SYSTEM AMD METHOD FOR OPTIMIZING FUEL ECONOMY USING
PREDICTIVE ENVIRONMENT AND DRIVER BEHAVIOR INFORMATION
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. provisional application Serial No.
61/366,322, filed on July 21, 2010, and U.S. provisional application Serial No. 61/391,229,
filed on October 8, 2010, the disclosures of which are incorporated herein by reference in
their entirety.
BACKGROUND
[0002] Driver bias may cause significant variations in the fuel economy of a vehicle.
For example, studies conducted by the U.S. Environmental Protection Agency (EPA) have
shown that, even when holding the vehicle and the route constant, driver behavior may
account for up to 35% variation in fuel economy (FE).
[0003] Driver assistance systems for optimizing fuel economy may be categorized into
two general groups based on their mechanisms of influence. Passive assistance systems
include those where only advisory feedback is provided to the driver. In active assistance
systems, system automation takes over some portion of vehicle control. Some active
automation technologies work relatively independent of the driver, such as adaptive cruise
control, and some work in conjunction with the driver, such as an automated/automatic
transmission.
[0004] Passive driver assistance schemes for fuel economy have been around for some
time. Many of the original equipment manufacturers (OEMs) offer some type of driver
interface for fuel economy in at least some of their vehicle models. The commercial vehicle
market, on the other hand, is typically dominated by aftermarket devices, which are often
coupled with telematic solutions. In recent years, nomadic devices have also emerged,
boosted by smart phone technology, but their effectiveness may be hindered due to limited
access to the vehicle's data bus and limited capability as a driver interface.
[0005] Existing fuel-economy interface devices may provide visual feedback of
instantaneous or average fuel economy. Some may also provide a fuel efficiency "score" to
the driver. The existing interface devices, however, are not noted for providing actionable
information to the driver in terms of which behavior is related to poor fuel economy or how to
operate the vehicle in a more fuel-efficient manner. In addition, existing fuel economy driver
feedback schemes are typically based on vehicle operations rather than the driver's interaction
with the vehicle operating environment. For example, an aggressive driver braking hard
while tailgating may be rated the same as a conservative driver being forced to brake hard
because another vehicle suddenly cut in front of him, as both will lead to the same hard
braking event and the same fuel economy assessment. As a result, the fuel economy score or
rating given to the driver is often confusing and misleading, which makes it difficult for both
the driver to make use of the data.
[0006] Currently there is a general lack of actionable information and a poor
differentiation between driver-caused and environment-caused fuel economy inefficiency.
Both of these factors limit the effectiveness of existing fuel-economy driver interface
technologies. In addition, since the driver's response to any passive feedback is typically
going to be slow and coarse by nature, passive feedback may become distracting and
ineffective in situations that require fast, frequent, or high-accuracy response from the driver.
[0007] As for active feedback technologies, examples include synthetic brake pulse or
acceleration resistance, road speed governor, automated/automatic transmission, and standard
or adaptive cruise control, to name a few. Some of these technologies were not intentionally
introduced for the purpose of improving fuel efficiency, but have advanced in recent years to
assist fuel economy determinations. For instance, current automated/automatic transmissions
may have the capability of estimating road grade and load and adjusting gearshift schedule for
better fuel economy.
[0008] Existing active feedback devices, however, have limitations similar to passive
devices. Many existing active feedback solutions do not have the intelligence to differentiate
between driver-caused and environment-caused fuel economy inefficiency. In addition, many
of the control strategies employed in active assistance solutions are based only on
instantaneous information, such as the load-based gearshift scheduling of many newer
automated/ automatic transmissions .
[0009] Achieving optimum driver behavior and powertrain operation to maximize fuel
efficiency depends on the ability to estimate future conditions of the driver, vehicle and
environment. Lack of knowledge of or inappropriate response to such future conditions may
limit the effectiveness of vehicle automation systems. In recent years, progress in intelligent
transportation systems (ITS) has increased availability of road and traffic information at both
vehicle level and fleet level. Since fuel consumption is a cumulative measure over time, the
optimality of instantaneous driver behavior is significantly dependent on future conditions.
The availability of predictive road and traffic information from modern ITS technologies,
such as global positioning systems (GPS), digital maps and radars, make it feasible and
affordable to reduce driver bias through enhanced driver feedback and/or powertrain
automation. Most of the early solutions have been focused on utilizing predictive
topographical information to reduce fuel consumption in a cruise control context, where the
vehicle is switching between full autonomy and full driver control per driver's choice. Since
the fuel-efficient control strategy will only be active in the full autonomy mode, accurate,
predictive sensor information is generally required to enable the system to make appropriate
decisions consistently to avoid frequent driver intervention. With the help of technologies
such as GPS and digital maps, reasonably accuracy information is readily available for simple
and largely static environments, such as long distance travel on an open freeway. For the
more complex urban environment, where future traffic conditions may be fluid and
unpredictable, sensor and decision errors are inevitable and the driver is more likely to simply
take over control of the vehicle, which makes cruise-control based solutions less effective.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1 is a schematic illustration o an automotive vehicle employing an
exemplary active driver assistance system (ADAS);
[0011] FIG. 2 is a more detailed schematic illustration of the ADAS of FIG. 1;
[0012] FIG. 3 is a schematic illustration of an exemplary look-ahead powertrain
management module (LPM) that may be employed with the ADAS;
[0013] FIG. 4 is a schematic illustration of an exemplary sensor fusion module that may
be employed with the LPM;
[0014] FIG. 5 schematically illustrates operation of an exemplary scenario recognition
module that may be employed with the LPM;
[0015] FIG. 6 illustrates an exemplary method for determining a vehicle speed profile
for maximizing fuel economy;
[0016] FIG. 7 is a schematic illustration of an exemplary arbitration module that may
be employed with the LPM; and
[0017] FIG. 8 schematically illustrates an exemplary operating scheme that may be
employed with the arbitration module.
DETAILED DESCRIPTION
[0018] Referring now to the discussion that follows and the drawings, illustrative
approaches to the disclosed systems and methods are described in detail. Although the
drawings represent some possible approaches, the drawings are not necessarily to scale and
certain features may be exaggerated, removed, or partially sectioned to better illustrate and
explain the present disclosure. Further, the descriptions set forth herein are not intended to be
exhaustive, otherwise limit, or restrict the claims to the precise forms and configurations
shown in the drawings and disclosed in the following detailed description.
[0019] FIG. 1 schematically illustrates a motor vehicle 20 employing an exemplary
active driver assistance system (ADAS) 22 for optimizing vehicle fuel economy. For
purposes of discussion, the features and operation of ADAS 22 are described primarily in
connection with a goal of maximizing fuel economy, but it shall be appreciated that the
features of ADAS 22 may also be utilized to improve other performance characteristics in
addition to fuel economy, for example, minimizing vehicle wear and tear and travel time. An
exemplary arrangement of ADAS 22 is illustrated in more detail in Figure 2. More
specifically, FIG. 2 schematically illustrates the exemplary arrangement of the various
elements of ADAS 22. As noted above, the disclosed arrangement is merely to facilitate
discussion and the arrangement is not limiting. Moreover, certain elements may be added or
removed without compromising the relevant operation of ADAS 22.
