Vehicle control systems for controlling automated acceleration and braking of vehicles

Abstract

A method for controlling the automated acceleration and braking of vehicles comprises detecting, via at least one vehicle object sensor of a host vehicle, a target vehicle (CIP target vehicle) in front of the host vehicle, obtaining a path history of the CIP target vehicle, wherein the path history contains multiple historical positions of the CIP target vehicle at specified time intervals, obtaining a path history of the host vehicle, wherein the path history of the host vehicle contains multiple historical positions of the host vehicle at the specified time intervals, comparing the path history of the CIP target vehicle with the path history of the host vehicle to determine a divergence index, and in response to the fact that the divergence index is less than a specified divergence threshold,controlling the automated acceleration and braking of the host vehicle, at least partially based on the tracked movement of the CIP target vehicle.

Description

INTRODUCTION

[0001] The information provided in this section is intended to provide a general context for the disclosure. The work of the presently named inventors, to the extent described in this section, as well as those aspects of the description that do not otherwise qualify as prior art at the time of filing, are neither expressly nor implicitly acknowledged as prior art to the present disclosure.

[0002] The present disclosure relates generally to vehicle control systems for effectively detecting a closest in-path (CIP) vehicle, enabling control of automated acceleration and braking of a vehicle, including follow-through control from a CIP vehicle.

[0003] Some vehicles contain object tracking sensors that identify a closest-in-path (CIP) vehicle traveling in front of a host vehicle. During complex transition maneuvers, a target vehicle may deviate from the linear projection of a host vehicle's path. In some situations, an object tracking sensor may be unable to appropriately select a CIP vehicle, maintain the CIP vehicle, and abandon the CIP vehicle due to the rapidly changing conditions of the host vehicle and the target vehicle. This can result in suboptimal target object tracking, resulting in poor longitudinal control performance, for example, during automated collision-impending acceleration and braking (CIB), adaptive cruise control (ACC), etc. SUMMARY

[0004] A method for controlling automated acceleration and braking of vehicles includes detecting, via at least one vehicle object sensor of a host vehicle, a closest in-path target vehicle (CIP target vehicle) ahead of the host vehicle, obtaining a path history of the CIP target vehicle, the path history including a plurality of historical positions of the CIP target vehicle at specified time intervals, obtaining a path history of the host vehicle, the path history of the host vehicle including a plurality of historical positions of the host vehicle at the specified time intervals, comparing the path history of the CIP target vehicle with the path history of the host vehicle to determine a divergence index, and in response to the divergence index being less than a specified divergence threshold,controlling the automated acceleration and braking of the host vehicle based at least in part on the tracked motion of the CIP target vehicle.

[0005] According to other features, the method includes, in response to the divergence index being greater than the specified divergence threshold, ceasing to track the CIP target vehicle, wherein the automated acceleration and braking of the host vehicle are not controlled based on the tracked motion of the CIP target vehicle.

[0006] According to other features, the method includes, subsequent to ceasing to track the CIP target vehicle, identifying a second vehicle ahead of the host vehicle, the second vehicle different from the CIP target vehicle, and obtaining an initial path history of the second vehicle, the initial path history of the second vehicle including a plurality of historical positions of the second vehicle at the specified time intervals.

[0007] In other features, the method includes controlling automated lateral movement of the host vehicle based at least in part on the tracked movement of the CIP target vehicle via one or more steering actuators.

[0008] In other features, the method includes calculating the specified divergence threshold according to one or more gain parameters, wherein the one or more gain parameters include at least one of a highway type, a city street type, a presence of a road anomaly, and a route navigation path.

[0009] In other features, the method includes obtaining environmental context data associated with the host vehicle, the environmental context data including at least one of map data associated with a location of the host vehicle, road data associated with the location of the host vehicle, road anomaly data associated with the location of the host vehicle, route navigation data associated with the location of the host vehicle, and vehicle perception and vision data.

[0010] According to other features, determining the divergence index includes determining a piecewise localized lateral error position between the path history of the CIP target vehicle and the path history of the host vehicle, determining a piecewise localized heading error between the path history of the CIP target vehicle and the path history of the host vehicle, and combining the piecewise localized lateral error position and the piecewise localized heading error to determine the divergence index based on the piecewise localized lateral error position and the piecewise localized heading error.

[0011] According to other features, obtaining the path history of the CIP target vehicle in response to an initial detection of the CIP target vehicle as a newly selected target vehicle includes establishing a virtual historical initial path of the CIP target vehicle using a systematic dynamically established backpropagation behind the CIP target vehicle.

[0012] In other features, the method includes determining current state variables of the host vehicle, the current state variables including at least one of a lateral velocity of the host vehicle, a longitudinal velocity of the host vehicle, a yaw rate of the host vehicle, and a driver input from at least one of a steering wheel or a pedal, and identifying an updated position of the host vehicle according to the current state variables.

