination with each other. In such a case, the two or more
sensors can form a sensor network. The sensor system 120 and/or the
one or more sensors can be operatively connected to the one or more
processors 110, the data store(s) 115, and/or another element of
the vehicle 100 (including any of the elements shown in FIG.
1).
[0054] The sensor system 120 can include any suitable type of
sensor. Various examples of different types of sensors will be
described herein. However, it will be understood that the
implementations are not limited to the particular sensors
described. The sensor system 120 can include one or more vehicle
sensors 121. The vehicle sensors 121 can detect, determine, and/or
sense information about the vehicle 100 itself, including the
operational status of various vehicle components and systems.
[0055] In one or more arrangements, the vehicle sensors 121 can be
configured to detect, and/or sense position and/orientation changes
of the vehicle 100, such as, for example, based on inertial
acceleration. In one or more arrangements, the vehicle sensors 121
can include one or more accelerometers, one or more gyroscopes, an
inertial measurement unit (IMU), a dead-reckoning system, a global
navigation satellite system (GNSS), a navigation system 147, and/or
other suitable sensors. The vehicle sensors 121 can be configured
to detect, and/or sense one or more characteristics of the vehicle
100. In one or more arrangements, the vehicle sensors 121 can
include a speedometer to determine a current speed of the vehicle
100.
[0056] Alternatively, or in addition, the sensor system 120 can
include one or more environment sensors 122 configured to acquire,
and/or sense driving environment data. "Driving environment data"
includes any data or information about the external environment in
which a vehicle is located or one or more portions thereof. For
example, the one or more environment sensors 122 can be configured
to detect, quantify, and/or sense obstacles in at least a portion
of the external environment of the vehicle 100 and/or
information/data about such obstacles. The one or more environment
sensors 122 can be configured to detect, measure, quantify, and/or
sense other things in at least a portion the external environment
of the vehicle 100, such as, for example, nearby vehicles, lane
markers, signs, traffic lights, traffic signs, lane lines,
crosswalks, curbs proximate the vehicle 100, off-road objects,
etc.
[0057] Various examples of sensors of the sensor system 120 will be
described herein. The example sensors may be part of the one or
more environment sensors 122 and/or the one or more vehicle sensors
121. Moreover, the sensor system 120 can include operator sensors
that function to track or otherwise monitor aspects related to the
driver/operator of the vehicle 100. However, it will be understood
that the implementations are not limited to the particular sensors
described. As an example, in one or more arrangements, the sensor
system 120 can include one or more radar sensors 123, one or more
LIDAR sensors 124, one or more sonar sensors 125, and/or one or
more cameras 126.
[0058] The vehicle 100 can further include a communication system
130. The communication system 130 can include one or more
components configured to facilitate communication between the
vehicle 100 and one or more communication sources. Communication
sources, as used herein, refers to people or devices with which the
vehicle 100 can communicate with, such as external networks,
computing devices, operator or occupants of the vehicle 100, or
others. As part of the communication system 130, the vehicle 100
can include an input system 131. An "input system" includes any
device, component, system, element or arrangement or groups thereof
that enable information/data to be entered into a machine. In one
or more examples, the input system 131 can receive an input from a
vehicle occupant (e.g., a driver or a passenger). The vehicle 100
can include an output system 132. An "output system" includes any
device, component, or arrangement or groups thereof that enable
information/data to be presented to the one or more communication
sources (e.g., a person, a vehicle passenger, etc.). The
communication system 130 can further include specific elements
which are part of or can interact with the input system 131 or the
output system 132, such as one or more display device(s) 133, and
one or more audio device(s) 134 (e.g., speakers and
microphones).
[0059] The vehicle 100 can include one or more vehicle systems 140.
Various examples of the one or more vehicle systems 140 are shown
in FIG. 1. However, the vehicle 100 can include more, fewer, or
different vehicle systems. It should be appreciated that although
particular vehicle systems are separately defined, each or any of
the systems or portions thereof may be otherwise combined or
segregated via hardware and/or software within the vehicle 100. The
vehicle 100 can include a propulsion system 141, a braking system
142, a steering system 143, throttle system 144, a transmission
system 145, a signaling system 146, and/or a navigation system 147.
Each of these systems can include one or more devices, components,
and/or combinations thereof, now known or later developed.
[0060] The one or more processors 110 can be operatively connected
to communicate with the various vehicle systems 140 and/or
individual components thereof. For example, returning to FIG. 1,
the one or more processors 110 can be in communication to send
and/or receive information from the various vehicle systems 140 to
control the movement, speed, maneuvering, heading, direction, etc.
of the vehicle 100. The one or more processors 110 may control some
or all of these vehicle systems 140.
