Tracking control method and device, vehicle, storage medium and program product

Abstract

The application relates to the technical field of driving control, in particular to a tracking control method and device, a vehicle, a storage medium and a program product, wherein the method comprises the following steps: solving an estimated forward distance of a target vehicle according to multiple parameters to determine an estimated forward looking area of the target vehicle; solving a tracking trajectory curvature of the estimated forward looking area, optimizing the estimated forward distance according to the tracking trajectory curvature to obtain a final forward distance, and determining a final forward looking area of the target vehicle; traversing path points in the final forward looking area to obtain a target tracking point of the target vehicle; and solving a tracking control parameter of the target vehicle according to the target tracking point, so that the target vehicle can be controlled to track and drive according to the tracking control parameter. Therefore, the problems in the related art, such as low calculation accuracy of the variable forward distance method, large tracking error and difficulty in meeting the tracking control performance requirements of the vehicle, are solved.

Description

Technical Field

[0001] This application relates to the field of driving control technology, and in particular to a tracking control method, device, vehicle, storage medium, and program product. Background Technology

[0002] Pure tracking algorithms, as robust and reliable control algorithms, are easy to tune and simple to control. They consume relatively little computation time in trajectory tracking applications and are suitable for low-speed driving conditions. Their tracking performance largely depends on the forward sight distance; under different conditions, the forward sight distance needs to be dynamically adjusted to achieve better tracking results.

[0003] In related technologies, the methods for adjusting the forward sight distance can be divided into, but are not limited to, two types. One is a rule-based variable forward sight distance method, which mainly uses some pre-set explicit judgment rules to directly output a fixed or linearly adjusted forward sight distance when the corresponding rules are met, such as calibrating the corresponding forward sight distance change rate according to curvature or vehicle speed. The other is a variable forward sight distance method based on upper-level fuzzy control, which uses fuzzy sets and fuzzy inference rules to handle complex and uncertain driving conditions and dynamically output a continuously adjustable forward sight distance.

[0004] However, the rule-based variable look-ahead distance method in related technologies is strongly correlated with road conditions and calibration, which leads to a large amount of calibration work. The variable look-ahead distance method based on upper-level fuzzy control is more related to the formulation of fuzzy rules and relies too much on experience. When the experience is incorrect or lacks experience, the calculation accuracy of the variable look-ahead distance will be low, resulting in a large tracking error and making it difficult to meet the vehicle tracking control performance requirements. This needs to be solved urgently. Summary of the Invention

[0005] This application provides a tracking control method, device, vehicle, storage medium, and program product to solve the problems in related technologies, such as variable look-ahead distance methods being strongly correlated with road conditions and calibration, leading to a large calibration workload, or being highly dependent on fuzzy rule formulation, relying too much on experience, and resulting in low accuracy of variable look-ahead distance calculation when experience is incorrect or lacking, thus leading to large tracking errors and difficulty in meeting the vehicle's tracking control performance requirements.

[0006] A first aspect of this application provides a vehicle tracking control method, comprising the following steps: acquiring multiple parameters of a target vehicle, calculating an estimated forward-looking distance of the target vehicle based on the multiple parameters, and determining an estimated forward-looking region of the target vehicle based on the estimated forward-looking distance; calculating the tracking trajectory curvature of the estimated forward-looking region, optimizing the estimated forward-looking distance based on the tracking trajectory curvature to obtain a final forward-looking distance of the target vehicle, and determining a final forward-looking region of the target vehicle based on the final forward-looking distance; traversing the path points of the final forward-looking region to obtain a target tracking point of the target vehicle, calculating tracking control parameters of the target vehicle based on the target tracking point, and controlling the target vehicle to perform tracking driving according to the tracking control parameters.

[0007] Optionally, in one embodiment of this application, obtaining multiple parameters of the target vehicle to calculate the estimated forward look-ahead distance of the target vehicle based on the multiple parameters includes: obtaining the current speed, maximum braking acceleration, abnormal reaction distance, and minimum turning radius of the target vehicle; and calculating the estimated forward look-ahead distance of the target vehicle based on the current speed, the maximum braking acceleration, the abnormal reaction distance, and the minimum turning radius.

[0008] Optionally, in one embodiment of this application, the step of optimizing the estimated forward-looking distance based on the tracking trajectory curvature to obtain the final forward-looking distance of the target vehicle, and determining the final look-ahead region of the target vehicle based on the final forward-looking distance, includes: determining the influence coefficients of the target vehicle at different vehicle speeds and the influence coefficients of the target vehicle at different road curvatures based on the forward-looking distance and vehicle speed of the target vehicle and the proportional relationship between the forward-looking distance and the tracking trajectory curvature, so as to obtain the speed influence coefficient and road curvature influence coefficient of the estimated forward-looking distance based on the current vehicle speed of the target vehicle and the tracking trajectory curvature; optimizing the estimated forward-looking distance by combining the speed influence coefficient, the road curvature influence coefficient, and the tracking trajectory curvature to obtain the final forward-looking distance; and increasing and decreasing the target amount respectively on the final forward-looking distance, so as to determine the final look-ahead region based on the area between the increased target amount and the decreased target amount on the final forward-looking distance.