[0013] According to other features, the method includes transforming the coordinates of the plurality of historical positions of the CIP target vehicle to generate an updated path history of the CIP target vehicle, transforming the coordinates of the plurality of historical positions of the host vehicle to generate an updated path history of the host vehicle, and comparing the updated path history of the CIP target vehicle with the updated path history of the host vehicle to determine an updated divergence index.

[0014] In other features, the method includes detecting a plurality of target vehicles in front of the host vehicle via the at least one vehicle object sensor of a host vehicle, tracking the movement of each of the plurality of target vehicles, and selecting one of the plurality of target vehicles as the CIP target vehicle based on the movement of each of the plurality of target vehicles.

[0015] According to other features, selecting one of the plurality of target vehicles as the CIP target vehicle includes determining a probabilistic forward path of the host vehicle and selecting one of the plurality of target vehicles with the lowest time to collision (TTC) with the host vehicle. According to other features, the at least one vehicle object sensor includes at least one of a camera, a laser, or a lidar sensor.

[0016] A vehicle control system for controlling automated acceleration, braking, and steering of vehicles includes at least one vehicle object sensor configured to detect a closest-in-path target vehicle (CIP target vehicle) ahead of a host vehicle, and a vehicle control module of the host vehicle, wherein the vehicle control module is configured to obtain a path history of the CIP target vehicle, the path history including a plurality of historical positions of the CIP target vehicle at specified time intervals, obtain a path history of the host vehicle, the path history of the host vehicle including a plurality of historical positions of the host vehicle at the specified time intervals, compare the path history of the CIP target vehicle with the path history of the host vehicle to determine a divergence index, and in response to the divergence index being less than a specified divergence threshold,to control the automated acceleration, braking, and steering of the host vehicle based at least in part on the tracked motion of the CIP target vehicle.

[0017] According to other features, the vehicle control module is configured, in response to the divergence index being greater than the specified divergence threshold, to cease tracking the CIP target vehicle, wherein the automated acceleration and braking of the host vehicle are not controlled based on the tracked motion of the CIP target vehicle.

[0018] According to other features, the vehicle control module is configured, subsequent to ceasing to track the CIP target vehicle, to identify a second vehicle ahead of the host vehicle, the second vehicle being different from the CIP target vehicle, and to obtain an initial path history of the second vehicle, the initial path history of the second vehicle including a plurality of historical positions of the second vehicle at the specified time intervals.

[0019] In other features, the vehicle control module is configured to calculate the specified divergence threshold according to one or more gain parameters, wherein the one or more gain parameters include at least one of a highway type, a city street type, a presence of a road anomaly, and a route navigation path.

[0020] According to other features, the vehicle control module is configured to obtain environmental context data associated with the host vehicle, the environmental context data including at least one of map data associated with a location of the host vehicle, road data associated with the location of the host vehicle, road anomaly data associated with the location of the host vehicle, route navigation data associated with the location of the host vehicle, and vehicle perception and vision data.

[0021] According to other features, determining the divergence index includes determining a piecewise localized lateral position error between the path history of the CIP target vehicle and the path history of the host vehicle, determining a piecewise localized heading error between the path history of the CIP target vehicle and the path history of the host vehicle, and combining the piecewise localized lateral position error and the piecewise localized heading error to determine the divergence index based on the piecewise localized lateral error position and the piecewise localized heading error.

[0022] According to other features, the vehicle control module is configured to determine current state variables of the host vehicle, the current state variables including at least one of a lateral velocity of the host vehicle, a longitudinal velocity of the host vehicle, a yaw rate of the host vehicle, and a driver input from at least one of a steering wheel and a pedal, and to identify an updated position of the host vehicle according to the current state variables.

[0023] Further areas of applicability of the present disclosure will become apparent from the detailed description, claims, and drawings. The detailed description and specific examples are for purposes of illustration only and are not intended to limit the scope of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present disclosure will be more fully understood from the detailed description and the accompanying drawings, in which: Fig. 1 is a graphical representation of an exemplary vehicle including object detectors configured to track a closest in path (CIP) vehicle in front of a host vehicle; Fig. 2 is a block diagram of an exemplary CIP target vehicle tracking module including a vehicle path history comparison; Fig. 3 is a graphical representation illustrating exemplary path histories of a host vehicle and a CIP target vehicle; Fig. 4 is a graphical representation illustrating an example of a CIP target vehicle traveling around a roundabout; Fig. 5 is a flowchart illustrating an exemplary process for tracking a CIP target vehicle that includes a vehicle path history comparison; and Fig. 6 is a flowchart illustrating an exemplary process for calculating a divergence index for a stop determination of a CIP target vehicle.