[0020] ADAS 22 may be configured to interact with a vehicle driver and utilize driver
input when formulating a strategy for maximizing fuel economy. ADAS 22 generally
operates in the background to continuously formulate and update vehicle operating strategies
to maximize fuel economy and evaluate whether and how to communicate with or influence
the driver's operation of the vehicle, through either active transmission/engine control and/or
advisory driver feedback. ADAS 22 will not, however, request more power from the vehicle
engine than requested by the driver. ADAS 22 may utilize historical and predictive
information of both the vehicle operating environment (i.e., route and traffic information), as
well as how the vehicle is being operated by the driver. The information may be obtained
from on-board vehicle sensors, digital maps, vehicle-to-vehicle (V2V) communication
systems, and vehicle-to-infrastructure (V2I) communication systems.
[0021] The environment factors effecting fuel economy may be separated from driver
behaviors factors, with the environment factors being categorized into individual driving
scenarios. Driver assistance decisions may be based on real-time scenario recognition and
scenario-specific strategies that target non-safety related driving behavior and which may
significantly effect fuel economy. Although ADAS 22, as described herein in an exemplary
maimer, is particularly applicable to commercial vehicles having an automated/automatic
transmission, it may also be employed with other vehicle configurations as well, including
semi-automatic and manual shift transmissions.
[0022] Continuing to refer to FIG. 1, vehicle 20 may include four rear drive wheels 24
and two front non-drive wheels 26. In other illustrative configurations, all wheels may be
drive wheels. Moreover, there may be more or fewer wheels for vehicle 20. Operably
associated with each of the wheels 24 and 26 may be a conventional type of wheel brake 28.
[0023] Vehicle 20 includes a vehicle drive system, generally designated 30. Vehicle
drive system 30 includes a vehicle engine 32 and a transmission 34. Transmission 34
operatively connects to engine 32 and transmits drive torque generated by engine 32 to rear
drive wheels 24. Extending rearward from the transmission 34, and also forming a portion of
vehicle drive system 30, is a drive-line, generally designated 36. A drive shaft 38
interconnects transmission 34 to an inter-wheel differential 40 for transferring drive torque to
a left rear axle shaft 42 and right rear axle shaft 44. Drive-line 36 is illustrated and described
as including shafts 38, 42 and 44 primarily to facilitate understanding of the overall vehicle
drive system 30, and not by way of limitation. For example, in the illustrated vehicle 20, and
by way of example only, drive shaft 38 is illustrated as including a single shaft, but in practice
may be configured to include additional shafts connected by way of universal joints or other
suitable coupling mechanisms.
[0024] Vehicle 20 may include a transmission/engine control unit (TECU) 46 for
controlling operation of engine 32 and transmission 34. TECU 46 may receive signals
generated by various engine, transmission and vehicle sensors, and process the signals to
control operation of engine 32 and transmission 34. Although illustrated as a separate
component from transmission 34, TECU 46 may also be integrated with transmission 34.
Moreover, TECU 46 may be a single computing module or a plurality of computing modules
at different locations within vehicle 20 and communicatively connected as discussed in more
detail below.
[0025] Referring to FIGS. 1 and 2, ADAS 22 may include an on-board look-ahead
powertrain management module (LPM) 48 that may provide primary control of the ADAS
system. LPM 48 may form a portion of the more general system based TECU 46, or may be
in operational communication with TECU 46. LPM 48 may include, for example, a
microprocessor, a central processing unit (CPU), and a digital controller, among others.
[0026] LPM 48 may employ a mix of passive and active driver assistance system
architecture aimed at improving vehicle fuel economy by assisting the driver to optimize a
vehicle drive-cycle with respect to fuel economy without compromising safety, operating
comfortableness, or the productivity of the vehicle operator. Fuel economy optimization may
be achieved through a combination of engine and transmission shift and drive torque controls
and actionable driver feedback. When employed with commercial vehicle fleets, LPM 48
may also provide commercial fleet managers with actionable driver fuel economy
performance information. Depending on the requirements of a particular application, LPM 48
may be fully integrated with TECU 46, or configured as a slave module of TECU 46.
[0027] With continued reference to FIGS. 1 and 2, LPM 48 may be configured to
communicate with an externally located back-office route/driver database (BORD) 50 for
storing various information used in connection with ADAS 22. BORD 50 may include a
route database 52 configured to store data on a particular route that vehicle 20 operates, as
well as a compilation of historical real-time traffic information occurring over the route.
BORD 50 may also be configured store three-dimensional coordinates of a road network that
vehicle 20 will be operating on, and the corresponding traffic constraints, such as speed
limits, traffic lights, stop signs, and real-time traffic information. The real-time traffic
information may be provided by a third-party service. The information stored on BORD 50
may include high-fidelity, third-party digital maps, or may be derived from aggregated route
data received from LPM 48 over time. In the case of the latter, BORD 50 may also include a
route learning module that compiles a knowledge base of the road network over time by
combining route information learned from vehicle 20 and other vehicles through their daily
operations.
[0028] BORD 50 may also maintain an operator driving behavior database 54 for each
driver of vehicle 20, which may be updated over time using data received from LPM 48.
Driving performance data with respect to individual operating scenarios may be downloaded
and stored on BORD 50. BORD 50 may also include a driver fuel economy performance
evaluation module 58 configured to evaluate each driver's fuel economy under various
operating scenarios.
[0029] Although illustrated as a component separate from vehicle 20, BORD 50 may be
configured as a simplified on-board sub-module of LPM 48.
[0030] A data-link interface unit 58 selectively establishes a wired 60 and/or wireless
62 communication link between LPM 48 and BORD 50. For purposes of discussion, wireless
communication links are indicated throughout the drawing figures by a dashed line, and wired
communication links are indicated by a solid line. However, in practice, it may be possible
for wired and wireless conmiunication links to be used interchangeably depending on the
requirements of a particular application. Data-link interface unit 58 may be used to upload
pre-acquired (or learned-on-the-fly) route information and driver operating behavior data
information to an on-broad route/driver behavior database 64 located on vehicle 20. Datalink
unit 58 may also be used to download new route and driver operating behavior data
compiled by ADAS 22 from recent trips to BORD 50. The route and driver operating
behavior database 52,54 stored on BORD 50 may include a comprehensive digital map of an
entire geographic region within which a business operates, and driving behavior information
on multiple drivers. To minimized the size and cost of on-broad route/driver behavior
database 64, the on-board database may be configured to only store route and driver operating
information specific to a particular route vehicle 20 will be traveling. The upload and
download process may occur at a vehicle distribution terminal before and after a trip, or may
take place in real-time through wireless communication between BORD 50 and ADAS 22.