[0009] Optionally, in one embodiment of this application, traversing the path points of the final look-ahead region to obtain the target tracking point of the target vehicle includes: obtaining the current position of the vehicle; and traversing the path points of the final look-ahead region based on the current position to obtain the target tracking point of the target vehicle.

[0010] Optionally, in one embodiment of this application, the step of solving the tracking control parameters of the target vehicle based on the target tracking point includes: solving at least one of the target vehicle's steering angle, steering angular velocity, heading angle deviation correction, lateral deviation correction, and lateral acceleration based on the target tracking point; and determining the tracking control parameters based on at least one of the steering angle, steering angular velocity, heading angle deviation correction, lateral deviation correction, and lateral acceleration.

[0011] A second aspect of this application provides a vehicle tracking control device, comprising: a solving module, configured to acquire multiple parameters of a target vehicle, to solve for an estimated forward-looking distance of the target vehicle based on the multiple parameters, and to determine an estimated forward-looking region of the target vehicle based on the estimated forward-looking distance; an optimization module, configured to calculate the tracking trajectory curvature of the estimated forward-looking region, optimize the estimated forward-looking distance based on the tracking trajectory curvature, obtain a final forward-looking distance of the target vehicle, and determine a final forward-looking region of the target vehicle based on the final forward-looking distance; and a control module, configured to traverse the path points of the final forward-looking region to obtain a target tracking point of the target vehicle, solve for tracking control parameters of the target vehicle based on the target tracking point, and control the target vehicle to perform tracking driving according to the tracking control parameters.

[0012] Optionally, in one embodiment of this application, the solving module includes: a first acquisition unit, configured to acquire the current speed, maximum braking acceleration, abnormal reaction distance, and minimum turning radius of the target vehicle; and a first solving unit, configured to solve for the estimated forward sight distance of the target vehicle based on the current speed, the maximum braking acceleration, the abnormal reaction distance, and the minimum turning radius.

[0013] Optionally, in one embodiment of this application, the optimization module includes: a query unit, configured to determine the influence coefficients of the target vehicle at different vehicle speeds and the influence coefficients of the target vehicle at different road curvatures based on the forward-looking distance and vehicle speed of the target vehicle and the proportional relationship between the forward-looking distance and the curvature of the tracking trajectory, so as to obtain the speed influence coefficient and road curvature influence coefficient of the estimated forward-looking distance based on the current vehicle speed of the target vehicle and the curvature of the tracking trajectory; an optimization unit, configured to optimize the estimated forward-looking distance by combining the speed influence coefficient, the road curvature influence coefficient and the tracking trajectory curvature, to obtain the final forward-looking distance; and a first determination unit, configured to add a target amount and subtract a target amount respectively on the final forward-looking distance, so as to determine the final look-ahead region based on the region between the target amount added and the target amount subtracted on the final forward-looking distance.

[0014] Optionally, in one embodiment of this application, the control module includes: a second acquisition unit, configured to acquire the current position of the vehicle; and a traversal unit, configured to traverse the path points of the final look-ahead region based on the current position to obtain the target tracking point of the target vehicle.

[0015] Optionally, in one embodiment of this application, the control module includes: a second solving unit, configured to solve for at least one of the target vehicle's steering angle, steering angular velocity, heading angle deviation correction, lateral deviation correction, and lateral acceleration based on the target tracking point; and a second determining unit, configured to determine the tracking control parameters based on at least one of the steering angle, steering angular velocity, heading angle deviation correction, lateral deviation correction, and lateral acceleration.

[0016] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle tracking control method as described in the above embodiments.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the vehicle tracking control method described above.

[0018] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, is used to implement the vehicle tracking control method described above.

[0019] This application embodiment can solve for the estimated forward look-ahead distance and estimated look-ahead region based on multiple parameters of the target vehicle. Then, it optimizes the estimated forward look-ahead distance based on the tracking trajectory curvature of the estimated look-ahead region to obtain the final look-ahead region. By traversing the path points of the final look-ahead region, the target tracking point and tracking control parameters of the target vehicle can be obtained. Thus, it realizes the estimation of the estimated forward look-ahead distance based on vehicle parameters and current vehicle speed, and then determines the optimized forward look-ahead distance by combining the average curvature in the estimated forward look-ahead distance. This effectively improves the accuracy of the forward look-ahead distance in this application, helps to determine the optimal tracking target point, and significantly reduces the vehicle's control tracking error. Moreover, the forward look-ahead distance in this application can change in real time with the actual road curvature and vehicle speed of the target path, making the error calculation of the pure tracking algorithm good when tracking more complex roads, and ensuring that the vehicle's tracking control effect is smooth and accurate. This solves the problems in related technologies, such as variable look-ahead distance methods being strongly correlated with road conditions and calibration, leading to a large calibration workload, or being too dependent on fuzzy rule formulation, relying too much on experience, which can result in low accuracy of variable look-ahead distance calculation when experience is incorrect or lacking, leading to large tracking errors and difficulty in meeting the vehicle's tracking control performance requirements.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a vehicle tracking control method according to an embodiment of this application; Figure 2 This is a flowchart of a pure tracking control method for adaptive adjustment of forward sight distance according to an embodiment of this application; Figure 3 This is a schematic diagram illustrating the principle of a pure tracking lateral control method according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a vehicle tracking control device provided according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0022] Figure label: 10 - Vehicle tracking and control device; 100 - Solver module, 200 - Optimization module and 300 - Control module; 501 - Memory, 502 - Processor and 503 - Communication interface. Detailed Implementation