[0025] Reference numerals may be reused in the drawings to identify similar and / or identical elements. DETAILED DESCRIPTION

[0026] Some vehicles incorporate object tracking sensors that identify and track a closest-in-path (CIP) vehicle ahead of a host vehicle. During complex transition maneuvers, a target vehicle may deviate from a linear projection of a host vehicle path. In some situations, an object tracking sensor may be unable to appropriately select a CIP vehicle, maintain the CIP vehicle, and abandon the CIP vehicle due to the rapidly changing conditions of the host vehicle and target vehicle. This can result in suboptimal target object tracking, resulting in poor longitudinal control performance during, for example, automated collision-impending acceleration and braking (CIB), adaptive cruise control (ACC), etc.

[0027] According to some exemplary embodiments described herein, a vehicle control module is configured to implement a path divergence rationalization mechanism (which may include an environmentally aware leader-follower quantification metric) to efficiently generate a track memory of a target object to properly maintain and abandon a CIP target vehicle. The vehicle control module may be configured to establish a backward-propagated virtual historical path for a CIP target vehicle without any prior track that has recorded a position history of the CIP target vehicle. The virtual historical path may be established according to data obtained regarding environmental constraints (e.g., road conditions) at a location of the CIP target vehicle.

[0028] According to various implementations, the vehicle control module is configured to execute a mathematical method that identifies a true CIP target vehicle during complex transition maneuvers, such as a roundabout and sharp turns, where other methods, such as a constant circle and model-based vehicle path prediction, fail to operate properly. Because the current state of the host vehicle and target vehicles is highly unpredictable, during these maneuvers, a predicted path of the host vehicle could deviate sufficiently from a CIP target vehicle to temporarily result in an inappropriate abandonment of the CIP target vehicle and a subsequent inappropriate retrieval of the CIP target vehicle.

[0029] The constant entry and exit of a CIP target vehicle through a curve can produce sudden braking and acceleration sequences, such as those provided by features such as automated collision avoidance (CIB), which is uncomfortable for a driver and raises safety concerns. Some embodiments described herein include vehicle control modules configured to obtain path histories of the host vehicle and the target vehicles to perform enhanced identification and stopping of CIP target vehicles (e.g., compared to only a vehicle path prediction model).

[0030] For example, a vehicle control module may be configured to use a divergence index to rationalize the path histories of the host vehicle and target objects to improve the performance of a follow-and-hold algorithm for CIP target vehicles in various driving scenarios. The vehicle control module may use a lead-follow metric to quantify the deviation of the host vehicle and target objects according to the obtained path histories.

[0031] A vehicle control module may be configured to establish a virtual path history for target objects that appear unexpectedly, such as the approach of a target vehicle that has been stopped at a red light for a while. The virtual path history for the target object may be established based at least in part on environmental constraints, such as lane markings, road types, etc.

[0032] In Fig. 1, a vehicle 10 includes the front wheels 12 and the rear wheels 13. In Fig. 1, a drive unit 14 selectively delivers torque to the front wheels 12 and / or the rear wheels 13 via drivelines 16 and 18, respectively. The vehicle 10 may include various types of drive units. For example, the vehicle may be an electric vehicle, such as a battery electric vehicle (BEV), a hybrid vehicle or a fuel cell vehicle, a vehicle including an internal combustion engine (ICE), or another type of vehicle.

[0033] Some examples of the drive unit 14 may include any suitable electric motor, a power inverter, and a motor controller configured to control the power switches within the power inverter to adjust the motor speed and torque during propulsion and / or regeneration. A battery system provides or receives power to the electric motor of the drive unit 14 via the inverter during propulsion or regeneration.

[0034] While the vehicle 10 in Fig. 1 includes a drive unit 14, the vehicle 10 may have other configurations. For example, two separate drive units may drive the front wheels 12 and the rear wheels 13, one or more individual drive units may drive individual wheels, etc. As can be appreciated, other vehicle configurations and / or other drive units may be used.

[0035] The vehicle control module 20 may be configured to control the operation of one or more vehicle components, such as the drive unit 14 (e.g., by commanding torque settings of an electric motor of the drive unit 14). The vehicle control module 20 may receive inputs to control components of the vehicle, such as signals received from a steering wheel, an accelerator pedal, etc. The vehicle control module 20 may monitor the vehicle's telematics for safety purposes, such as vehicle speed, vehicle location, vehicle braking and acceleration, etc.

[0036] The vehicle control module 20 may receive signals from any suitable components for monitoring one or more aspects of the vehicle, including one or more vehicle sensors (such as cameras, microphones, pressure sensors, wheel position sensors, location sensors such as Global Positioning System (GPS) antennas, etc.). Some sensors may be configured to monitor the vehicle's current motion, vehicle acceleration, steering torque, etc.

[0037] As in Fig. 1, the vehicle 10 includes an optional rear object detector 24, an optional front object detector 26, and an optional side object detector 28. According to various implementations, the vehicle 10 may include more or fewer (or none) of any of these optional sensors. Each object sensor may include any suitable camera, laser, lidar sensor, etc., used to detect objects around the vehicle 10.

