Vehicle control system for controlling automatic vehicle acceleration and braking

Abstract

The invention relates to a vehicle control system for controlling automatic vehicle acceleration and braking. A method for controlling automatic vehicle acceleration and braking includes: detecting, by at least one vehicle object sensor of a host vehicle, a nearest (CIP) target vehicle within a path in front of the host vehicle; obtaining a path history of the CIP target vehicle, wherein the path history comprises a plurality of historical positions of the CIP target vehicle in a specified time interval; obtaining a path history of the vehicle, wherein the path history of the vehicle comprises a plurality of historical positions of the vehicle in a specified time interval; comparing the path history of the CIP target vehicle with the path history of the vehicle to determine a difference index; and in response to the difference index being less than the specified difference threshold, controlling automatic acceleration and braking of the host vehicle based at least in part on the tracked motion of the CIP target vehicle.

Description

Technical Field

[0001] The present disclosure generally relates to a vehicle control system for efficiently detecting a closest-in-path (CIP) vehicle to enable control of automatic vehicle acceleration and braking, including distance control to the CIP vehicle. Background Art

[0002] The information provided in this section is intended to generally present the background of the disclosure. No admission is made, either explicitly or implicitly, that the work of the inventors named herein is prior art to the present disclosure, covering what is described in this section and aspects of this description that might not have been originally identified as prior art at the time of filing.

[0003] Some vehicles include object tracking sensors that identify the closest in-path (CIP) vehicle traveling in front of the host vehicle. During complex transient maneuvers, the target vehicle may deviate from the straight-line projection of the host vehicle's path. In some cases, the object tracking sensors may not correctly select, hold, and abandon the CIP vehicle due to the rapidly changing states of the host vehicle and the target vehicle. This may result in suboptimal target object tracking, which can lead to poor longitudinal control performance during processes such as automatic acceleration and collision imminent braking (CIB), adaptive cruise control (ACC), etc. Summary of the invention

[0004] A method for controlling automatic vehicle acceleration and braking comprises: detecting a closest in-path (CIP) target vehicle in front of a host vehicle via at least one vehicle object sensor of the host vehicle; obtaining a path history of the CIP target vehicle, the path history comprising a plurality of historical positions of the CIP target vehicle in a specified time interval; obtaining a path history of the host vehicle, the path history of the host vehicle comprising a plurality of historical positions of the host vehicle in the specified time interval; 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 automatic acceleration and braking of the host vehicle based at least in part on the tracked motion of the CIP target vehicle.

[0005] In other features, the method includes abandoning tracking of the CIP target vehicle in response to the difference index being greater than the specified difference threshold, wherein automatic acceleration and braking of a host vehicle are not controlled based on the tracked movement of the CIP target vehicle.

[0006] In other features, the method includes, after abandoning tracking of the CIP target vehicle, identifying a second vehicle ahead of the vehicle, the second vehicle being 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 in the specified time interval.

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

[0008] In other features, the method includes calculating the specified difference threshold based on one or more gain parameters, wherein the one or more gain parameters include at least one of a highway road type, an urban road type, the presence of a road anomaly, and a route navigation path.

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

[0010] In other features, determining the difference index includes: determining a segmented local lateral position error between the path history of the CIP target vehicle and the path history of the host vehicle; determining a segmented local heading error between the path history of the CIP target vehicle and the path history of the host vehicle; and combining the segmented local lateral position error and the segmented local heading error to determine the difference index based on the segmented local lateral position error and the segmented local heading error.

[0011] In other features, obtaining the path history of the CIP target vehicle includes: in response to initially detecting the CIP target vehicle as a newly selected target vehicle, constructing a virtual historical initial path of the CIP target vehicle using a systematic dynamically constructed back propagation behind the CIP target vehicle.

[0012] In other features, the method includes: determining a current state variable of the vehicle, wherein the current state variable includes at least one of: a lateral velocity of the vehicle, a longitudinal velocity of the vehicle, a yaw rate of the vehicle, and a driver input from at least one of a steering wheel or a pedal; and identifying an updated position of the vehicle based on the current state variable.

[0013] In other features, the method includes: transforming the coordinates of multiple historical positions of the CIP target vehicle to generate an updated path history of the CIP target vehicle; transforming the coordinates of multiple 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 difference index.