[0023] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0024] The following description, with reference to the accompanying drawings, outlines a tracking control method, apparatus, vehicle, storage medium, and program product according to embodiments of this application. Addressing the issues raised in the background section regarding variable look-ahead distance methods, which are either highly dependent on road conditions and calibration, leading to significant calibration workload, or heavily reliant on fuzzy rule formulation, resulting in low accuracy in variable look-ahead distance calculations and large tracking errors when experience is flawed or lacking, thus failing to meet vehicle tracking control performance requirements, this application provides a vehicle tracking control method. In this method, the estimated look-ahead distance and estimated look-ahead region of the target vehicle are calculated based on multiple parameters. The estimated look-ahead distance is then optimized based on the tracking trajectory curvature of the estimated look-ahead region to obtain the final look-ahead region. By traversing the path points of the final look-ahead region, the target tracking point and tracking control parameters of the target vehicle can be obtained. Therefore, this method achieves the estimation of the forward look-ahead distance based on vehicle parameters and current speed, and then determines the optimized forward look-ahead distance by combining the average curvature in the estimated forward look-ahead distance. This effectively improves the accuracy of the forward look-ahead distance in this application, helps determine the optimal tracking target point, and significantly reduces the vehicle's control tracking error. Furthermore, the forward look-ahead distance in this application can change in real time with the actual road curvature and vehicle speed along the target path, ensuring good error calculation for pure tracking algorithms tracking complex roads and guaranteeing smooth and accurate vehicle tracking control. This solves the problems of related technologies where variable forward look-ahead distance methods are either strongly correlated with road conditions and calibration, leading to a large calibration workload, or heavily dependent on fuzzy rule formulation, relying too much on experience, and resulting in low accuracy in variable forward look-ahead distance calculations when experience is incorrect or lacking, leading to large tracking errors and difficulty in meeting the vehicle's tracking control performance requirements.

[0025] Specifically, Figure 1 This is a flowchart of a vehicle tracking control method provided in an embodiment of this application.

[0026] like Figure 1 As shown, the vehicle tracking control method includes the following steps: Step S101: Obtain multiple parameters of the target vehicle to calculate the estimated forward look-ahead distance of the target vehicle based on the multiple parameters, and determine the estimated forward look-ahead area of ​​the target vehicle based on the estimated forward look-ahead distance. Step S102: Calculate the curvature of the tracking trajectory in the estimated look-ahead area, optimize the estimated look-ahead distance based on the curvature of the tracking trajectory, and obtain the final look-ahead distance of the target vehicle, so as to determine the final look-ahead area of ​​the target vehicle based on the final look-ahead distance; Step S103: Traverse the path points of the final look-ahead region to obtain the target tracking point of the target vehicle. Solve the tracking control parameters of the target vehicle based on the target tracking point, and control the target vehicle to follow the tracking control parameters.

[0027] In some embodiments, this application may first calculate the estimated forward-looking distance of the target vehicle by obtaining multiple parameters based on basic calculation rules, and then determine the estimated forward-looking region of the target vehicle by adding or subtracting a target amount from the estimated forward-looking distance. That is, add a target amount to the estimated forward-looking distance and subtract the target amount from the estimated forward-looking distance. Finally, determine the estimated forward-looking region based on the area between the distance after adding the target amount to the estimated forward-looking distance and the distance after subtracting the target amount (forward-looking distance: a length value; forward-looking region: the distance range after forward-looking distance ± target amount).

[0028] For example, if a vehicle's estimated forward visibility distance is 50 meters, and the target distance is ±10 meters, then the estimated forward visibility area is between 40 and 60 meters. It should be noted that the specific target distance can be determined by those skilled in the art based on the actual vehicle conditions. This embodiment is merely illustrative and does not impose any specific limitations.

[0029] Here, the target vehicle can be understood as the vehicle object tracked and controlled in an autonomous driving or assisted driving system. Through processing such as forward-looking distance and forward-looking area, the target vehicle can ultimately be tracked and driven according to a certain trajectory.

[0030] Then, in this embodiment of the application, the tracking trajectory curvature of the estimated look-ahead region can be solved to further optimize the estimated look-ahead distance, thereby obtaining a more accurate and reliable final look-ahead distance for the target vehicle, and thus obtaining a more accurate final look-ahead region based on the final look-ahead distance. The process of obtaining the final look-ahead region based on the final look-ahead distance is the same as the process of obtaining the estimated look-ahead region based on the estimated look-ahead distance, and will not be described again in this embodiment of the application.

[0031] After obtaining the accurate final look-ahead region, this embodiment of the application can traverse all path points in the final look-ahead region to obtain the target tracking point of the target vehicle, so as to solve the tracking control parameters of the target vehicle based on the obtained target tracking point, and finally control the target vehicle to follow the tracking control parameters according to the tracking control parameters.