[0038] According to some example embodiments, a vehicle object detector may be configured to detect a closest in path vehicle (CIPV) (e.g., another vehicle ahead of a current travel path of the vehicle 10), a vulnerable road user (VRU) (e.g., a pedestrian or cyclist), etc. The vehicle control module 20 may be configured to control the movement of the vehicle 10 based on a detected CIP target vehicle, such as by increasing or decreasing the automated acceleration of the vehicle 10 (e.g., via adaptive cruise control), automatically applying the brakes of the vehicle 10 (e.g., in response to a collision braking event), controlling the automated lateral movement of the host vehicle via one or more steering actuators, etc.

[0039] The vehicle control module 20 may communicate with another device via a wireless communication interface, which may include one or more wireless antennas for transmitting and / or receiving wireless communication signals. The wireless communication interface may communicate, for example, via any suitable wireless communication protocols, including, but not limited to, vehicle-to-everything (V2X) communication, Wi-Fi communication, wireless wide area network (WAN) communication, cellular communication, personal area network (PAN) communication, short-range wireless communication (e.g., Bluetooth), etc. The wireless communication interface may communicate with a remote computing device via one or more wireless and / or wired networks.With respect to vehicle-to-vehicle (V2X) communication, the vehicle 10 may include one or more V2X transceivers (e.g., V2X signal transmitting and / or receiving antennas).

[0040] The vehicle 10 also includes a user interface. The user interface may include any suitable displays (such as on an instrument panel, console, or elsewhere), a touchscreen or other input device, speakers for generating sound, etc.

[0041] Fig. Figure 2 is a block diagram of an exemplary CIP target vehicle tracking module 100 that includes a vehicle path history comparison. The CIP target vehicle tracking module 100 may be implemented, for example, by the vehicle control module 20. Fig. 1 using the vehicle detection sensor data from the front object detector 26.

[0042] The CIP target vehicle tracking module 100 may receive or obtain environmental context extraction data 102, such as map information near a location of the vehicle 10, road information near a location of the vehicle 10, road anomaly information (e.g., road construction) near a location of the vehicle 10, navigation information regarding a planned route of the vehicle 10, etc.

[0043] The vehicle state measurement / estimation 104 may include any suitable parameters indicating a current position of the host vehicle, a current movement of the host vehicle, etc. For example, the vehicle control module may be configured to determine the state parameters of the host vehicle, such as: This module provides the state variables of the host vehicle, such as the lateral velocity v y , the longitudinal velocity v x , the yaw rate ω z, driver inputs from at least one steering wheel or pedal, etc.

[0044] The probabilistic vehicle path prediction 106 may include any suitable algorithm for estimating a future travel path of the host vehicle. For example, a projected path of the host vehicle may be used to select a CIP target from multiple detected objects, such as selecting a target vehicle on the projected path of the host vehicle that has the lowest time to collision (TTC).

[0045] The object categorization and filtering 108 can classify objects detected, for example, by the front object detector 26, such as identifying other vehicles, stationary objects on the roadside, pedestrians, cyclists, etc.

[0046] The initial object path setup 110 will be further described below with respect to Fig. 3. For example, a path history of a target vehicle object may be backpropagated when no path history is available (such as when the host vehicle is approaching a target vehicle that has been stopped at a red light for some time). The path history may be backpropagated using one or more road constraints, such as a centerline or the shoulder of the road. According to some example embodiments, a virtual historical initial path of the CIP target vehicle may be constructed using systematic, dynamically constructed backpropagation behind the CIP target vehicle.

[0047] For example, using a vision capability of the front object detector 26 (or other suitable forward-facing camera), a vehicle control module may estimate a centerline of a current travel lane of the host vehicle using a third-order polynomial: yp=c0+c1x+c2x2+c3x3

[0048] To generate an initial path history of a target vehicle object (e.g., a newly selected target vehicle or a "never before seen" CIP target vehicle), the vehicle control module can assume that the target vehicle has previously followed the road, so that the initial path history has the same coefficients as the centerline c0, c1, and c2, except for c3. Assuming that the target vehicle object follows the path but with a different lateral position, c3 can be calculated as follows: c3op=yo−(c0+c1xo+c2x02)xo3

[0049] Using this polynomial, the transverse positions of the desired points on the initial path of the object are given as: [Xo(k)Yo(k)] generated.

[0050] For the host vehicle, the initial path can be assumed to be straight.

[0051] Fig. 3 is a graphical representation illustrating exemplary path histories of a host vehicle 302 and a CIP target vehicle 304. As in Fig. 3, the host vehicle 302 travels along a path history 306 that includes multiple positions of the host vehicle 302 corresponding to different time intervals.