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

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

[0016] A vehicle control system for controlling automatic vehicle acceleration and braking and steering, comprising: at least one vehicle object sensor configured to detect a closest in-path (CIP) target vehicle in front of a host vehicle; and a vehicle control module of the host vehicle, the vehicle control module configured to: obtain a path history of the CIP target vehicle, the path history comprising a plurality of historical positions of the CIP target vehicle in a specified time interval; obtain a path history of the host vehicle, the path history of the host vehicle comprising a plurality of historical positions of the host vehicle in the specified time interval; compare the path history of the CIP target vehicle with the path history of the host vehicle to determine a difference index; and in response to the difference index being less than a specified difference threshold, control automatic acceleration, braking and steering of the host vehicle based at least in part on the tracked movement of the CIP target vehicle.

[0017] In other features, the vehicle control module is configured to abandon tracking of the CIP target vehicle in response to the difference index being greater than the specified difference threshold, wherein automatic acceleration and braking of the host vehicle are not controlled based on the tracked movement of the CIP target vehicle.

[0018] In other features, after abandoning tracking of the CIP target vehicle, the vehicle control module is configured to: identify a second vehicle in front of the vehicle, which is different from the CIP target vehicle; and obtain an initial path history of the second vehicle, the initial path history of the second vehicle including multiple historical positions of the second vehicle in the specified time interval.

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

[0020] In other features, the vehicle control module is configured to obtain environmental context data associated with the vehicle, wherein the environmental context data includes at least one of the following: map data associated with the location of the vehicle, road data associated with the location of the vehicle, road anomaly data associated with the location of the vehicle, route navigation data associated with the location of the vehicle, and vehicle perception and visual data.

[0021] In other features, determining the difference index includes: determining a segmented local lateral position error between the path history of the CIP target vehicle and the path history of the host vehicle; determining a segmented local heading error between the path history of the CIP target vehicle and the path history of the host vehicle; and combining the segmented local lateral position error and the segmented local heading error to determine the difference index based on the segmented local lateral position error and the segmented local heading error.

[0022] In other features, the vehicle control module is configured to: determine a current state variable of the vehicle, wherein the current state variable includes at least one of: a lateral velocity of the vehicle, a longitudinal velocity of the vehicle, a yaw rate of the vehicle, and a driver input from at least one of a steering wheel and a pedal; and identify an updated position of the vehicle based on the current state variable.

[0023] Other 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 illustrative purposes only and are not intended to limit the scope of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present disclosure will be more fully understood from the detailed description and accompanying drawings, in which:

[0025] Figure 1 is a schematic diagram of an example vehicle including an object detector configured to track a closest in-path (CIP) vehicle in front of the vehicle.

[0026] Figure 2 is a block diagram of an example CIP target vehicle tracking module including vehicle path history comparison.

[0027] Figure 3 is a schematic diagram showing example path histories of the host vehicle and the CIP target vehicle.

[0028] Figure 4 is a schematic diagram showing an example of a CIP target vehicle traveling around a roundabout.

[0029] Figure 5 is a flow chart depicting an example process for tracking a CIP target vehicle including vehicle path history comparison.

[0030] Figure 6 is a flow chart depicting an example process for calculating a variance index for making a CIP target vehicle maintenance determination.

[0031] In the drawings, reference numerals may be repeated to identify similar and / or identical elements. DETAILED DESCRIPTION

[0032] Some vehicles include object tracking sensors that identify and track the closest in-path (CIP) vehicle traveling in front of the host vehicle. During complex transient maneuvers, the target vehicle may deviate from the straight-line projection of the host vehicle's path. In some cases, the object tracking sensors may not correctly select, hold, and abandon the CIP vehicle due to the rapidly changing states of the host vehicle and the target vehicle. This may result in suboptimal target object tracking, which can lead to poor longitudinal control performance during processes such as automatic acceleration and collision imminent braking (CIB), adaptive cruise control (ACC), etc.

[0033] In some example embodiments described herein, the vehicle control module is configured to implement a path divergence rationalization mechanism (which may include a leader-follower quantification metric of environmental awareness) to effectively create a trajectory store of a target object in order to properly maintain and abandon a CIP target vehicle. The vehicle control module may be configured to construct a back-propagated virtual history path of the CIP target vehicle without any a priori trajectory of the recorded position history of the CIP target vehicle. The virtual history path may be constructed based on data obtained about environmental constraints (e.g., road conditions) at the location of the CIP target vehicle.