[0061] The vehicle 100 can include one or more modules, at least
some of which are described herein. The modules can be implemented
as computer-readable program code that, when executed by a
processor 110, implement one or more of the various processes
described herein. The processor 110 can be a device, such as a CPU,
which is capable of receiving and executing one or more threads of
instructions for the purpose of performing a task. One or more of
the modules can be a component of the one or more processors 110,
or one or more of the modules can be executed on and/or distributed
among other processing systems to which the one or more processors
110 is operatively connected. The modules can include instructions
(e.g., program logic) executable by one or more processors 110.
Alternatively, or in addition, one or more data store 115 may
contain such instructions.
[0062] In one or more arrangements, one or more of the modules
described herein can include artificial or computational
intelligence elements, e.g., neural network, fuzzy logic or other
machine learning algorithms. Further, in one or more arrangements,
one or more of the modules can be distributed among a plurality of
the modules described herein. In one or more arrangements, two or
more of the modules described herein can be combined into a single
module.
[0063] Detailed implementations are disclosed herein. However, it
is to be understood that the disclosed implementations are intended
only as examples. Therefore, specific structural and functional
details disclosed herein are not to be interpreted as limiting, but
merely as a basis for the claims and as a representative basis for
teaching one skilled in the art to variously employ the aspects
herein in virtually any appropriately detailed structure. Further,
the terms and phrases used herein are not intended to be limiting
but rather to provide an understandable description of possible
implementations. Various implementations are shown in FIGS. 1-5,
but the implementations are not limited to the illustrated
structure or application.
[0064] The flowcharts and block diagrams in the figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various implementations. In this regard, each block in
the flowcharts or block diagrams can represent a module, segment,
or portion of code, which comprises one or more executable
instructions for implementing the specified logical function(s). It
should also be noted that, in some alternative implementations, the
functions noted in the block can occur out of the order noted in
the figures. For example, two blocks shown in succession can be
executed substantially concurrently, or the blocks can sometimes be
executed in the reverse order, depending upon the functionality
involved.
[0065] The systems, components and/or methods described above can
be realized in hardware or a combination of hardware and software
and can be realized in a centralized fashion in one processing
system or in a distributed fashion where different elements are
spread across several interconnected processing systems. Any kind
of processing system or other apparatus adapted for carrying out
the methods described herein is suited. A typical combination of
hardware and software can be a processing system with
computer-usable program code that, when being loaded and executed,
controls the processing system such that it carries out the methods
described herein. The systems, components and/or methods also can
be embedded in a computer-readable storage, such as a computer
program product or other data programs storage device, readable by
a machine, tangibly embodying a program of instructions executable
by the machine to perform methods and methods described herein.
These elements also can be embedded in an application product which
comprises all the features enabling the implementation of the
methods described herein and, which when loaded in a processing
system, is able to carry out these methods.
[0066] Furthermore, arrangements described herein can take the form
of a computer program product embodied in one or more
computer-readable media having computer-readable program code
embodied or embedded, such as stored thereon. Any combination of
one or more computer-readable media can be utilized. The
computer-readable medium can be a computer-readable signal medium
or a computer-readable storage medium. The phrase
"computer-readable storage medium" means a non-transitory storage
medium. A computer-readable storage medium can be, for example, but
not limited to, an electronic, magnetic, optical, electromagnetic,
infrared, or semiconductor system, apparatus, or device, or any
suitable combination of the foregoing. More specific examples (a
non-exhaustive list) of the computer-readable storage medium would
include the following: an electrical connection having one or more
wires, a portable computer diskette, a hard disk drive (HDD), a
solid state drive (SSD), a RAM, a ROM, an EPROM or Flash memory, an
optical fiber, a portable compact disc read-only memory (CD-ROM), a
digital versatile disc (DVD), an optical storage device, a magnetic
storage device, or any suitable combination of the foregoing. In
the context of this document, a computer-readable storage medium
can be any tangible medium that can contain, or store a program for
use by, or in connection with, an instruction execution system,
apparatus, or device.
[0067] Program code embodied on a computer-readable medium can be
transmitted using any appropriate medium, including but not limited
to wireless, wireline, optical fiber, cable, RF, etc., or any
suitable combination of the foregoing. Computer program code for
carrying out operations for aspects of the present arrangements can
be written in any combination of one or more programming languages,
including an object-oriented programming language such as Java.TM.
Smalltalk, C++ or the like and conventional procedural programming
languages, such as the "C" programming language or similar
programming languages. The program code can execute entirely on the
user's computer, partly on the user's computer, as a stand-alone
software package, partly on the user's computer and partly on a
remote computer, or entirely on the remote computer or server. In
the latter scenario, the remote computer can be connected to the
user's computer through any type of network, including a LAN or a
WAN, or the connection can be made to an external computer (for
example, through the Internet using an Internet Service
Provider).