[0052] In the example after Fig. 3, the host vehicle 302 has a current position at a time {k}, having previously been at a position 307 at a time {k - 1}. Fig. 3 also illustrates an example position of the host vehicle 302 at a time {k - 2}.

[0053] Similarly, the CIP target vehicle 304 has a path history 308 that includes the positions at a time {k}, an earlier time {k - 1}, and another earlier time {k - 2}. Because the CIP target vehicle 304 is ahead of the host vehicle 302, the path history 308 of the CIP target vehicle 304 is ahead of the host vehicle 302 in time. The fixed time intervals can be any suitable period, such as every 100 milliseconds, every 500 ms, every second, etc.

[0054] The vehicle control module may be configured to record the positions of the vehicles along the path histories in a longitudinal direction 310 (x0(k)) and a lateral direction 312 (y0(k)). The values ​​of the longitudinal direction 310 and the lateral direction 312 may be compared for each vehicle at each time interval to determine whether the host vehicle 302 is traveling the same path that the CIP target vehicle 304 has already traveled.

[0055] Again in Fig. 2, the path history module 112 may perform several functions, such as a path transformation 114, a vehicle odometry 116, and a history buffer 118. The path history module 112 may be configured to store the path histories of the host vehicle and a target vehicle object and to transform the path histories into coordinates based on a location of the host vehicle in response to a trigger (such as fixed time intervals, etc.).

[0056] The history buffer 118 may store a position of the target vehicle object and provide updates to determine a path of the target vehicle object using any suitable algorithm, such as: [Xo(k)Yo(k)]=[0m×1Im×m0m×10m×m001×m001×m0m×10m×m0m×1Im×m001×m001×m][Xo(k−1)Yo(k−1)]+[0m×10m×1100m×10m×101][xo(k)yo(k)] to generate, where X o and Y o Vectors are those that contain the history of the longitudinal and transverse positions of the target object, while xo and y o its current longitudinal and transverse positions.

[0057] The previous position of the host vehicle can be stored and recalculated according to a calibratable time using any suitable algorithm, such as: [Xh(k)Yh(k)]=[0m×1Im×m0m×10m×m001×m001×m0m×10m×m0m×1Im×m001×m001×m][Xh(k)Yh(k)] updated to generate a path history of the host vehicle, where X h and Y h Vectors that contain the history of the longitudinal and lateral positions of the host vehicle.

[0058] Since the positions of the target vehicle object and the host vehicle are stored and updated over time, the positions can be transmitted according to a new position of the host vehicle. For example, the vehicle odometry 116 can use any suitable algorithm, such as: xh(k)=xh(k−1)+dt(vx(k)cos(ψh(k−1))−vy(k)sin(ψh(k−1))) yh(k)=yh(k−1)+dt(vx(k)sin(ψh(k−1))+vy(k)cos(ψh(k−1))) ψh(k)=ψh(k−1)+dtωz

[0059] The path transformation 114 can use any suitable algorithm, such as: [xn(k)yn(k)]=R[x(k)−xh(k)y(k)−yh(k)] where ℜ is defined as a rotation matrix: R=[cos ψh(k)sin ψh(k)−sin ψh(k)cos ψh(k)]

[0060] For each set of X / Y vectors, a third-order polynomial can be fitted. As a result, each path history (e.g., the path history of the target vehicle object and the path history of the host vehicle) can be represented by four coefficients (Ci=[c0i,c1i,c2i,c3i]) According to some exemplary embodiments, only the stored positions of the target vehicle object that are behind the host vehicle are used for the polynomial estimation, as shown in Fig. 4. The coefficients can be determined, for example, according to a function F that calculates the polynomial, where (i) can be the host vehicle or the target vehicle: Ci=F(X,Y)

[0061] Fig. 4 is a graphical representation illustrating an example of a CIP target vehicle 404 traveling around a roundabout. As shown in Fig. 4, the CIP target vehicle 404 includes a path history 414 that includes a plurality of positions 405 that indicate the locations of the CIP target vehicle 404 at different times (e.g., at fixed time intervals).

[0062] The CIP target vehicle 404 also includes a path history portion 412 located behind a current location of the host vehicle 402 and indicating previous positions 408 of the CIP target vehicle 404 that occurred behind the current location of the host vehicle 402.

[0063] Fig. 4 also illustrates a path history 410 of the host vehicle 402, which includes previous positions 406 of the host vehicle. According to some example embodiments, the vehicle control module may be configured to compare the path history 410 of the host vehicle 402 with the path history portion 412 of the CIP target vehicle 404 to determine whether the host vehicle 402 is traveling the same path as previously traveled by the CIP target vehicle 404, or how far the path histories differ.

[0064] Again in Fig. 2, for example, a quantification metric 120 and a path rationalization 122 may be used to determine how much the path histories of the host vehicle and the CIP target vehicle diverge from each other to determine whether the host vehicle should continue to track a target vehicle as a CIP target vehicle. As an example, if the host vehicle follows a nearly identical path history as previously traveled by the CIP target vehicle, the vehicle control module may make a determination 124 to continue to hold the CIP target vehicle event if it makes a sharp turn based on a probability that the host vehicle follows the same sharp turn path as the CIP target vehicle.