[0031] Information/data used by LPM 48 to formulate a vehicle operating strategy for
maximizing fuel economy may be obtained from a variety of sensors and information
systems, which may include conventional on-board sensors, such as radar, vision, and a
vehicle databus, and information technology services (ITS), such as a global positioning
system (GPS), three-dimensional digital maps, and vehicle-to-vehicle (V2V) and vehicle-toinfrastructure
(V2I) communication devices. For example, ADAS 22 may include various
on-board sensors 66 configured to provide information regarding the operating environment
immediately surrounding vehicle 20. Sensors 66 collect information concerning potential
obstacles, such as other vehicles, present within the immediate vicinity of vehicle 20. Data
collected by sensors 66 may be used by ADAS 22 to estimate, for example, a distance
between vehicle 20 and the adjacent vehicles, as well as a speed and trajectory of the adjacent
vehicles. Sensors 66 may include radar 68 and vision sensors. ADAS 22 may further include
one or more data receivers 70 for receiving data transmitted over various wireless
communication systems. Such systems may include dedicated short range communication
systems (DSRC) 72, which may include for example, vehicle-to-vehicle (V2V)
communication devices. ADAS 22 may also receive data transmitted over dedicated
government frequencies from a data transmitter 73, for example, a global positioning system
(GPS) 74 that may be used to generate three-dimensional digital maps 77, and a traffic
message channel (TMC) 76.
[0032] Information and data received from sensors 66, data receiver 70, and various
other vehicle sensors, may be communicated to LPM 48 across a vehicle Controller Area
Network (CAN) 78. A known communications standard defined by the Society of
Automotive Engineers (SAE) is SAE J1939 for CAN-based communications. SAE J1587 is
another possible communications standard that may also be used. Moreover, other
communications standards, such as IS09141 K, or other known standards, may be employed.
[0033] ADAS 22 may also include a driver interface 79 that operably communicates
with LPM 48. Driver interface 79 provides the driver of vehicle 20 with information
concerning fuel economy and feedback for modifying driving behavior to help maximize fuel
economy. Driver interface 79 may included various visual displays, audio sources, such as
speakers, and haptic devices. Haptic technology is a tactile feedback technology that takes
advantage of a user's sense of touch by applying forces, vibrations, and/or motions to the user.
[0034] Referring to FIG. 3, LPM 48 may be configured to generate one or more output
signals. For example, a first output signal 92 may be transmitted to TECU 46 for use in
connection with controlling engine 32 and transmission 34. A second output signal 94 may
be transmitted to driver interface 79. The output signals from LPM 48 may be used to
provide engine drive torque/speed control, transmission shift control and advisory driver
information (i.e., through driver interface 79). Although exemplarily configured LPM 48 is
not described as employing brake control as a means for providing active assistance, the
illustrated system architecture may nevertheless be adapted to include active safety features,
such as brake control.
[0035] Continuing to refer to FIG. 3, LPM 48 may include five primary modules: a
vehicle condition module 82; an environment awareness module 84; a driver intent
recognition module 86; a FE-optimal behavior estimation module 88; and an arbitration
module 90. Although identified as individual modules for purposes of discussion, in practice,
the individual modules may be combined into one or more integrated modules. The modules
may be implemented as a combination of hardware and software, and may include one or
more software applications or processes for causing one or more computer processors to
perform the operations of LPM 48. The preceding discussion is equally applicable to TECU
46, which as noted, above may incorporate LPM 48 in one exemplary approach. The
operation of each module is described in more below.
Vehicle Condition Module (82 )
[0036] Vehicle condition module 82 operates to estimate vehicle load. This may be
accomplished, for example, by using an extended Kalman filter (KF) based method
employing data provided by GPS 74, digital maps 77, and vehicle CAN 78. Vehicle load
estimates may also be obtained through other means, such as private and governmental
operated weigh stations. Vehicle condition module 82 may also determine engine and
transmission configurations based on data received, for example, from vehicle CAN 78, or
through offline calibration procedures.
Environment Awareness Module (84)
[0037] Environment awareness module (84) may include a sensor fusion module 96
and a scenario recognition module 98. The two modules are identified separately for
purposes of discussion, but in practice, sensor fusion module 96 and scenario recognition
module 98 may be integrated into fewer or more modules.
[0038] Referring also to FIG. 4, sensor fusion module 96 may employ an algorithm that
utilizes the available sensor information from sensors 66 (FIG. 1) and data receiver 70 (FIG.
1) to predict future speed constraints that a driver may be facing within a look-ahead window
100. Sensor fusion module 96 receives information/data from on-board vehicle sensors 66
concerning the operating environment within an immediate vicinity of vehicle 20, and
integrates that information/data with environment information/data received by data receiver
70 from various external data systems, such as GPS 74, traffic message channel (TMC) 76,
and vehicle-to-infrastructure (V2I) communications. Sensor fusion module 96 may use the
available sensor information/data to formulate a digital model 101 of the current vehicle
operating environment occurring within an operating window 103. Operating window 103
includes look-ahead window 100, which extends a selected distance 102 in front of vehicle 20
along the vehicle's path of travel.
[0039] In the example illustrated in FIG. 4, operating window 103 is divided into three
segments: a first segment 104; a second 106; and a third segment 108. In practice, however,
operating window 103 may also be divided into fewer or more segments. Further, the number
of segments need not remain constant, and may be varied based on various factors, including
the operating environment in which vehicle 20 is operating. The operating environment may
include, for example, road conditions (i.e., number of lanes, curvature of the road, whether the
road or snow covered, etc.), traffic density and the proximity of other vehicles to vehicle 20.
[0040] Each segment 104, 106 and 108, may be defined by a starting node and an
ending node. For example, a first node 110 corresponds to the starting node of segment 104,
and a second node 112 correspond to its ending node. The ending node of a segment
corresponds to the starting node of the next subsequent segment. For example, second node
112, which corresponds to the ending node of segment 106, also corresponds to the starting
node of second segment 106. Similarly, a third node 114 correspond to the ending node of
second segment 106, and the starting node of third segment 108. A fourth node 116
corresponds to an ending node of third segment 108. First node 110 also coincides with a
beginning of operating window 103, and fourth node 116 coincides with an end of operating
window 103. Operating window 103 may be continuously updated in order that vehicle 20 is
never located outside the beginning and ending boundaries of operating window 103, as
defined by first node 110 and fourth node 116.
[0041] Sensor fusion module 96 may generate a road condition database 118 that may
include information on road conditions occurring within first segment 104, such as, for
example, a length of the segment, the node type, posted speed limits, road class, number of
lanes, and grade and curvature profiles. Exemplary node types may include a point at which a
speed change occurs, an intersection, traffic light, stop sign and other points of the road where
either road topology or a road speed limit change occurs. Similar road condition databases
120 and 1 2 may be compiled for second and third segments 106 and 108.
[0042] Sensor fusion model 96 may receive information/data from various sources. For
example, information/data concerning location and speed of vehicles traveling in the vicinity
of vehicle 20 may be obtained from sensors 66, and may include an estimated actual distance
(Range { }) between vehicle 20 and each adjacent vehicle, a speed (Range rate {r j}) of the
adjacent vehicle, and a confidence factor (Confidence {C i}). Confidence {C i provides an
indication of the accuracy of the data used to determine Range {r ,} and Range rate {r }.
Information/data concerning traffic patterns in the vicinity of vehicle 20 may be obtained
from data receiver 70, which may include, for example, an estimated speed at which traffic is
flowing within look-ahead window 100 (Traffic flow speed {s i}), a geographical location of
the traffic flow (Traffic flow location {d j}), and a computed factor (Confidence {C j}).