[0034] In various embodiments, the vehicle control module is configured to implement a mathematical method that identifies true CIP target vehicles in complex transient maneuvers such as roundabouts and sharp turns where other methods such as circle determination and model-based vehicle path prediction fail to operate correctly. In these maneuvers, due to the high unpredictability of the current states of the host vehicle and the target vehicle, the predicted path of the host vehicle may deviate far from the CIP target vehicle temporarily resulting in improper abandonment of the CIP target vehicle and improper pickup of the subsequent CIP target vehicle.

[0035] Constantly abandoning and picking up CIP target vehicles during turns may create abrupt braking and acceleration sequences, such as in features such as auto-acceleration and collision imminent braking (CIB), which can be uncomfortable for the driver and raise safety concerns. Some embodiments described herein include a vehicle control module configured to obtain path histories of host and target vehicles in order to perform improved CIP target vehicle identification and retention (e.g., compared to vehicle path prediction models alone).

[0036] For example, the vehicle control module can be configured to use the difference index to rationalize the path history of the host vehicle and the target object to improve the performance of the CIP target vehicle tracking and keeping algorithm in different driving scenarios. The vehicle control module can use the pilot following metric to quantify the deviation of the host vehicle from the target object based on the obtained path history.

[0037] The vehicle control module can be configured to construct a virtual path history of an unexpected target object, such as a target vehicle approaching a target vehicle that has been stopped at a red light for a period of time. The virtual path history of the target object can be constructed at least in part based on environmental constraints such as lane markings, road type, etc.

[0038] Reference now Figure 1 , the vehicle 10 includes front wheels 12 and rear wheels 13. Figure 1 , the drive unit 14 selectively outputs torque to the front wheels 12 and / or the rear wheels 13 through drive lines 16 and 18, respectively. The vehicle 10 may include different types of drive units. For example, the vehicle may be an electric vehicle, such as a pure electric vehicle (BEV), a hybrid vehicle or a fuel cell vehicle, a vehicle including an internal combustion engine (ICE), or other types of vehicles.

[0039] Some examples of drive unit 14 may include any suitable electric motor, power inverter, and engine controller configured to control power switches within the power inverter to adjust engine speed and torque during propulsion and / or regeneration. During propulsion or regeneration, the battery system provides power to or receives power from the electric motor of drive unit 14 through the power inverter.

[0040] Although Figure 1 The vehicle 10 includes one drive unit 14, but the vehicle 10 can have other configurations. For example, two separate drive units can drive the front wheels 12 and the rear wheels 13, one or more separate drive units can drive separate wheels, etc. It will be appreciated that other vehicle configurations and / or drive units can be used.

[0041] 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 manipulating the torque setting of the electric motor of the drive unit 14). The vehicle control module 20 may receive inputs for controlling vehicle components, such as signals received from a steering wheel, an accelerator pedal, etc. The vehicle control module 20 may monitor vehicle telematics for safety purposes, such as vehicle speed, vehicle location, vehicle braking and acceleration, etc.

[0042] The vehicle control module 20 may receive signals from any suitable component for monitoring one or more aspects of the vehicle, including one or more vehicle sensors such as cameras, microphones, pressure sensors, wheel position sensors, position sensors such as global positioning system (GPS) antennas, etc. Some sensors may be configured to monitor the current motion of the vehicle, the acceleration of the vehicle, steering torque, etc.

[0043] like Figure 1 As shown, the vehicle 10 includes an optional rear object detector 24, an optional front object detector 26, and an optional side object detector 28. In various embodiments, the vehicle 10 may include more or less (or none) of any of these optional sensors. Each object sensor may include any suitable camera, laser, lidar sensor, etc. for detecting objects around the vehicle 10.

[0044] In some example embodiments, the vehicle object detector may be configured to detect a closest in-path vehicle (CIPV) (e.g., another vehicle ahead of the 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 the detected CIP target vehicle, such as by increasing or decreasing automatic acceleration of the vehicle 10 (e.g., via adaptive cruise control), automatically applying brakes of the vehicle 10 (such as in response to a collision imminent braking event), controlling automatic lateral movement of the host vehicle via one or more steering actuators, etc.