[0065] Path rationalization 122 may be structured to facilitate determining whether to abandon or maintain a target vehicle as a CIP vehicle. For example, heading errors (e.g., piecewise localized heading errors) and lateral errors (e.g., piecewise localized lateral position errors) between the paths of the host vehicle and a target vehicle may be calculated, with the heading error and lateral error combined into a single divergence index value using specified weights. If the divergence index is greater than a threshold defined by quantification metric 120, the vehicle control module may abandon the previously tracked CIP target vehicle. Example equations for the lateral error (C0), heading error (C1), and divergence index (DI) may be as follows: ΔC0(k)=c0o(k)−c0h(k) ΔC1(k)=c1o(k)−c1h(k) DI=K0|ΔC0(k)|+K1|ΔC1(k)| be calculated.

[0066] If DI > DI0, the vehicle control module may be configured to abandon tracking of the target vehicle as a CIP vehicle. Otherwise, the vehicle control module may continue to maintain the target vehicle as the CIP vehicle.

[0067] The quantification metric 120 may use DI0 as a threshold to determine whether to hold or abandon a CIP target vehicle. An example equation for determining the threshold is: DI=D0ΔC0min+D1ΔC1min, where ΔC0min and ΔC1min are a lateral and heading error threshold, respectively. D0 and D1 are gains to adjust DI0, for example, according to a road type, road anomalies (such as construction), and a planned path of the host vehicle. According to some exemplary embodiments, the following equations may be used: D0=D0t×D0t×D0p D1=D1t×D1t×D1p where D twhich indicates the road types, such as highway or urban, D a indicates whether road anomalies exist, and D p specifies a planned path that the host vehicle will follow (e.g., as indicated by route navigation, vehicle perception and visibility data, etc.).

[0068] Fig. Figure 5 is a flowchart illustrating an exemplary process for tracking a CIP target vehicle that includes a vehicle path history comparison. Fig. The process illustrated in Figure 5 can be carried out, for example, by the vehicle control module 20 after Fig. 1. At 504, the process begins by detecting an object within a threshold distance from a host vehicle.

[0069] The controller may optionally receive environmental context data at 508. The control module is then configured to determine the host vehicle's state variables, such as the host vehicle's lateral and longitudinal motion, at 512. At 516, the controller identifies a closest in-path target vehicle ahead of the host vehicle.

[0070] At 520, the controller determines whether an initial object path is available for the target vehicle. If not, at 524, the controller establishes an initial target object path using a road centerline and backpropagation. After establishing the initial target object path, or if an initial object path is available (e.g., because the host vehicle has been following the target vehicle for a period of time), at 528, the controller obtains a path history for the host vehicle and for the target vehicle.

[0071] At 532, the controller transforms the path history coordinates according to a current position of the host vehicle. The vehicle control module is configured to determine at 536 whether to hold or abandon a current CIP target vehicle. Further details regarding the determination of whether to hold or abandon a current CIP target vehicle are described below with respect to Fig. 6 is discussed further.

[0072] The vehicle control module is configured to determine at 540 whether the CIP target vehicle should be held or abandoned. If control determines that the current CIP target vehicle should be held, control proceeds to 544 to continue controlling the acceleration and deceleration of the host vehicle according to the tracked motion of the CIP vehicle. Control then returns to 528 to obtain updated path histories for the host vehicle and the CIP target vehicle.

[0073] If control determines at 540 to abandon the CIP target vehicle, control proceeds to 548 to abandon the target vehicle from the CIP track. Control then returns to 516 to identify a different target vehicle object as the new CIP vehicle for the CIP track.

[0074] Fig. 6 is a flowchart illustrating an exemplary process for calculating a divergence index for a stop determination of a CIP target vehicle. Fig. The process illustrated in Figure 5 can be carried out, for example, by the vehicle control module 20 after Fig. 1. At 604, the process begins by obtaining road type parameters, road anomaly parameters, and planned vehicle path parameters.

[0075] At 608, the controller is configured to set a divergence threshold according to the obtained parameters. The vehicle control module is then configured to obtain a cross-track error between the host vehicle's path history and the target vehicle's path history, and a heading error between the host vehicle's path history and the target vehicle's path history.

[0076] At 616, the vehicle control module is configured to determine a divergence index based on the lateral and heading errors. If at 620, the divergence index is greater than the divergence threshold, control proceeds to 624 to release the target vehicle from CIP vehicle tracking. If at 620, control determines that the divergence index is not greater than the divergence threshold, control maintains the target vehicle as the tracked CIP vehicle at 628.