[0032] Here, path points refer to a series of discrete coordinate points that constitute a complete driving path within the look-ahead area; target tracking points can be understood as key coordinate points that are selected after traversing the path points, and that the target vehicle can accurately follow, guiding the vehicle's driving direction and position.

[0033] By using the target tracking point, the embodiments of this application can calculate the tracking control parameters when the vehicle is tracking, such as steering angle, vehicle speed, acceleration, etc. The target vehicle can be controlled to follow the tracking parameters according to the tracking control parameters, and it can be determined when to turn, when to accelerate or decelerate, etc.

[0034] This application embodiment can first preliminarily determine the estimated forward-looking distance and the estimated forward-looking area based on multiple parameters, and then dynamically optimize the preliminarily determined estimated forward-looking distance by combining the tracking trajectory curvature of the estimated forward-looking area. Based on the final forward-looking distance, the path points in the final forward-looking area are selected to identify the target tracking point, and the tracking control parameters for the target vehicle during tracking are solved. This ensures that the forward-looking area and the tracking control parameters used by the target vehicle during tracking can accurately adapt to its actual driving trajectory. For example, shortening the forward-looking distance on curves improves handling flexibility, while extending the forward-looking distance on straight roads ensures driving stability. This significantly improves the accuracy, safety, and adaptability of vehicle tracking, and avoids trajectory tracking deviations or driving risks caused by a fixed forward-looking distance.

[0035] Optionally, in one embodiment of this application, obtaining multiple parameters of the target vehicle to solve for the estimated forward look-ahead distance of the target vehicle based on the multiple parameters includes: obtaining the current speed, maximum braking acceleration, abnormal reaction distance, and minimum turning radius of the target vehicle; and solving for the estimated forward look-ahead distance of the target vehicle based on the current speed, maximum braking acceleration, abnormal reaction distance, and minimum turning radius.

[0036] Based on the descriptions of other embodiments, it will be understood that the embodiments of this application can solve for the estimated forward-looking distance of the target vehicle based on multiple parameters of the acquired vehicle.

[0037] In some embodiments, the multiple parameters of the target vehicle obtained by this application include, but are not limited to, the target vehicle's current speed, maximum braking acceleration, abnormal reaction distance, and minimum turning radius, so that the estimated forward sight distance of the target vehicle can be calculated based on the target vehicle's current speed, maximum braking acceleration, abnormal reaction distance, and minimum turning radius.

[0038] Here, maximum braking acceleration can be understood as the maximum deceleration that a vehicle can achieve during emergency braking; abnormal reaction distance can be understood as the distance a vehicle travels to react when encountering a sudden abnormal condition while driving; and minimum turning radius can be understood as the shortest distance from the turning center to the outer side of the vehicle body when the vehicle is traveling at the minimum turning angle.

[0039] Based on the vehicle speed, the estimated forward-looking distance of the target vehicle can be calculated in the embodiments of this application. The calculation formula can be, but is not limited to, expressed as follows: (1) in, The vehicle's speed; 、 、 This is a constant term, related to vehicle parameters such as maximum braking acceleration, abnormal reaction distance, and minimum turning radius.

[0040] It should be noted that in the actual implementation process, 、 、 The parameters can be determined by those skilled in the art based on other key driving parameters related to vehicle tracking. The embodiments in this application are for illustrative purposes only and are not intended to impose specific limitations.

[0041] The embodiments of this application can combine the target vehicle's current speed, maximum braking acceleration, abnormal reaction distance, and minimum turning radius to solve the estimated forward sight distance of the target vehicle. This comprehensively considers the braking situation at the target vehicle's current speed and the distance requirements during the abnormal reaction phase, while also taking into account the vehicle's flexibility constraints when turning. This ensures that the forward sight distance is sufficient for the vehicle to complete safe avoidance operations, avoids trajectory planning deviations caused by excessively far or close distances, and improves the safety and adaptability of autonomous driving.

[0042] Optionally, in one embodiment of this application, the forward-looking distance is optimized based on the curvature of the tracking trajectory to obtain the final forward-looking distance of the target vehicle, and the final look-ahead area of ​​the target vehicle is determined based on the final forward-looking distance. This includes: determining the influence coefficients of the target vehicle at different vehicle speeds and the influence coefficients of the target vehicle at different road curvatures based on the forward-looking distance and vehicle speed of the target vehicle, as well as the proportional relationship between the forward-looking distance and the curvature of the tracking trajectory; obtaining the speed influence coefficient and the road curvature influence coefficient for estimating the forward-looking distance based on the current vehicle speed and the curvature of the tracking trajectory; optimizing the estimated forward-looking distance by combining the speed influence coefficient, the road curvature influence coefficient, and the tracking trajectory curvature to obtain the final forward-looking distance; and increasing and decreasing the target amount based on the final forward-looking distance to determine the final look-ahead area based on the area between the increased target amount and the decreased target amount based on the final forward-looking distance.