[0045] The vehicle control module 20 can communicate with another device through a wireless communication interface, which may include one or more wireless antennas for transmitting and / or receiving wireless communication signals. For example, the wireless communication interface can communicate through any suitable wireless communication protocol, including but not limited to vehicle-to-everything (V2X) communication, Wi-Fi communication, wireless local area network (WAN) communication, cellular communication, personal area network (PAN) communication, short-range wireless communication (e.g., Bluetooth), etc. The wireless communication interface can communicate with a remote computing device through one or more wireless and / or wired networks. With respect to vehicle-to-everything (V2X) communication, the vehicle 10 may include one or more V2X transceivers (e.g., V2X signal transmitting and / or receiving antennas).

[0046] The vehicle 10 also includes a user interface. The user interface may include any suitable display (such as on a dashboard, console, or elsewhere), a touch screen or other input device, speakers for producing audio, and the like.

[0047] Figure 2 is a block diagram of an example CIP target vehicle tracking module 100 including vehicle path history comparison. The CIP target vehicle tracking module 100 may be comprised of, for example, Figure 1 The vehicle control module 20 uses vehicle detection sensor data from the forward object detector 26 to perform.

[0048] The CIP target vehicle tracking module 100 can receive or obtain environmental context extraction data 102, such as map information near the location of the vehicle 10, road information near the location of the vehicle 10, road anomaly information (e.g., road construction) near the location of the vehicle 10, navigation information about the planned route of the vehicle 10, etc.

[0049] The vehicle state measurement / estimation 104 may include any suitable parameters indicating the current position of the host vehicle, the current motion of the host vehicle, etc. For example, the vehicle control module may be configured to determine the host vehicle state parameters, such as the module providing a host vehicle state variable, such as the lateral velocity v y , longitudinal speed v x , yaw angular velocity ω z , driver input from at least one of the steering wheel or pedals, etc.

[0050] The probabilistic vehicle path prediction 106 may include any suitable algorithm for estimating the future travel path of the host vehicle. For example, the projected path of the host vehicle may be used to select a CIP target from a plurality of detected objects, such as selecting a target vehicle with a lowest time to collision (TTC) on the projected path of the host vehicle.

[0051] Object classification and filtering 108 may classify objects detected, for example, by the front object detector 26 , such as identifying other vehicles, stationary roadside objects, pedestrians, bicyclists, and the like.

[0052] Reference below Figure 3 The initial object path construction 110 is further described. For example, when the path history is not available (e.g., such as when the host vehicle is approaching a target vehicle that has been stopped at a red light for a period of time), the path history of the target vehicle object can be back-propagated. The path history can be back-propagated using one or more road constraints (such as the center line or shoulder line of the road). In some example embodiments, a virtual historical initial path of the CIP target vehicle can be constructed using back-propagation of a systematic, dynamically constructed back-propagation behind the CIP target vehicle.

[0053] For example, using the vision capabilities of the forward object detector 26 (or other suitable forward-facing camera), the vehicle control module may estimate the centerline of the host vehicle's current lane using a third-order polynomial:

[0054] y p =c0+c1x+c2x 2 +c3x 3

[0055] To generate an initial path history for a target vehicle object (e.g., a newly selected target vehicle or a "never seen before" CIP target vehicle), the vehicle control module may assume that the target vehicle has previously followed the road, such that the initial path history has the same coefficients c0, c1, and c2 as the centerline, except for c3. Assuming that the target vehicle object is following the path, but at a different lateral position, c3 may be calculated as follows:

[0056]

[0057] Using this polynomial, the lateral positions of the desired points on the object's initial path are generated as follows:

[0058]

[0059] For the vehicle, the initial path can be assumed to be a straight line.

[0060] Figure 3 3 is a schematic diagram showing an example path history of the host vehicle 302 and the CIP target vehicle 304. Figure 3 As shown, the host vehicle 302 travels along a path history 306 , which includes a plurality of positions of the host vehicle 302 corresponding to different time intervals.

[0061] exist Figure 3 In the example of , the vehicle 302 has a current position at time {k} and was previously at position 307 at time {k-1}. Figure 3 Also shown is an example position of host vehicle 302 at time {k-2}.