Confidence {C i} provides an indication of the accuracy of the data used to determine Traffic
flow speed {S i and Traffic flow location {d }. Sensor fusion model 96 may also receive
road and route information from GPS and third party mapping services.
[0043] Sensor fusion module 96 uses the information received from sensors 68 and data
receiver 70 to generate a digital target overlay with road geometry 124, which may be filtered
through a bank of extended Kalman filter (E F) type trackers to determine various operating
parameters of a nearest in-path vehicle 125 relative to vehicle 20. The computed operating
parameters may include a relative speed (v;), a lane position (1;), a direction (h,); and a relative
distance (di) of nearest in-path vehicle 125 relative to vehicle 20. Relative distance (dj) takes
into account the speed of the vehicles, and consequently, is typically less than the actual
distance (Range {r ;}). The computed properties may be used to determine an available space
127 between vehicle 20 and nearest in-path vehicle 125. The relative speed (Vi), lane position
(li), direction (hi) and relative distance (d;) of nearest in-path vehicle 125 may be used in
conjunction with the traffic flow infomiation (when available) obtained by data receiver 70 to
generate a micro traffic prediction model 126 describing traffic conditions occurring
immediately in front of vehicle 20. Micro traffic prediction model 126 may be used to
compute a speed profile 128 of nearest in-path vehicle 125.
[0044] The individual in-path vehicle information (i.e., relative speed (v;), lane position
(li), direction (hi) and relative distance (d;)) may also be integrated with the traffic flow
information (when available) obtained by data receiver 70 and a historical host vehicle speed
profile 130, which may be obtained, for example, from BORD 50, to generate a macro traffic
prediction model 132. An averaged traffic speed profile 134 may be computed for each
segment 104, 106 and 108 of operating window 103 using macro traffic prediction model
132.
[0045] With continued reference to FIG. 3 and Table 1 below, scenario recognition
module 98 may use the output from sensor fusion module 96 (i.e., speed profile 128 of
nearest in-path vehicle 125 and averaged traffic speed profile 134) to identify an operating
scenario occurring within look-ahead window 100. Scenario recognition module 98 may
include multiple predefined operating scenarios from which to select. Each operating
scenario may be defined by mission and environment conditions that a driver encounters, and
generally includes two attributes. The first attribute may include static environment
constraints 136, shown in FIG. 5 and Table 1, and the second attribute may include dynamic
environment constraints 138. Static environment constraints 136 may include, for example,
road geometry and posted speed limits, which information may be obtained from GPS and
digital map systems. Dynamic environment constraints 138 , shown in FIG. 5 and Table 1,
may include real-time traffic information, such as the relative speed and location of
sunOunding vehicles, and phasing of traffic lights located along the look-ahead window route.
This information may be obtained from on-board sensors 66 (FIG. 1), which may include
radar and vision systems, and remote information technology systems, such as vehicle-tovehicle
(V2V) and vehicle-to-infrastructure (V2I) communication devices that transmit
information to data receiver 70.
[0046] The exemplary implementation of scenario recognition module 98 includes
eight predefined operating scenarios. In practice, however, fewer or more operating scenarios
may be defined depending on the requirements of a particular application. Each operating
scenario may include a set of static environment constraints 136 and a set of dynamic
environment constraints 138. For example, the eight predefined operating scenarios are
arrived at by combining four sets of static environment constraints with two sets of dynamic
environment constraints. The four sets of static environment constraints 136 may be include
the following:
1. "Up-speed" 142: defined by a road segment in look-ahead window
100 that includes an increase in the posted speed limit, such as may
occur when entering a freeway on-ramp or a segment of road
following a stop sign;
2. "Down-speed" 144: defined by a road segment in the look-ahead
window 100 that includes a decrease in the posted speed limit, such
as may occur when exiting a freeway off-ramp or a segment of road
approaching a stop sign;
3. "Speed keeping" 146: defined by a road segment in the look-ahead
window 100 with a constant speed limit; and
4. "ELSE" 148: defined by a low speed environment or off-road
condition, such as may occur in a parking lot and on a non-public
service road.
[0047] The two sets of dynamic environment constraints 138 may include a "Traffic"
condition and a "No Traffic" condition, depending on whether the traffic flow is significantly
lower than the posted speed limit or there is an impeding vehicle in a path of travel of vehicle
20 (i.e., "Traffic").
[0048] LPM 48 may utilize a finite state algorithm to determine a current value of static
environment constraints 136 and dynamic environment constraints 138. FIG. 5 schematically
illustrates an exemplary decision making process that may be employed by the finite state
algorithm to determine a current value of the static and dynamic environment constraints.
The current value of static environment constraint 136 and dynamic environment constraint
138 may be determined by evaluating a value of various predefined parameters. The
predefined parameters may include, for example, a first distance parameter (Fl) and a second
distance parameter (F2). First distance parameter (Fl) corresponds to a condition in which a
distance (Dist2PrevNode) between vehicle 20 and a node immediately behind vehicle 20 (i.e.,
node 110 in FIG. 4) is greater than a threshold distance computed as function of a maximum
operating speed (SpdLmt) at which vehicle 20 may be operated. The maximum operating
speed (SpdLmt) may correspond to the lesser of a speed of the surrounding traffic (TraFlow),
a posted road speed limit (RdSpdLmt), and a maximum speed limit set in accordance with a
vehicle fleet policy (FleetPolicy). Second distance parameter (F2) corresponds to a condition
in which a distance (Dist2NextNode) between vehicle 20 and a node immediately proceeding
vehicle 20 (i.e., node 112 in FIG. 4) is greater than a threshold distance computed as function
of the maximum operating speed (SpdLmt) at which vehicle 20 may be operated.
0049) The predefined parameters may also include a first speed parameter (P0) and a
second speed parameter (PI). First speed parameter (P0) corresponds to a condition in which
a speed of the traffic within look-ahead window 100 (TraFlow) is less than a first selected
percentage (pO) of the posted road speed limit (RdSpdLmt). Second speed parameter (PI)
corresponds to a condition in which the speed of the traffic within look-ahead window 100
(TraFlow) is greater than a second selected percentage (pi) of the posted road speed limit
(RdSpdLmt). First and second percentages (pO) and (pi) are expressed in decimal form, and
range between zero and one, with (pO) being less than (pi).
[0050] The predefined parameters may also include a first time parameter (TO) and a
second time parameter (Tl). First time parameter (TO) corresponds to a condition in which a
distance (ImpVeh) between vehicle 20 and nearest in-path vehicle 125 is less than a minimum
threshold value computed as a function of the following: a time (Headway) for vehicle 20 to
travel the distance (ImpVeh) between vehicle between vehicle 20 and nearest in-path vehicle
125; the amount of time (TTC) it would take vehicle 20 to collide with nearest in-path vehicle
125 computed based on a relative speed between the two vehicles; and empirically
determined factors (tl) and (t2). Factor (tl) has a value much greater than zero and less than
02).