[0043] In some embodiments, when this application optimizes the estimated forward-looking distance based on the curvature of the tracking trajectory to obtain the final forward-looking distance of the target vehicle, it may, but is not limited to, combine the vehicle speed influence coefficient and road curvature influence coefficient of the target vehicle's forward-looking distance obtained from the proportional relationship between the target vehicle's forward-looking distance and vehicle speed and road curvature, as well as the tracking trajectory curvature, to optimize the estimated forward-looking distance of the target vehicle, thereby obtaining the final forward-looking distance.

[0044] First, the embodiments of this application can calculate and estimate the curvature of the tracking trajectory in the look-ahead region: considering that in the process of vehicle control tracking, the tracked trajectory is usually a series of discrete points, therefore, in the embodiments of this application, the mean or variance of the curvature of the next segment of the track to be tracked can be taken as the average curvature of the next segment, that is, the tracking trajectory curvature in the embodiments of this application.

[0045] Then, in this embodiment of the application, the influence coefficients of the target vehicle at different vehicle speeds and the influence coefficients of the target vehicle at different road curvatures can be obtained based on the proportional relationship between the forward sight distance of the target vehicle, the vehicle speed, and the road curvature.

[0046] Specifically, the forward sight distance of a vehicle is directly proportional to its speed and inversely proportional to the road curvature. Therefore, in this embodiment, the influence coefficients of the target vehicle at different speeds and different road curvatures can be determined through calibration or bench testing based on the proportional relationship between the forward sight distance of the target vehicle and its speed and road curvature. Table 1 is a statistical table of influence coefficients from one embodiment of this application, which can be, but is not limited to, represented as follows: Table 1

[0047] Then, in this embodiment of the application, the vehicle speed influence coefficient and road curvature influence coefficient for estimating the forward look-ahead distance can be obtained based on the target vehicle's current speed and the curvature of the tracking trajectory. By combining these vehicle speed influence coefficients, road curvature influence coefficients, and the tracking trajectory curvature, the estimated forward look-ahead distance is optimized to obtain the final forward look-ahead distance. The expression for the final forward look-ahead distance can be, but is not limited to, as follows: (2) in, For vehicle speed, For road curvature, The influence coefficient of speed, This is the influence coefficient of road curvature.

[0048] Finally, by increasing and decreasing the target amount at the final forward look-ahead distance, the area between the target amount increased at the final forward look-ahead distance and the target amount decreased at the optimized appropriate forward look-ahead distance can be used to determine the final look-ahead region.

[0049] This application embodiment determines the corresponding influence coefficient by combining the ratio of vehicle speed, tracking trajectory curvature, and forward-looking distance. Then, it optimizes and estimates the forward-looking distance based on the influence coefficient corresponding to the current actual information of the target vehicle. Finally, it delineates the final forward-looking area based on the final forward-looking distance. This allows the forward-looking distance to dynamically adapt to different vehicle speeds and the curvature of the actual driving road. It can change in real time with road curvature and vehicle speed. At the same time, by increasing or decreasing the target quantity, it ensures that the forward-looking area covers safety requirements without redundancy. This improves the accuracy of trajectory planning and driving safety, and enhances the adaptability of the autonomous driving system to complex road conditions. It also makes the error calculation of the pure tracking algorithm good when tracking more complex roads, and the control effect smooth and accurate.

[0050] Optionally, in one embodiment of this application, traversing the path points of the final look-ahead region to obtain the target tracking point of the target vehicle includes: obtaining the current position of the vehicle; and traversing the path points of the final look-ahead region based on the current position to obtain the target tracking point of the target vehicle.

[0051] In some embodiments, this application may, but is not limited to, traversing the path points of the final look-ahead region based on the vehicle's current position to obtain the target tracking point of the target vehicle.

[0052] For example, this application can, but is not limited to, use the target vehicle's current position, combined with some basic vehicle position prediction models (mathematical models or algorithms built based on the vehicle's current state parameters (such as speed, steering angle, wheelbase, etc.) and environmental / operating condition parameters, such as the vehicle's basic kinematic model, etc., which can predict the coordinates of the vehicle's upcoming position at a certain future moment through quantitative calculations, providing data support for subsequent selection of target tracking points and optimization of driving trajectories), to traverse the path points in the final look-ahead region, and then determine the optimal target tracking point for the vehicle according to the principle of finding the extreme value of the evaluation function. That is, the evaluation function is used to traverse and evaluate each path point in the final look-ahead region, and then the principle of finding the extreme value is used to determine the optimal target tracking point for the vehicle.

[0053] The evaluation function can be set or adjusted by those skilled in the art according to actual needs, but is not limited to this. The embodiments in this application are for illustrative purposes only and are not intended to impose specific limitations. After evaluating each path point in the look-ahead region using the evaluation function and obtaining the evaluation value of each path point, the path point corresponding to the maximum or minimum value among multiple path points can be used as the optimal target tracking point (the optimal target tracking point is determined based on the specific evaluation function, which determines whether the path point corresponding to the maximum or minimum value is the optimal target tracking point).

[0054] With evaluation function For example, among which, For the first The evaluation value of each path point To predict the lateral error for the next time step. To determine the heading angle error at the next moment, calculate the evaluation cost of each path point, and then judge... ?, that is, the first Does the evaluation value of each path point equal the maximum evaluation value? If so, then determine the first... Each path point is the optimal target tracking point for the vehicle.