[0062] Similarly, CIP target vehicle 304 has path history 308, which includes positions at time {k}, previous time {k-1}, and previous time {k-2}. Because CIP target vehicle 304 is in front of host vehicle 302, path history 308 of CIP target vehicle 304 is ahead of host vehicle 302 in time. The fixed time interval can be any suitable time period, such as every 100 milliseconds, every 500ms, every second, etc.

[0063] The vehicle control module can be configured to record the position of the vehicle along the path history in the longitudinal direction 310 (x0(k)) and the lateral direction 312 (y0(k)). The values ​​of the longitudinal direction 310 and the lateral direction 312 can be compared for each vehicle at each time interval to determine whether the host vehicle 302 is traveling along the same path that the CIP target vehicle 304 has traveled.

[0064] Return again Figure 2 , the path history module 112 may perform several functions, such as path transformation 114, vehicle odometer 116, and history buffer 118. The path history module 112 may be configured to store path histories of the host vehicle and target vehicle objects, and transform the path histories into coordinates based on the location of the host vehicle in response to triggers (such as fixed time intervals, etc.).

[0065] The history buffer 118 may store the location of the target vehicle object and provide updates using any suitable algorithm to generate the path of the target vehicle object, such as:

[0066]

[0067] Among them, X o and Y o is a vector containing the vertical and horizontal position history of the target object, and x o and o are its current vertical and horizontal positions.

[0068] Any suitable algorithm may be used to store and update the past positions of the vehicle based on calibratable time to generate the vehicle path history, such as:

[0069]

[0070] Among them, X h and Y h is a vector containing the longitudinal and lateral position history of the vehicle.

[0071] As the locations of the target vehicle object and the host vehicle are stored and updated over time, these locations may be transformed according to the new location of the host vehicle. For example, the vehicle odometer 116 may use any suitable algorithm, such as:

[0072] x h (k) = x h (k-1)+dt(v x (k)cos(ψ h (k-1))-v y (k)sin(ψ h (k-1)))

[0073] y h(k) = y h (k-1)+dt(v x (k)sin(ψ h (k-1))+v y (k)cos(ψ h (k-1)))

[0074] ψ h (k) = ψ h (k-1)+dtω z

[0075] Path transformation 114 may use any suitable algorithm, such as:

[0076]

[0077] in, is defined as a rotation matrix:

[0078]

[0079] A third-order polynomial can be fitted for each set of X / Y vectors. As a result, each path history (e.g., target vehicle object path history and host vehicle path history) can be represented by four coefficients: In some example embodiments, Figure 4 As shown, only the stored position of the target vehicle object behind the host vehicle is used for polynomial estimation. For example, the coefficients can be determined based on the function F of the calculation polynomial, where (i) can be the host vehicle or the target object vehicle:

[0080] C i =F(X, Y)

[0081] Figure 4 4 is a schematic diagram showing an example of a CIP target vehicle 404 traveling around a roundabout. Figure 4 As shown, CIP target vehicle 404 has a path history 414 including a plurality of locations 405 indicating the locations of CIP target vehicle 404 at different points in time (eg, fixed time intervals).

[0082] The CIP target vehicle 404 also has a path history portion 412 behind the current position of the host vehicle 402 , which indicates a previous position 408 of the CIP target vehicle 404 that occurred behind the current position of the host vehicle 402 .

[0083] Figure 4Also shown is a path history 410 of the host vehicle 402, including the host vehicle's previous position 406. In some example embodiments, the vehicle control module may be configured to compare the path history 410 of the host vehicle 402 with a path history portion 412 of the CIP target vehicle 404 to determine whether the host vehicle 402 is traveling along the same path as a path previously traveled by the CIP target vehicle 404, or how much the path histories differ from each other.

[0084] For example, refer again to Figure 2 , the quantified metric 120 and the path rationalization 122 may be used to determine how much the path histories of the host vehicle and the CIP target vehicle differ from one another in order to determine whether the host vehicle should continue to track the target vehicle as the CIP target vehicle. As an example, if the host vehicle is following almost the exact same path history that the CIP target vehicle previously traveled, then even if the CIP target vehicle is making a sharp turn, the vehicle control module may make a determination 124 to continue to stay with the CIP target vehicle due to the likelihood that the host vehicle will follow the same sharp turn path as the CIP target vehicle.