[0051] Second time parameter (Tl) corresponds to a condition in which distance
(ImpVeh) between vehicle 20 and nearest in-path vehicle 125 is greater than a maximum
threshold value computed as a function of following: the time (Headway) for vehicle 20 to
travel the distance (ImpVeh) between vehicle between vehicle 20 and nearest in-path vehicle
125; the amount of time (TTC) it would take vehicle 20 to collide with nearest in-path vehicle
125 computed based on a relative speed between the two vehicles; and empirically
determined factors (t3) and (t4). Factor (t3) has a value much greater than zero and less than
(t4).
[0052] The current value of the dynamic environment constraint 138 may be
determined by evaluating the status of parameters P0, PI, TO and Tl. If it is determined that
parameters I and Tl are satisfied, the dynamic environment constraint is set to "No Traffic"
137. If it is determined that parameters P0 and TO are satisfied, the dynamic environment
constraint is set to "Traffic" 139.
[0053] The current value of the static environment constraint 136 may be determined
by evaluating a current speed (V) of vehicle 20; the status of parameters F0 and F2; whether
vehicle 20 is being operated off-road (i.e., OffRoad = true) or on road (i.e., OffRoad = false);
and whether a maximum operating speed (SpdLmt) of vehicle 20 is increasing or decreasing.
Static environment constraint 136 is initially set to a value of "ELSE" 148. Static
environment constraint 136 will also be set to "ELSE" 148 whenever vehicle 20 is operated
off-road (i.e., OffRoad = true). Static environment constraint 136 transitions from "ELSE"
148 to "Up-Speed" 142 when it is determined that the velocity (V) of vehicle 20 is greater
than zero (i.e., vehicle 20 is moving) and parameter Fl is not satisfied. Static environment
constraint 136 transitions from "ELSE" 148 to "Down-Speed" 144 when it is determined that
the velocity (V) of vehicle 20 is greater than zero (i.e., vehicle 20 moving) and parameter F2
is satisfied. Static environment constraint 136 transitions from "Up-speed" 142 to "Down-
Speed" 144 when the maximum speed (SpdLmt) of vehicle 20 is set to decrease at the next
node (i.e., node 112 in FIG. 4) and parameter F2 is not satisfied. Static environment
constraint 136 transitions from "Up-speed" 142 to "Speed Keeping" 146 when both
parameters Fl and F2 are satisfied. Static environment constraint 136 transitions from
"ELSE" 148 to "Down-Speed" 144 when it is determined that the velocity (V) of vehicle 20
is greater than zero (i.e., vehicle 20 moving) and parameter F2 is satisfied. Static
environment constraint 136 transitions from "Down-speed" 144 to "Up-Speed" 142 whenever
vehicle 20 passes a new node (i.e., node 112 in FIG. 14) and the maximum speed (SpdLmt) of
vehicle 20 has increased. Static environment constraint 136 transitions from "Down-speed"
144 to "Speed Keeping" 146 when both parameters Fl and F2 are satisfied. Static
environment constraint 136 transitions from "ELSE" 148 to "Down-Speed" 144 when it is
determined that the velocity (V) of vehicle 20 is greater than zero (i.e., vehicle 20 moving)
and parameter F2 is satisfied. Static environment constraint 136 transitions from "Speed
Keeping " 146 to "Up-Speed" 142 whenever vehicle 20 passes a new node (i.e., node 112 in
FIG. 4) and the maximum speed (SpdLmt) of vehicle 20 has increased. Static environment
constraint 136 transitions from "Speed Keeping" 142 to "Down-Speed" 144 when the
maximum speed (SpdLmt) of vehicle 20 is set to decrease at the next node (i.e., node 112 in
FIG. 4) and parameter F2 is not satisfied.
[0054] The control strategy implemented by LPM 48 is dependent on the current
operating scenario in which vehicle 20 is operating. For example, with reference to Table 1
below, if static environment constraint 136 is set to "Up-Speed" 142, which may occur, for
example, when vehicle 20 is operating on a road segment that includes an in increase in speed
limit, such as a freeway on ramp, and a value of "No traffic" 137 is assigned to dynamic
environment constraint 138, scenario recognition module 98 will determine that no
intervention is necessary and LPM 48 will not attempt to influence the driving behavior of the
vehicle operator. On the other hand, if static environment constraint 136 is set to "Down-
Speed" 144, which may occur, for example, when vehicle 20 is exiting a freeway off-ramp,
and a value of "Traffic" 139 is assigned to dynamic environment constraint 138, scenario
recognition module 98 will determine there is a potential for intervening in the operation of
vehicle 20 or otherwise influencing the driving behavior of the vehicle operator.
Table 1
[0055] Environment awareness module 98 only determines if there is a potential for
intervening in the operation of vehicle 20 and/or influencing the driving behavior of the
vehicle operator. The decision on whether LPM 48 actually intervenes in the operation of
vehicle 20, or otherwise attempts to influence the driving behavior of the vehicle operator
occurs within arbitration module 90, which is described in more detail below.
FE-optimal Behavior Estimation Module (88)
[0056] FE-optimal behavior estimation module 88 may use the output from
environment awareness module 84 and vehicle condition module 84 to derive an optimized
drive cycle (i.e., target speed profile v*(t)) by determining a fuel economy that may be
achievable under the environment constraints determined by environment awareness module
84. Target speed profile v*(t) may include acceleration intensity and gearshift timing. Target
speed profile v*(t) may be determined based on the following optimization scheme, where Dup
is a predicted travel distance profile assuming vehicle 20 will be following the micro/macro
traffic flow as determined by environment awareness module 84. The result of this
optimization process is a speed profile v*(t) that enables vehicle 20 to travel distance Dup
within look-ahead window 100 (FIG. 4) with minimum fuel usage and without having to pass
any vehicles or incurring any additional travel time.
[0057] The following is an example of the calculations that may be employed by FEoptimal
behavior estimation module 88 to compute speed profile n (ί ) :
T1 LLooookkAAhheeaadd
v * (t) = arg min Fuel(t)dt, such that
v(t)
* (T) £ V SpdLM ( v(t)dt), for VT,0 < T £
and \ v (t)dt £ D up ( , /orVf ,0 < t £ TLoo h ad
T LookAhead and v * (t)dt = D o e
where D up TLookAhead ) = ook l d
and where Ti00 a ea is a user defined parameter that defines a forward time horizon
over which the system will predict the FE-optimal behavior
[0058] Depending on the particular scenario, the calculation of speed profile v*(f) may
vary from computational sequence described above. An exemplary speed profile v*(t)
computational method 148 is illustrated in FIG. 6. Information/data received from
environment awareness module 84 (block 150) may be used in conjunction with macro traffic
prediction 132 generated by sensor fusion module 96 to determine an average distance (Da g)
(block 152) that corresponds to an available open distance 127 (FIG. 4) between vehicle 20
and a nearest in-path vehicle 5 (FIG. 4) (block. A determination is made at block 154 on
whether vehicle 20 is operating in traffic based on the status of dynamic environment
constraint 138, as provided by environment awareness module 84. If dynamic environment
constraint 138 is set to "No Traffic" (FIG. 5), the computation proceeds to block 156, where
an initial target speed profile is determined based on the current road speed limit and node
type. The computation then proceeds to block 158, where the previously computed speed
profile may be modified, if necessary, to accommodate a particular grade and curvature of the
road within look-ahead window 100. The resulting output (block 160) is a target speed
profile n*(Dc), which may be used by FE-optimal behavior estimation module 88 to
determine a corresponding engine/transmission optimized drive torque estimated to produce
the target speed profile.