[0055] For example, suppose a private car is driving on a main urban road at a current speed of 40 km / h. After optimization, the final forward look-ahead distance is 25 meters, with an increase or decrease of ±5 meters. The final look-ahead area is 20 to 30 meters. The preset path within this area is a straight road connecting to a gentle curve, with the road curvature gradually increasing from 0 to 0.03 meters. - ¹, The pathpoints within the final look-ahead area are discretely distributed at 1-meter intervals, totaling 11 pathpoints: P1 (20 meters from the vehicle, straight road, road curvature 0), P2 (21 meters from the vehicle, straight road, curvature 0)...P6 (25 meters from the vehicle, curve start point, road curvature 0.01m) - ¹)……P11 (30 meters from the vehicle, in the middle of the curve, with a curvature of 0.03m)- ¹).

[0056] By combining parameters such as the vehicle's current speed of 40km / h, current front wheel steering angle of 0°, wheelbase of 2.7 meters, and road surface adhesion coefficient of 0.85, a vehicle position prediction model is constructed. The model can calculate the precise location coordinates that the vehicle can reach within the next 1.2 seconds if it continues to drive in the current state to reach the path point in the look-ahead area.

[0057] Then, in this embodiment of the application, all path points from P1 to P11 can be traversed sequentially, and the deviation can be calculated for each point, for example: For P3 (22 meters away from the vehicle, in the straight lane), the predicted position of the car when it arrives at the point can be calculated by the model. Comparing this with the actual coordinates of P3, the lateral deviation is 0.05 meters and the heading deviation is 0.8°. For P7 (26 meters from the vehicle, a gentle curve with a curvature of 0.02m) - ¹) The lateral deviation can be calculated to be 0.03 meters and the heading deviation to be 1.2°; For P10 (29 meters from the vehicle, a gentle curve with a curvature of 0.03m) - ¹) The lateral deviation can be calculated to be 0.15 meters and the heading deviation to be 3.5°. This process can be repeated to complete the traversal of all points.

[0058] The evaluation function is used to find the extreme value and determine the optimal tracking point: The evaluation function is set as: J = k1 × lateral deviation + k2 × heading deviation + k3 × steering change rate, where k1 = 6, k2 = 4, and k3 = 2 are weights used to comprehensively consider the vehicle's steering stability while ensuring driving accuracy. The J value is calculated for each path point: The value of J for P3 is 6 × 0.05 + 4 × 0.8 + 2 × 0.1 = 0.3 + 3.2 + 0.2 = 3.7. P7's J = 6 × 0.03 + 4 × 1.2 + 2 × 0.4 = 0.18 + 4.8 + 0.8 = 5.78; P8 (27 meters from the vehicle, with a curvature of 0.025m) - ¹) J = 6 × 0.04 + 4 × 1.8 + 2 × 0.6 = 0.24 + 7.2 + 1.2 = 8.64; The J values ​​of the remaining points are between the two, with P3 having the smallest J value. This means that P3 has the smallest tracking error and the smoothest turning, so P3 can be determined as the optimal target tracking point.

[0059] Ultimately, the car will use P3 as the target tracking point, and then solve for the tracking control parameters, such as slightly adjusting the steering angle to 0.5° and correcting the vehicle speed to maintain 40km / h, to ensure that the vehicle can smoothly enter the curve, accurately follow the path, and avoid deviating from the lane or making sudden steering.

[0060] This application embodiment can traverse the path points of the final look-ahead region and use the evaluation function to find the extreme value to select the optimal target tracking point. This can comprehensively balance the core requirements of vehicle tracking accuracy and steering smoothness, making the calculated target tracking point more forward-looking and adaptable. This effectively improves the accuracy of vehicle trajectory tracking and driving smoothness, reduces the risk of deviating from the path or sudden operation, and adapts to the autonomous driving control requirements under different road conditions.

[0061] Optionally, in one embodiment of this application, solving the tracking control parameters of the target vehicle based on the target tracking point includes: solving at least one of the target vehicle's steering angle, steering angular velocity, heading angle deviation correction, lateral deviation correction, and lateral acceleration based on the target tracking point; and determining the tracking control parameters based on at least one of the steering angle, steering angular velocity, heading angle deviation correction, lateral deviation correction, and lateral acceleration.

[0062] In some embodiments, when solving the tracking control parameters of the target vehicle based on the target tracking point, this application may, but is not limited to, solve the target vehicle's steering angle, steering angular velocity, heading angle deviation correction, lateral deviation correction, lateral acceleration, and other tracking control parameters.

[0063] Among them, the steering angle is a key control quantity for the lateral control of the vehicle. It can be used to adjust the deflection angle of the wheels, thereby directly changing the driving direction of the vehicle and achieving the technical effect of adapting to the path curvature of the target tracking point. For example, the steering angle is increased when encountering a curve and kept at 0° when traveling straight.

[0064] Steering angular velocity can control the speed and acceleration of wheel steering. Effective steering angular velocity control can prevent sudden wheel steering and effectively improve the smoothness of wheel trajectory tracking.