[0085] The path rationalization 122 can be configured to assist in determining whether to abandon or maintain a target vehicle as a CIP vehicle. For example, a heading error (e.g., a piecewise localized heading error) and a lateral error (e.g., a piecewise localized lateral position error) can be calculated between the paths of the host vehicle and the target object vehicle, where the heading error and the lateral error are combined into a single difference index value using specified weights. If the difference index is greater than a threshold defined by the quantitative metric 120, the vehicle control module can abandon the previously tracked CIP target vehicle. Example equations for the lateral error (C0), the heading error (C1), and the difference index (DI) can be calculated according to the following formula:

[0086]

[0087]

[0088] DI=K0|ΔC0(k)+K1|ΔC1(k)|

[0089] If DI>DI0, the vehicle control module may be configured to abandon tracking the target vehicle as a CIP vehicle. Otherwise, the vehicle control module may continue to keep the target vehicle as a CIP vehicle.

[0090] The quantitative metric 120 may use DI0 as a threshold to determine whether to maintain or abandon the CIP target vehicle. An example equation for determining the threshold is:

[0091]

[0092] in, and are lateral and heading error thresholds. D0 and D1 are gains for adjusting D10 based on, for example, road type, road anomalies (such as construction areas), and the planned path of the vehicle. In some example embodiments, the following equation may be used:

[0093]

[0094]

[0095] Among them, D t Indicates the type of road such as expressway or city road, D a Indicates whether there is road abnormality, and D p Indicates the planned path along which the host vehicle will travel (e.g., as indicated by route guidance, vehicle perception and vision data, etc.).

[0096] Figure 5 is a flow chart depicting an example process for tracking a CIP target vehicle including vehicle path history comparison. Figure 5 The process shown can be accomplished by, for example Figure 1 The vehicle control module 20 is executed. At 504, the process begins by detecting an object within a threshold distance of the vehicle.

[0097] Control may optionally obtain environmental context data at 508. The control module is then configured to determine host vehicle state variables, such as lateral and longitudinal motion of the host vehicle, at 512. At 516, control identifies the nearest target vehicle in the path ahead of the host vehicle.

[0098] At 520, control determines whether an initial object path for the target vehicle is available. If not, control constructs an initial target object path using the road centerline and back propagation at 524. After constructing the initial target object path, or if the initial object path is available (e.g., because the host vehicle has been following the target vehicle for some time), control obtains path histories for the host vehicle and the target vehicle at 528.

[0099] At 532, the control transforms the path history coordinates according to the current vehicle position. At 536, the vehicle control module is configured to determine whether to maintain or abandon the current CIP target vehicle. Figure 6 Further details regarding determining whether to maintain or abandon the current CIP target vehicle are discussed further.

[0100] The vehicle control module is configured to determine whether the CIP target vehicle should be maintained or abandoned at 540. If control determines that the current CIP target vehicle should be maintained, control proceeds to 544 to continue controlling the acceleration and braking of the host vehicle based on 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.

[0101] If control determines at 540 to abandon the CIP target vehicle, control proceeds to 548 to abandon CIP tracking of the target vehicle. Control then returns to 516 to identify a different target vehicle object as a new CIP vehicle for CIP tracking.

[0102] Figure 6 is a flow chart depicting an example process for calculating a variance index for making a CIP target vehicle maintenance determination. Figure 6 The process shown can be accomplished by, for example Figure 1 The vehicle control module 20 is executed. At 604, the process begins by obtaining road type parameters, road anomaly parameters, and planned vehicle path parameters.

[0103] At 608 , the controller is configured to set a difference threshold based on the obtained parameters. At 612 , the vehicle control module is then configured to obtain a lateral error between the host vehicle path history and the target vehicle path history and a heading error between the host vehicle path history and the target vehicle path history.

[0104] At 616, the vehicle control module is configured to determine a difference index based on the lateral and heading errors. If the difference index is greater than a difference threshold at 620, control proceeds to 624 to abandon CIP vehicle tracking of the target vehicle. If control determines at 620 that the difference index is not greater than the difference threshold, control maintains the target vehicle as a tracked CIP vehicle at 628.