[0059] Continuing to refer to FIG. 6, if it is determined at block 154 that vehicle 20 is
operating in traffic based on the status of dynamic environment constraint 138, the
computation proceeds to block 162, where a travel distance D p within look-ahead window
100 (FIG. 4) is computed based on the macro/micro traffic prediction provided by
environment awareness module 84. The process then proceeds to block 164, where an initial
target speed profile is determined based on the determined travel distance Dup. The
computation proceeds to block 166, where the previously computed initial speed profile may
modified, if necessary, in accordance with a current road speed limit within look-ahead
window 100 and a node type. The resulting output is a target speed profile n*(Dc), which
may be used by FE-optimal behavior estimation module 88 to determine a corresponding
engine/transmission optimized drive torque calculated to produce the target speed profile.
[0060] The computed target speed profile may be used to determine a corresponding
throttle position, acceleration command or drive torque, for enabling vehicle 20 to track the
target speed determined by FE-optimal behavior estimation module 88. This may be
accomplished by tracking a reference command, such as a reference or a reference speed,
using feedback linearization. The process may employ an approximate validated plant model
for determining a drive torque estimated to achieve the target speed profile. A set of
exemplary equations that may be employed by the plant model algorithm are described
below. A vehicle speed (V) can be expressed as a function of engine torque (Te), braking
torque (Tb) and vehicle drag forces. These dynamic forces may be obtained from the
validated plant model. The corresponding velocity dynamics, expressed in control affine
form, are as follows:
(a) V = )Te +f ub +f )
where:
i is the gear ratio;
¾ is a brake command; and
a is a grade angle.
[0061] Since the disclosed exemplary configuration of ADAS 22 does not include
control of brake command ¾, control over speed profile v*(t) may be accomplished by
manipulating the engine torque T produced by engine 32 and the gear ratio on the
transmission to achieve the target speed V target- Using first order speed error dynamics, the
relationship between vehicle velocity (V) and target speed Vtarg et may be expressed as:
>) V - V l ) +kv V - Vl s ) =
= e „ + k„e„ = 0
[0062] Target speed Vtarget y be assumed to have a relatively low rate of change, and
as such, Vtarget closely approximates zero. This reduces equation (b) above to:
(c) v = k v V Vt g t )
[0063] Substituting equation (c) into equation (a) yields the following:
(d) +f u
b +f V, a) = - kv (V - Vl l ))+Vtarg
[0064] Solving equation (d) for Te results in the following equation for computing an
optimized drive torque Te estimated to attain the vehicle target speed V tar get for a particular
gear option
(e) T - v - V ) f Ub - f 3 (V,a))
[0065] Multiple gear options may be feasible to achieve the same demand drive torque.
In that case, the estimated vehicle fuel economy with respect to other feasible gear options
may be estimated and the best gear selection with respect to fuel economy may be chosen and
the corresponding engine torque may become the final recommended engine torque.
Driver Intent Recognition Module (86)
[0066] Operating in parallel with environment awareness module 84 and FE-optimal
behavior estimation module 88, driver intent recognition module 86 accumulates historical
driving data under various environment conditions. The compiled data may be used to
formulate a statistical model for estimating a vehicle driver's current and future intent
regarding the operation of vehicle 20, and estimate if the driver's actions are being executed,
for example, in connection passing, merging, and performing a lane change, to name a few.
Driver intent recognition module 86 provides information/data that may be used by arbitration
module 90 when assessing whether to actively intervene in the operation of vehicle 20 and/or
prompt the driver to modify their driving behavior.
Arbitration Module (90)
[0067] A schematic illustration of an exemplarily configured arbitration module 90 is
illustrated in FIG. 7. Arbitration module 90 determines an arbitrated drive torque for
propelling vehicle 20. The arbitrated drive torque may fall within a range bounded by and
including the optimized torque determined by FE-optimal behavior estimation module 88 and
a drive torque requested by the vehicle driver. The arbitrated drive torque may be used by
various vehicle controls, such as TECU 46, to control operation of engine 32, for example,
through manipulation of an engine throttle position, and transmission 34, for example,
through initiation of a gear range shift. The arbitrated torque may also be used to formulate a
strategy for passively prompting the driver of the vehicle to adjust their driving behavior, for
example, by manually manipulating a vehicle accelerator pedal to adjust an engine throttle
position, or manually initiating a transmission gear shift.
[0068] Arbitration module 90 may utilize the information/data compiled and/or
determined by vehicle condition module 82, environment awareness module 84, driver intent
recognition module 86, and FE-optimal behavior estimation module 88, when determining the
arbitrated torque. Factors that may be considered include a difference between the driver
demand/intent, as determined by driver intent recognition module 86, and the fuel economy
optimal behavior profile, as determined by FE-optimal behavior estimation module 88.
Another factor is the nature of the perceived driver intent, for example, whether the driver's
actions are safety and/or performance related. In instances where environment awareness
module 84 determines that driver assistance may benefit fuel economy (i.e., there is a
potential for intervention, as indicated, for example, in Table 1), arbitration module 90
determines whether to provide advisory feedback to the driver or actively assist the driver
through powertrain automation, for example, performing gearshifts and controlling engine
torque and speed. The determination whether to engage in advisory feedback and active
assistance may take into consideration related safety and drivability factors, the accuracy of
the output from environment awareness module 84, and the manner in which the driver
behavior deviates from the fuel economy optimal behavior determined by FE-optimal
behavior estimation module 88.
[0069] When determining whether to actively intervene in the operation of vehicle 20
and/or encourage modifications in driving behavior, arbitration module 90 evaluates various
sets of sub-scenarios. For purposes of discussion, exemplary arbitration module 90 is
described as evaluating three sub-scenarios. In practice, however, fewer or more subscenarios
may be specified. With reference to FIG. 7, the three exemplary sub-scenarios may
include a safety scenario identification 168, a system confidence 170 and a driver acceptance
identification 172.
[0070] Sub-scenario 1 8 is concerned with identifying safety and performance related
events that may require a driver to maintain full control of vehicle 20. This may be
accomplished by assessing the driver's intent to override the actions of LPM 48 in an effort to
obtain complete and full control of vehicle 20, as may be indicated by the operation of various
vehicle controls The following is an exemplary list of various vehicle controls and operating
conditions that may be monitored by arbitration module 90 when evaluating vehicle safety
and/or performance related conditions:
a) Vehicle braking - indicated by a brake pedal position greater or lesser than
zero;
b) Decelerating - indicated by an accelerator pedal position at time t j that is
greater than an accelerator pedal position at time t.+l for j consecutive time
steps;
c) Coasting - indicated by an accelerator pedal position at time tj+l that is less
than an accelerator pedal position at time t,., which is less than an accelerator
pedal position at time ti+1 for j consecutive time steps;
d) Just before changing lane - indicated by a turn signal on and vehicle (20) not
decelerating;
e) Just before turning - indicated by turn signal on and vehicle (20) decelerating
or coasting;
f) Attempting to catch a traffic light - indicated by the next node being a traffic
light and vehicle (20) has passed a slowing down distance from the node (i.e.,
traffic light) and the vehicle (20) is not decelerating;
g) Just prior to commencing passing - (similar to changing lane); and
h) Just prior to commencing merging - (similar to changing lane)
[0071] A safety flag is activated (i.e., set to "YES") if any of the above identified safety
and/or performance conditions is detected by arbitration module 90.