[0065] The heading angle deviation correction here refers to the angle deviation between the vehicle's actual driving direction (heading angle) and the tangent direction of the target path (the expected driving path of the target vehicle). For example, the current actual driving direction deviates from the tangent direction of the target path by 3°. The corresponding steering adjustment can ensure the consistency between the vehicle's driving direction and the target path.

[0066] The lateral deviation correction here refers to the left and right offset of the vehicle's actual position from the target path. For example, if the vehicle's actual position deviates from the target path by 0.2 meters, the vehicle's steering angle can be effectively corrected by outputting a certain compensation control amount, so that the vehicle returns to the preset path.

[0067] Lateral acceleration, which controls the lateral movement of a vehicle, can be used as a constraint to prevent dangerous situations caused by excessive steering.

[0068] This application's embodiments can achieve comprehensive and precise adaptation to the target tracking point by solving multi-dimensional lateral tracking control parameters such as steering angle and steering angular velocity. It can quickly correct path deviations through steering angle and deviation correction, while ensuring driving smoothness through steering angular velocity and lateral acceleration. The synergistic effect of multiple parameters can effectively improve the accuracy of trajectory tracking and driving stability, reduce uncomfortable operations such as sharp turns, and adapt to different vehicle speeds and road curvature conditions, enhancing the reliability and adaptability of autonomous driving control.

[0069] Example 2 Figure 2 This is a flowchart of a pure tracking control method for adaptive adjustment of forward sight distance according to an embodiment of this application. Figure 3 This is a schematic diagram illustrating the principle of a pure tracking lateral control method according to an embodiment of this application, as shown below. Figure 2 and Figure 3 As shown: (1) Calculate the preset forward sight distance (estimated forward sight distance) based on vehicle speed and vehicle parameters:

[0070] in, The vehicle's speed; 、 、 This is a constant term, related to vehicle parameters such as maximum braking acceleration, abnormal reaction distance, and minimum turning radius.

[0071] (2) Take the mean or variance of the curvature of the next segment of the trajectory to be tracked as the mean curvature of the next segment as the preset look-ahead region tracking trajectory curvature. ; (3) Determine the relationship between forward sight distance and vehicle speed: L is proportional to vehicle speed; (4) Determine the relationship between forward sight distance and road curvature: L is inversely proportional to curvature, that is:

[0072] in, For vehicle speed, For road curvature, The influence coefficient of speed, The influence coefficient of road curvature; (5) The relationship between the influence coefficient and each influencing factor can be determined by calibration or experience. Here, we will use Table 1 from the previous embodiment. (6) Obtain the influence coefficient in real time by looking up a table based on the current vehicle speed and road curvature, and then apply the formula. Calculate the optimized forward look-ahead distance.

[0073] (7) After determining the forward sight distance L, add or subtract a value (such as a pre-determined calibration value) to the forward sight distance as the forward look area; (8) Based on the vehicle position prediction model, traverse the points in the look-ahead region, and use the principle of finding the extreme value of the evaluation function. as well as Determine the optimal target tracking point for the vehicle; (9) After the target tracking point is determined, the relevant calculation formula is used to continue the calculation to obtain the corresponding tracking control parameters (such as steering angle) until the steering wheel angle is output to the actuator of the target vehicle.

[0074] The vehicle tracking control method proposed in this application can calculate the estimated forward-looking distance and estimated look-ahead region based on multiple parameters of the target vehicle. Then, the estimated forward-looking distance is optimized based on the tracking trajectory curvature of the estimated look-ahead region to obtain the final look-ahead region. By traversing the path points of the final look-ahead region, the target tracking point and tracking control parameters of the target vehicle can be obtained. Thus, by estimating the estimated forward-looking distance based on vehicle parameters and current vehicle speed, and then combining the average curvature in the estimated forward-looking distance to determine the optimized forward-looking distance, the accuracy of the forward-looking distance in this application is effectively improved, which helps to determine the optimal tracking target point and significantly reduces the vehicle's control tracking error. Furthermore, the forward-looking distance in this application can change in real time with the actual road curvature and vehicle speed of the target path, ensuring good error calculation for pure tracking algorithms tracking complex roads and guaranteeing smooth and accurate vehicle tracking control performance. This solves the problems in related technologies, such as variable look-ahead distance methods being strongly correlated with road conditions and calibration, leading to a large calibration workload, or being too dependent on fuzzy rule formulation, relying too much on experience, which can result in low accuracy of variable look-ahead distance calculation when experience is incorrect or lacking, leading to large tracking errors and difficulty in meeting the vehicle's tracking control performance requirements.

[0075] Next, the vehicle tracking control device according to the embodiments of this application is described with reference to the accompanying drawings.

[0076] Figure 4 This is a schematic diagram of the vehicle tracking control device according to an embodiment of this application.

[0077] like Figure 4 As shown, the vehicle tracking control device 10 includes: a solution module 100, an optimization module 200, and a control module 300.