[0105] The foregoing description is merely illustrative in nature and is in no way intended to limit the present disclosure, its application or use. The broad teachings of the present disclosure can be implemented in various forms. Therefore, although the present disclosure includes specific examples, the true scope of the present disclosure should not be limited thereto, because after studying the drawings, the specification and the appended claims, other modifications will become apparent. It should be understood that one or more steps in the method can be performed in different orders (or simultaneously) without changing the principles of the present disclosure. In addition, although each embodiment is described above as having certain features, any one or more of those features described with respect to any embodiment of the present disclosure can be implemented in the features of any other embodiment and / or combined with the features of any other embodiment, even if the combination is not explicitly described. In other words, the embodiments described are not mutually exclusive, and the permutations and combinations of one or more embodiments are still within the scope of the present disclosure.

[0106] Spatial and functional relationships between elements (e.g., relationships between modules, circuit elements, semiconductor layers, etc.) are described using various terms, including "connected," "engaged," "coupled," "adjacent," "next to," "on top of," "above," "below," and "arranged." Unless explicitly described as "directly," when describing a relationship between a first and a second element in the above disclosure, the relationship may be a direct relationship in which no other intervening elements exist between the first and second elements, but may also be an indirect relationship in which one or more intervening elements exist (spatially or functionally) between the first and second elements. As used herein, the phrase "at least one of A, B, and C" should be interpreted to mean a logical (A or B or C) using a non-exclusive logical "or," and should not be interpreted to mean "at least one of A, at least one of B, and at least one of C."

[0107] In each figure, the direction of the arrow indicated by the arrow generally shows the information flow (such as data or instructions) that the diagram is concerned with. For example, when component A and component B exchange various information but the information transmitted from component A to component B is relevant to the diagram, the arrow can be directed from component A to component B. This unidirectional arrow does not mean that no other information is transmitted from component B to component A. In addition, for information sent from component A to component B, component B can send an information request to component A or receive an information confirmation.

[0108] In this application, including the definitions below, the term "module" or the term "controller" may be replaced with the term "circuit". The term "module" may refer to a part of or include an application-specific integrated circuit (ASIC); a digital, analog or mixed digital-analog discrete circuit; a digital, analog or mixed digital-analog 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 code executed by the processor circuit; other suitable hardware components that provide the functionality; or a combination of some or all of the above, such as in a system on a chip.

[0109] The module may include one or more interface circuits. In some examples, the interface circuit may include a wired or wireless interface connected to a local area network, the Internet, a wide area network, or a combination thereof. The functionality of any given module of the present disclosure may be distributed among multiple modules connected by interface circuits. For example, multiple modules may allow for load balancing. In another example, a server (also referred to as a remote or cloud) module may perform some functions on behalf of a client module.

[0110] The term "code" as used above may include software, firmware and / or microcode, and may refer to a program, a routine, a function, a class, a data structure and / or an object. The term "shared processor circuit" includes a single processor circuit that executes some or all code from multiple modules. The term "group processor circuit" includes a processor circuit that executes some or all code from one or more modules in conjunction with another processor circuit. 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" includes a single memory circuit that stores some or all code from multiple modules. The term "group memory circuit" includes a memory circuit that stores some or all code from one or more modules in conjunction with another memory.

[0111] 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 propagated through a medium (e.g., a carrier wave); therefore, the term "computer-readable medium" may be considered to be tangible and non-transient. Non-limiting examples of non-transient tangible computer-readable media are non-volatile memory circuits (such as flash memory circuits, erasable programmable read-only memory circuits, or mask read-only memory circuits), volatile memory circuits (such as static random access memory circuits or dynamic random access memory circuits), magnetic storage media (such as analog or digital magnetic tape or hard disk drives), and optical storage media (such as CDs, DVDs, or Blu-ray discs).

[0112] The apparatus and methods described in this application may be implemented in part or in whole by a special purpose computer created by configuring a general purpose computer to perform one or more specific functions embodied in a computer program. The functional blocks, flow chart components and other elements described above act as software specifications that can be translated into a computer program by the routine work of a skilled technician or programmer.

[0113] The computer program includes processor executable instructions stored on at least one non-transitory tangible computer readable medium. The computer program may also include or rely on stored data. The computer program may include a basic input / output system (BIOS) that interacts with the hardware of the special-purpose computer, device drivers that interact with specific devices of the special-purpose computer, one or more operating systems, user applications, background services, background applications, etc.