[0072] Sub-scenario 172 is concerned the driver's acceptance or resistance to LPM 48
exerting control over the operation of vehicle 20. The following is a list of vehicle operating
conditions, which if detected by arbitration module 90 may indicate the driver of vehicle 20 is
resisting the actions of LPM 48:
a) Continuous driver demand for acceleration (CDA) (as indicated by the driver
requested drive torque) that exceeds an LPM 48 determined optimized drive
torque, and no deceleration detected for a certain time period, and which a
previous arbitration was in disfavor of the driver's actions;
b) Driver fully actuating accelerator pedal - indicated by an accelerator pedal
position greater than a threshold position; and
c) Driver demand for acceleration under load - indicated by factors similar to
CDA, but occurring over a shorter time period and the product of (road grade)
x (vehicle mass) exceeds a threshold load.
[0073] A driver resistance flag is activated (i.e., set to "YES") if any of the above
identified operating conditions is detected by arbitration module 90.
[0074] Sub-scenario 170 evaluates the accuracy of the information/data provided to the
various LPM modules and assigns a corresponding rating value. By way of example,
arbitration module 90 may employ three rating designations, including a "High Confidence",
"Medium Confidence", and "Low Confidence".
[0075] Arbitration module 90 may transition between various operating modes
depending on the status of the safety flag and the driver resistance flag. The exemplarily
configured arbitration module 90 employs three operating modes, but in practice, fewer or
more operating modes may be defined. The three operating modes may include the
following:
a) Driver Mode 174 - no intervention: arbitrated drive torque is equal to 100%
driver requested drive torque;
b) LPM Mode 176 - full intervention: arbitrated drive torque is equal to 100%
LPM determined optimized drive torque; and
c) Transition Mode 178 - when transitioning between Driver Mode 174 and the
LPM Mode 176, arbitrated torque may be ramped between the driver requested
drive torque and the LPM determined optimized drive torque.
[0076] FIG. 8 schematically illustrates an exemplary arbitration algorithm 179 that may
be employed by arbitration module 90. The decision process commences at start 180.
Arbitration module 90 checks the status of the safety flag and driver resistance flag. If either
flag is activated (i.e., set to "YES"), arbitration module 90 will proceed to activate Driver
Mode 174. Driver Mode 174 will continue to remain activated so long as either the safety
flag or driver resistance flag is activated (i.e., set to "YES"). If, on the other hand, arbitration
module 90 determines that neither the safety flag nor the driver resistance flags are activated
(i.e., both flags set to "NO"), arbitration module 90 will than proceed to activate LPM Mode
176. LPM Mode 176 will remain activated until either the safety flag or the driver resistance
flag is activated (i.e., set to "YES").
[0077] Continuing to refer to FIG. 8, f the safety flag is activated (i.e., set to "YES")
while operating in LPM Mode 176, arbitration module 90 will proceed to activate Driver
Mode 174. If the driver resistance flag is activated (i.e., set to "YES") while operating in
LPM Mode 176, and the status of the safety flag remains unchanged (i.e., set to "NO"),
arbitration module 90 will proceed to activate Transition Mode 178. Arbitration module 90
will continue to operate in Transition Mode 178 so long as the safety flag is deactivated (i.e.,
set to "NO") and the transition between the LPM Mode and the Driver Mode has not yet been
completed. If the safety flag is activated (i.e., set to "YES") while operating in Transition
Mode 178, arbitration module 90 will proceed to activate Driver Mode 174. If the driver
resistance flag is deactivated (i.e., set to "NO") while operating in Transition Mode 178, and
the status of the safety flag remains unchanged (i.e., set to "NO"), arbitration module 90 will
proceed to activate LPM Mode 176.
[0078] Continuing to refer to FIG. 8, if the safety flag is deactivated (i.e., set to "NO")
while operating in Driver Mode 174, arbitration module 90 will proceed to activate Transition
Mode 178. Arbitration module 90 will continue to operate in Transition Mode 178 so long as
the safety flag is deactivated (i.e., set to "NO") and the transition between the LPM Mode and
the Driver Mode has not yet been completed.
[0079] It will be appreciated that the exemplary active driver assistance system
described herein has broad applications. The foregoing configurations were chosen and
described in order to illustrate principles of the methods and apparatuses as well as some
practical applications. The preceding description enables others skilled in the art to utilize
methods and apparatuses in various configurations and with various modifications as are
suited to the particular use contemplated. In accordance with the provisions of the patent
statutes, the principles and modes of operation of the disclosed container have been explained
and illustrated in exemplary configurations.
[0080] In general, computing systems and/or devices, such as TECU 46 and LPM 48
(when not incorporated into TECU 46), may employ any of a number of computer operating
systems, including, but by no means limited to, versions and/or varieties of the Microsoft
Windows® operating system, the Unix operating system (e.g., the Solaris® operating system
distributed by Oracle Corporation of Redwood Shores, California), the AIX UNIX operating
system distributed by International Business Machines of Armonk, New York, the Linux
operating system, and the Mac OS X and iOS operating systems distributed by Apple Inc. of
Cupertino, California. Examples of computing devices include, without limitation, a module,
a computer workstation, a server, a desktop, notebook, laptop, or handheld computer, or some
other computing system and/or device.
[0081] Computing devices generally include computer-executable instructions, where
the instructions may be executable by one or more computing devices such as those listed
above. Computer-executable instructions may be compiled or interpreted from computer
programs created using a variety of programming languages and/or technologies, including,
without limitation, and either alone or in combination, Java™, C, C++, Visual Basic, Java
Script, Perl, etc. In general, a processor (e.g., a microprocessor) receives instructions, e.g.,
from a memory, a computer-readable medium, etc., and executes these instructions, thereby
performing one or more processes, including one or more of the processes described herein.
Such instructions and other data may be stored and transmitted using a variety of computerreadable
media.
[0082] A computer-readable medium (also referred to as a processor-readable medium)
includes any non-transitory (e.g., tangible) medium that participates in providing data (e.g.,
instructions) that may be read by a computer (e.g., by a processor of a computer). Such a
medium may take many forms, including, but not limited to, non-volatile media and volatile
media. Non-volatile media may include, for example, optical or magnetic disks and other
persistent memory. Volatile media may include, for example, dynamic random access
memory (DRAM), which typically constitutes a main memory. Such instructions may be
transmitted by one or more transmission media, including coaxial cables, copper wire and
fiber optics, including the wires that comprise a system bus coupled to a processor of a
computer. Common forms of computer-readable media include, for example, a floppy disk, a
flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any
other optical medium, punch cards, paper tape, any other physical medium with patterns of
holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip or
cartridge, or any other medium from which a computer can read.