[0068] In the description above, certain specific details are
outlined in order to provide a thorough understanding of various
implementations. However, one skilled in the art will understand
that the invention may be practiced without these details. In other
instances, well-known structures have not been shown or described
in detail to avoid unnecessarily obscuring descriptions of the
implementations. Unless the context requires otherwise, throughout
the specification and claims which follow, the word "comprise" and
variations thereof, such as, "comprises" and "comprising" are to be
construed in an open, inclusive sense, that is, as "including, but
not limited to." Further, headings provided herein are for
convenience only and do not interpret the scope or meaning of the
claimed invention.
[0069] Reference throughout this specification to "one or more
implementations" or "an implementation" means that a particular
feature, structure or characteristic described in connection with
the implementation is included in at least one or more
implementations. Thus, the appearances of the phrases "in one or
more implementations" or "in an implementation" in various places
throughout this specification are not necessarily all referring to
the same implementation. Furthermore, the particular features,
structures, or characteristics may be combined in any suitable
manner in one or more implementations. Also, as used in this
specification and the appended claims, the singular forms "a,"
"an," and "the" include plural referents unless the content clearly
dictates otherwise. It should also be noted that the term "or" is
generally employed in its sense including "and/or" unless the
content clearly dictates otherwise.
[0070] The headings (such as "Background" and "Summary") and
sub-headings used herein are intended only for general organization
of topics within the present disclosure and are not intended to
limit the disclosure of the technology or any aspect thereof. The
recitation of multiple implementations having stated features is
not intended to exclude other implementations having additional
features, or other implementations incorporating different
combinations of the stated features. As used herein, the terms
"comprise" and "include" and their variants are intended to be
non-limiting, such that recitation of items in succession or a list
is not to the exclusion of other like items that may also be useful
in the devices and methods of this technology. Similarly, the terms
"can" and "may" and their variants are intended to be non-limiting,
such that recitation that an implementation can or may comprise
certain elements or features does not exclude other implementations
of the present technology that do not contain those elements or
features.
[0071] The broad teachings of the present disclosure can be
implemented in a variety of forms. Therefore, while this disclosure
includes particular examples, the true scope of the disclosure
should not be so limited since other modifications will become
apparent to the skilled practitioner upon a study of the
specification and the following claims. Reference herein to one
aspect, or various aspects means that a particular feature,
structure, or characteristic described in connection with an
implementation or particular system is included in at least one or
more implementations or aspect. The appearances of the phrase "in
one aspect" (or variations thereof) are not necessarily referring
to the same aspect or implementation. It should also be understood
that the various method steps discussed herein do not have to be
carried out in the same order as depicted, and not each method step
is required in each aspect or implementation.
[0072] Generally, "module," as used herein, includes routines,
programs, objects, components, data structures, and so on that
perform particular tasks or implement particular data types. In
further aspects, a memory generally stores the noted modules. The
memory associated with a module may be a buffer or cache embedded
within a processor, a RAM, a ROM, a flash memory, or another
suitable electronic storage medium. In still further aspects, a
module as envisioned by the present disclosure is implemented as an
application-specific integrated circuit (ASIC), a hardware
component of a system on a chip (SoC), as a programmable logic
array (PLA), or as another suitable hardware component that is
embedded with a defined configuration set (e.g., instructions) for
performing the disclosed functions.
[0073] The terms "a" and "an," as used herein, are defined as one
as or more than one. The term "plurality," as used herein, is
defined as two or more than two. The term "another," as used
herein, is defined as at least a second or more. The terms
"including" and/or "having," as used herein, are defined as
including (i.e., open language). The phrase "at least one of . . .
and . . . . " as used herein refers to and encompasses any and all
possible combinations of one or more of the associated listed
items. As an example, the phrase "at least one of A, B and C"
includes A only, B only, C only, or any combination thereof (e.g.,
AB, AC, BC or ABC).
[0074] The preceding description of the implementations has been
provided for purposes of illustration and description. It is not
intended to be exhaustive or to limit the disclosure. Individual
elements or features of a particular implementation are generally
not limited to that particular implementation, but, where
applicable, are interchangeable and can be used in a selected
implementation, even if not specifically shown or described. The
same may also be varied in many ways. Such variations should not be
regarded as a departure from the disclosure, and all such
modifications are intended to be included within the scope of the
disclosure.
[0075] While the preceding is directed to implementations of the
disclosed devices, systems, and methods, other and further
implementations of the disclosed devices, systems, and methods can
be devised without departing from the basic scope thereof. The
scope thereof is determined by the claims that follow.
* * * * *