[0114] A computer program may include: (i) descriptive text to be parsed, such as HTML (Hypertext Markup Language), XML (Extensible Markup Language), or JSON (JavaScript Object Notation), (ii) assembly code, (iii) object code generated by a compiler from source code, (iv) source code executed by an interpreter, (v) source code compiled and executed by a just-in-time compiler, etc. By way of example only, source code may be written using syntax from a language including: C, C++, C#, Objective-C, Swift, Haskell, Go, SQL, R, Lisp, Fortran, Perl, Pascal, Curl, OCaml, HTML5 (Hypertext Markup Language Version 5), Ada, ASP (Active Server Pages), PHP (PHP: Hypertext Preprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Visual Lua, MATLAB, SIMULINK, and

Claims

1. A method for controlling automatic vehicle acceleration and braking, the method comprising: detecting, by at least one vehicle object sensor of the host vehicle, a closest in-path (CIP) target vehicle in front of the host vehicle; Obtaining a path history of a CIP target vehicle, the path history comprising a plurality of historical positions of the CIP target vehicle in a specified time interval; Obtaining a path history of the vehicle, the path history of the vehicle including a plurality of historical positions of the vehicle in the specified time interval; comparing the path history of the CIP target vehicle with the path history of the host vehicle to determine a difference index; as well as In response to the difference index being less than a specified difference threshold, automatic acceleration and braking of a host vehicle are controlled based at least in part on the tracked motion of the CIP target vehicle.

2. The method according to claim 1, further comprising: In response to the difference index being greater than the specified difference threshold, tracking of the CIP target vehicle is abandoned, wherein automatic acceleration and braking of a host vehicle are not controlled based on the tracked movement of the CIP target vehicle.

3. The method according to claim 2, further comprising, after giving up tracking the CIP target vehicle: Identifying a second vehicle ahead of the host vehicle, the second vehicle being different from the CIP target vehicle; as well as An initial path history of the second vehicle is obtained, wherein the initial path history of the second vehicle includes a plurality of historical positions of the second vehicle in the specified time interval.

4. The method of claim 1 , further comprising controlling, via one or more steering actuators, automated lateral motion of a host vehicle based at least in part on the tracked motion of the CIP target vehicle.

5. The method of claim 1 , further comprising calculating the specified difference threshold based on one or more gain parameters, wherein: The one or more gain parameters include at least one of a highway road type, a city road type, the presence of a road anomaly, and a route navigation path.

6. The method according to claim 5 further comprises obtaining environmental context data associated with the vehicle, wherein: The environmental context data includes at least one of the following: map data associated with the location of the vehicle, road data associated with the location of the vehicle, road anomaly data associated with the location of the vehicle, route navigation data associated with the location of the vehicle, and vehicle perception and vision data.

7. The method according to claim 1, wherein: Determining the difference index includes: Determining a piecewise local lateral position error between a path history of the CIP target vehicle and a path history of the host vehicle; Determining a piecewise local heading error between the path history of the CIP target vehicle and the path history of the host vehicle; and The piecewise local lateral position error and the piecewise local heading error are combined to determine the disparity index based on the piecewise local lateral position error and the piecewise local heading error.

8. The method according to claim 1, wherein: Obtaining the path history of the CIP target vehicle includes: in response to initially detecting the CIP target vehicle as a newly selected target vehicle, constructing a virtual historical initial path of the CIP target vehicle using a systematic dynamically constructed back propagation behind the CIP target vehicle.

9. The method according to claim 1, further comprising: determining a current state variable of the host vehicle, wherein the current state variable comprises 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 An updated position of the vehicle is identified based on the current state variable.

10. A vehicle control system for controlling automatic vehicle acceleration and braking and steering, the vehicle control system comprising: at least one vehicle object sensor configured to detect a closest in-path (CIP) target vehicle in front of the host vehicle; as well as A vehicle control module of the vehicle, wherein the vehicle control module is configured as follows: Obtaining a path history of a CIP target vehicle, the path history comprising a plurality of historical positions of the CIP target vehicle in a specified time interval; Obtaining a path history of the vehicle, the path history of the vehicle including a plurality of historical positions of the vehicle in the specified time interval; comparing the path history of the CIP target vehicle with the path history of the host vehicle to determine a difference index; as well as In response to the difference index being less than a specified difference threshold, automatic acceleration, braking, and steering of a host vehicle are controlled based at least in part on the tracked motion of the CIP target vehicle.

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