[0077] The foregoing description is merely illustrative and is in no way intended to limit the disclosure, its application, or uses. The broad teachings of the disclosure may be implemented in various forms. Therefore, while this disclosure contains specific examples, the true scope of the disclosure should not be so limited because other modifications will become apparent upon a study of the drawings, the specification, and the following claims. It should be recognized that one or more steps within a method may be performed in different orders (or simultaneously) without altering the principles of the present disclosure.Furthermore, although each of the embodiments has been described above with certain features, one or more of those features described with respect to any embodiment of the disclosure may be implemented in any of the other embodiments and / or combined with the features of any of the other embodiments, even if that combination is not explicitly described. In other words, the described embodiments are not mutually exclusive, and permutations of one or more embodiments with each other remain within the scope of this disclosure.

[0078] Spatial and functional relationships between elements (e.g., between modules, circuit elements, semiconductor layers, etc.) are described using various terms, including "connected," "engaged," "coupled," "adjacent," "beside," "on top of," "over," "below," and "disposed." When a relationship between a first and a second element is described in the above disclosure, that relationship may be a direct relationship, with no other intervening elements present between the first and second elements, but may also be an indirect relationship, with one or more intervening elements (either spatial or functional) present between the first and second elements if not explicitly described as "direct."As used herein, the phrase at least one of A, B, and C should be construed to mean a logical (A OR B OR C) using a non-exclusive logical OR, and should not be construed to mean "at least one of A, at least one of B, and at least one of C."

[0079] In the figures, the direction of an arrow, as indicated by the arrowhead, generally demonstrates the flow of information (such as data or instructions) of interest to the illustration. For example, if element A and element B exchange different information, but the information transmitted from element A to element B is relevant to the illustration, the arrow may point from element A to element B. This unidirectional arrow does not imply that no other information is transmitted from element B to element A. Further, for the information sent from element A to element B, element B may send requests for or acknowledgments of receipt of the information to element A.

[0080] In this application, including the definitions below, the term "module" or the term "controller" may be replaced by the term "circuit". The term "module" may refer to, be part of, or contain: an application-specific integrated circuit (ASIC); a digital, analog, or mixed analog / digital discrete circuit; a digital, analog, or mixed analog / digital integrated circuit; a combinational logic circuit; a field-programmable gate array (FPGA); a processor circuit (shared, dedicated, or group) that executes code; a memory circuit (shared, dedicated, or group) that stores the code executed by the processor circuit; other suitable hardware components that provide the described functionality; or a combination of some or all of the above, such as in a system on a chip.

[0081] The module may include one or more interface circuits. According to some examples, the interface circuits may include wired or wireless interfaces connected to a local area network (LAN), the Internet, a wide area network (WAN), or combinations thereof. The functionality of any given module of the present disclosure may be distributed among multiple modules connected via interface circuits. For example, multiple modules may enable load balancing. According to another example, a server module (also known as a remote or cloud module) may perform some functionality on behalf of a client module.

[0082] The term code, as used above, may include software, firmware, and / or microcode, and may refer to programs, routines, functions, classes, data structures, and / or objects. The term shared processor circuitry includes a single processor circuit that executes some or all of the code from multiple modules. The term group processor circuitry includes a processor circuit that, in combination with additional processor circuitry, executes some or all of the code from one or more modules. References to multiple processor circuits include multiple processor circuits on discrete dies, multiple processor circuits on a single die, multiple cores of a single processor circuit, multiple threads of a single processor circuit, or a combination of the above.The term shared memory circuit refers to a single memory circuit that stores some or all of the code from multiple modules. The term group memory circuit refers to a memory circuit that, in combination with additional memories, stores some or all of the code from one or more modules.

[0083] The term memory circuit is a subset of the term computer-readable medium. The term computer-readable medium, as used herein, does not include transient electrical or electromagnetic signals that propagate through a medium (such as on a carrier wave); the term computer-readable medium can therefore be considered tangible and non-transitory. Non-limiting examples of a non-transitory, tangible computer-readable medium include non-volatile memory circuits (such as a flash memory circuit, an erasable programmable read-only memory circuit, or a mask read-only memory circuit), volatile memory circuits (such as a static random access memory circuit or a dynamic random access memory circuit), magnetic storage media (such as an analog or digital magnetic tape or a hard disk drive), and optical storage media (such asa CD, a DVD or a Blu-ray Disc).

[0084] The devices and methods described in this application may be implemented partially or entirely by a special-purpose computer created by configuring a general-purpose computer to perform one or more specific functions embodied in computer programs. The function blocks, flowchart components, and other elements described above serve as software specifications that can be translated into computer programs through the routine work of a trained technician or programmer.

[0085] The computer programs contain processor-executable instructions stored on at least one non-transitory, tangible, computer-readable medium. The computer programs may also contain or rely on stored data. The computer programs may include a basic input / output system (BIOS) that interacts with the hardware of the special-purpose computer, device drivers that interact with special-purpose devices of the special-purpose computer, one or more operating systems, user applications, background services, background applications, etc.