[0083] Databases, data repositories or other data stores described herein such as those
involved in BORD 50 (e.g., Route database 52 and Driver behavior database 54), may include
various kinds of mechanisms for storing, accessing, and retrieving various kinds of data,
including a hierarchical database, a set of files in a file system, an application database in a
proprietary format, a relational database management system (RDBMS), etc. Each such data
store is generally included within a computing device employing a computer operating system
such as one of those mentioned above, and are accessed via a network in any one or more of a
variety of manners. A file system may be accessible from a computer operating system, and
may include files stored in various formats. An RDBMS generally employs the Structured
Query Language (SQL) in addition to a language for creating, storing, editing, and executing
stored procedures, such as the PL/SQL language mentioned above.
[0084] In some examples, system elements may be implemented as computer-readable
instructions (e.g., software) on one or more computing devices (e.g., modules, servers,
personal computers, etc.), stored on computer readable media associated therewith (e.g.,
disks, memories, etc.). A computer program product may comprise such instructions stored
on computer readable media for carrying out the functions described herein.
[0085 It is intended that the scope of the present methods and apparatuses be defined
by the following claims. However, it must be understood that the disclosed active driver
assistance system may be practiced otherwise than is specifically explained and illustrated
without departing from its spirit or scope. It should be understood by those skilled in the art
that various alternatives to the configuration described herein may be employed in practicing
the claims without departing from the spirit and scope as defined in the following claims. The
scope of the disclosed container should be determined, not with reference to the above
description, but should instead be determined with reference to the appended claims, along
with the full scope of equivalents to which such claims are entitled. It is anticipated and
intended that future developments will occur in the arts discussed herein, and that the
disclosed systems and methods will be incorporated into such future examples. Furthermore,
all terms used in the claims are intended to be given their broadest reasonable constructions
and their ordinary meanings as understood by those skilled in the art unless an explicit
indication to the contrary is made herein. In particular, use of the singular articles such as
"a," "the," "said," etc., should be read to recite one or more of the indicated elements unless a
claim recites an explicit limitation to the contrary. It is intended that the following claims
define the scope of the device and that the method and apparatus within the scope of these
claims and their equivalents be covered thereby. In sum, it should be understood that the
device is capable of modification and variation and is limited only by the following claims.
CLAIMS
What is claimed is:
1. A method for optimizing vehicle performance, the method comprising:
determining an optimized drive torque for maximizing vehicle fuel economy;
detecting a driver requested drive torque;
determining if the driver requested drive torque is at least one of performance related
and safety related; and
setting an arbitrated drive torque to the optimized drive torque when determining the
driver requested drive torque is not performance related and safety related, and setting the
arbitrated drive torque to the driver requested drive torque when determining the driver
requested drive torque to be at least one of performance related and safety related.
2. The method of claim 1 further comprising:
determining if the detected driver requested drive torque is less than the determined
optimized drive torque; and
setting the arbitrated drive torque to the driver requested drive torque when
determining the optimized drive torque to be greater than the driver requested drive torque.
3. The method of claim 2 further comprising setting the arbitrated drive torque to the
driver requested drive torque whenever the optimized drive torque is determined to be greater
than the driver requested drive torque.
4. The method of claim 2 further comprising monitoring operation of at least one vehicle
control in connection with determining if the driver requested drive torque is at least one of
performance related and safety related.
5. The method of claim 4 further comprising determining the driver requested drive
torque to be at least one of performance related and safety related when operation of the
monitored vehicle control is detected.
6. The method of claim 4, wherein the at least one vehicle control includes at least one of
a vehicle brake pedal position, vehicle accelerator pedal position, and vehicle turn signal.
7. The method of claim 1 further comprising:
monitoring a driver resistance to setting the arbitrated drive torque to the optimized
drive torque; and
setting the arbitrated drive torque between the driver requested drive torque and the
optimized drive torque when driver resistance is detected.
8. The method of claim 7, wherein driver resistance is indicated by at least one of a
driver requested drive torque exceeding the optimized drive torque for a selected time period,
a fully actuated vehicle accelerator pedal, and a driver initiated request for drive torque
occurring under a load condition.
9. The method of claim 1 further comprising:
determining a potential for optimizing fuel economy;
setting the arbitrated drive torque to the driver requested drive torque when it is
determined there is no potential for optimizing fuel economy.
10. The method of claim 9 further comprising:
determining at least one of a static environment constraint and a dynamic environment
constraint; and
determining the potential for optimizing fuel economy based on at least one of the
static environment constraint and the dynamic environment constraint.
11. The method of claim 10, wherein at least one of the static environment constraint and
the dynamic environment constraint is determined based on a vehicle speed limit.
12. The method of claim 1 further comprising adjusting a vehicle throttle position in
response to the arbitrated drive torque.
13. The method claim 12, wherein adjusting the vehicle throttle position comprises
manually manipulating a position of a vehicle accelerator pedal.
14. The method of claim 1 further comprising initiating a transmission gear shift in
response to the arbitrated drive torque.
15. A method for optimizing vehicle performance, the method comprising:
determining an optimized drive torque for maximizing vehicle fuel economy;
detecting a driver requested drive torque;
determining a static environment constraint;
determining a dynamic environment constrain;
determining a potential for optimizing fuel economy based on at least one of the static
environment constraint and dynamic environment constraint;
setting an arbitrated drive torque to the driver requested drive torque when
determining there is determined there is no potential for optimizing fuel economy;
setting the arbitrated drive torque to the optimized drive torque when determining
there is a potential for optimizing fuel economy.
16. The method of claim 15, further comprising
determining if the driver requested drive torque is at least one of performance related
and safety related; and
setting the arbitrated drive torque to the optimized drive torque when determining the
driver requested drive torque is not performance related and safety related, and setting the
arbitrated drive torque to the driver requested drive torque when determining the driver
requested drive torque is at least one of performance related and safety related.
17. The method of claim 16 further comprising monitoring operation of at least one
vehicle control in connection with determining if the driver requested drive torque is at least
one of performance related and safety related.
18. The method of claim 17 further comprising determining the driver requested drive
torque to be at least one of performance related and safety related when operation of the
monitored vehicle control is detected.
19. The method of claim 17, wherein the at least one vehicle control includes at least one
of a vehicle brake pedal position, vehicle accelerator pedal position, and vehicle turn signal.
20. The method of claim 15 further comprising:
monitoring a driver resistance to setting the arbitrated drive torque to the optimized
drive torque; and
setting the arbitrated drive torque between the driver requested drive torque and the
optimized drive torque when driver resistance is detected.
1. The method of claim 20, wherein driver resistance is indicated by at least one of a
driver requested drive torque exceeding the optimized drive torque for a selected time period,
a fully actuated vehicle accelerator pedal, and a driver initiated request for drive torque
occurring under a load condition.
22. The method of claim 15 further comprising:
determining if the detected driver requested drive torque is less than the determined
optimized drive torque; and
setting the arbitrated drive torque to the driver requested drive torque when
determining the optimized drive torque to be greater than the driver requested drive torque.