[0086] The computer programs may contain: (i) descriptive text to be parsed, such as: B. HTML (Hypertext Markup Language), XML (Extensible Markup Language) or JSON (JavaScript Object Notation), (ii) assembly code, (iii) object code generated from source code by a compiler, (iv) source code for execution by an interpreter, (v) source code for compilation and execution by a just-in-time compiler, etc. By way of example only, the source code may be written using the syntax of languages ​​including C, C++, C#, Objective-C, Swift, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl, Pascal, Curl, OCaml, Javascript®, HTML5 (Hypertext Markup Language, 5th Revision), Ada, ASP (Active Server Pages), PHP (PHP: Hypertext Preprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, Visual Basic®, Lua, MATLAB, SIMULINK and Python®. legend

[0087] In the drawings, N stands for No and Y stands for Yes.

Claims

[1] A method for controlling automated acceleration and braking of vehicles, the method comprising: Detecting, via at least one vehicle object sensor of a host vehicle, a closest in-path target vehicle (CIP target vehicle) in front of the host vehicle; Obtaining a path history of the CIP target vehicle, the path history containing a plurality of historical positions of the CIP target vehicle at specified time intervals; Obtaining a path history of the host vehicle, wherein the path history of the host vehicle includes a plurality of historical positions of the host vehicle in the specified time intervals; Comparing the path history of the CIP target vehicle with the path history of the host vehicle to determine a divergence index; and in response to the divergence index being less than a specified divergence threshold, controlling automated acceleration and braking of the host vehicle based at least in part on the tracked motion of the CIP target vehicle. [2] The method of claim 1, further comprising, in response to the divergence index being greater than the specified divergence threshold, ceasing to track the CIP target vehicle, wherein the automated acceleration and braking of the host vehicle are not controlled based on the tracked motion of the CIP target vehicle. [3] The method of claim 2, further comprising, subsequent to ceasing to track the CIP target vehicle: Identifying a second vehicle ahead of the host vehicle, wherein the second vehicle is different from the CIP target vehicle; and Obtaining an initial path history of the second vehicle, wherein the initial path history of the second vehicle includes a plurality of historical positions of the second vehicle in the specified time intervals. [4] The method of claim 1, further comprising controlling automated lateral movement of the host vehicle based at least in part on the tracked movement of the CIP target vehicle via one or more steering actuators. [5] The method of claim 1, further comprising calculating the specified divergence threshold according to one or more gain parameters, wherein the one or more gain parameters include at least one of a highway type, a city street type, a presence of a road anomaly, and a route navigation path. [6] The method of claim 5, further comprising obtaining environmental context data associated with the host vehicle, wherein the environmental context data includes at least one of map data associated with a location of the host vehicle, road data associated with the location of the host vehicle, road anomaly data associated with the location of the host vehicle, route navigation data associated with the location of the host vehicle, and vehicle perception and vision data. [7] The method of claim 1, wherein determining the divergence index includes: Determining a piecewise localized cross-fault position between the path history of the CIP target vehicle and the path history of the host vehicle; Determining a piecewise localized course error between the path history of the CIP target vehicle and the path history of the host vehicle; and Combining the piecewise localized transverse fault position and the piecewise localized heading error to determine the divergence index based on the piecewise localized transverse fault position and the piecewise localized heading error. [8] The method of claim 1, wherein obtaining the path history of the CIP target vehicle in response to an initial detection of the CIP target vehicle as a newly selected target vehicle includes establishing a virtual historical initial path of the CIP target vehicle using a systematic dynamically established backpropagation behind the CIP target vehicle. [9] The method of claim 1, further comprising: Determining current state variables of the host vehicle, the current state variables including at least one of a lateral velocity of the host vehicle, a longitudinal velocity of the host vehicle, a yaw rate of the host vehicle, and a driver input from at least one of a steering wheel or a pedal; and Identify an updated position of the host vehicle according to the current state variables. [10] Vehicle control system for controlling automated acceleration and braking and steering of vehicles, the vehicle control system comprising: at least one vehicle object sensor configured to detect a closest in-path target vehicle (CIP target vehicle) in front of a host vehicle; and a vehicle control module of the host vehicle, wherein the vehicle control module is configured to obtain a path history of the CIP target vehicle, wherein the path history contains several historical positions of the CIP target vehicle at specified time intervals; to obtain a path history of the host vehicle, wherein the path history of the host vehicle contains a plurality of historical positions of the host vehicle in the specified time intervals; compare the path history of the CIP target vehicle with the path history of the host vehicle to determine a divergence index; and in response to the divergence index being less than a specified divergence threshold, control the automated acceleration and braking and steering of the host vehicle based at least in part on the tracked motion of the CIP target vehicle.

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