[0078] The solution module 100 is used to acquire multiple parameters of the target vehicle, and to solve for the estimated forward look-ahead distance of the target vehicle based on the multiple parameters, so as to determine the estimated forward look-ahead area of ​​the target vehicle based on the estimated forward look-ahead distance; the optimization module 200 is used to calculate the tracking trajectory curvature of the estimated forward look-ahead area, optimize the estimated forward look-ahead distance based on the tracking trajectory curvature, and obtain the final forward look-ahead distance of the target vehicle, so as to determine the final forward look-ahead area of ​​the target vehicle based on the final forward look-ahead distance; the control module 300 is used to traverse the path points of the final forward look-ahead area to obtain the target tracking point of the target vehicle, solve for the tracking control parameters of the target vehicle based on the target tracking point, and control the target vehicle to track and drive according to the tracking control parameters.

[0079] Optionally, in one embodiment of this application, the solving module 100 includes: a first acquisition unit, used to acquire the current speed, maximum braking acceleration, abnormal reaction distance and minimum turning radius of the target vehicle; and a first solving unit, used to solve the estimated forward sight distance of the target vehicle based on the current speed, maximum braking acceleration, abnormal reaction distance and minimum turning radius.

[0080] Optionally, in one embodiment of this application, the optimization module 200 includes: a query unit, configured to determine the influence coefficients of the target vehicle at different vehicle speeds and the influence coefficients of the target vehicle at different road curvatures based on the proportional relationship between the target vehicle's forward-looking distance and its speed and the curvature of the tracking trajectory, so as to obtain the speed influence coefficient and road curvature influence coefficient for estimating the forward-looking distance based on the target vehicle's current speed and the tracking trajectory curvature; an optimization unit, configured to combine the speed influence coefficient, the road curvature influence coefficient, and the tracking trajectory curvature to optimize the estimated forward-looking distance and obtain the final forward-looking distance; and a first determination unit, configured to add and subtract target amounts to the final forward-looking distance, respectively, so as to determine the final look-ahead region based on the area between the added target amount and the subtracted target amount at the final forward-looking distance.

[0081] Optionally, in one embodiment of this application, the control module 300 includes: a second acquisition unit for acquiring the current position of the vehicle; and a traversal unit for traversing the path points of the final look-ahead region based on the current position to obtain the target tracking point of the target vehicle.

[0082] Optionally, in one embodiment of this application, the control module 300 includes: a second solving unit, configured to solve for at least one of the target vehicle's steering angle, steering angular velocity, heading angle deviation correction, lateral deviation correction, and lateral acceleration based on the target tracking point; and a second determining unit, configured to determine tracking control parameters based on at least one of the steering angle, steering angular velocity, heading angle deviation correction, lateral deviation correction, and lateral acceleration.

[0083] It should be noted that the explanation of the vehicle tracking control method embodiment described above also applies to the vehicle tracking control device of this embodiment, and will not be repeated here.

[0084] The vehicle tracking control device proposed in this application can calculate the estimated forward-looking distance and estimated look-ahead area based on multiple parameters of the target vehicle. Then, it optimizes the estimated forward-looking distance based on the tracking trajectory curvature of the estimated look-ahead area to obtain the final look-ahead area. By traversing the path points of the final look-ahead area, the target tracking point and tracking control parameters of the target vehicle can be obtained. Thus, it achieves the estimation of the estimated forward-looking distance based on vehicle parameters and current vehicle speed, and then determines the optimized forward-looking distance by combining the average curvature in the estimated forward-looking distance. This effectively improves the accuracy of the forward-looking distance in this application, helps determine the optimal tracking target point, and significantly reduces the vehicle's control tracking error. Furthermore, the forward-looking distance in this application can change in real time with the actual road curvature and vehicle speed of the target path, ensuring good error calculation for pure tracking algorithms tracking complex roads and guaranteeing smooth and accurate vehicle tracking control performance. This solves the problems in related technologies, such as variable look-ahead distance methods being strongly correlated with road conditions and calibration, leading to a large calibration workload, or being too dependent on fuzzy rule formulation, relying too much on experience, which can result in low accuracy of variable look-ahead distance calculation when experience is incorrect or lacking, leading to large tracking errors and difficulty in meeting the vehicle's tracking control performance requirements.

[0085] Figure 5 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include: The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0086] When the processor 502 executes the program, it implements the vehicle tracking control method provided in the above embodiments.

[0087] Furthermore, electronic devices also include: Communication interface 503 is used for communication between memory 501 and processor 502.

[0088] The memory 501 is used to store computer programs that can run on the processor 502.

[0089] Memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0090] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0091] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0092] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0093] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the vehicle tracking and control method described above.

[0094] This application also provides a computer program product, including a computer program that can execute computer instructions. When the computer instructions are executed by a processor, they implement the vehicle tracking control method provided in this application.

[0095] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0096] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0097] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0098] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0099] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0100] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0101] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0102] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A vehicle tracking control method, characterized in that, Includes the following steps: Multiple parameters of the target vehicle are acquired to calculate the estimated forward look-ahead distance of the target vehicle based on the multiple parameters, and the estimated forward look-ahead area of ​​the target vehicle is determined based on the estimated forward look-ahead distance. Calculate the tracking trajectory curvature of the estimated look-ahead region, optimize the estimated look-ahead distance based on the tracking trajectory curvature, obtain the final look-ahead distance of the target vehicle, and determine the final look-ahead region of the target vehicle based on the final look-ahead distance; The path points of the final look-ahead region are traversed to obtain the target tracking point of the target vehicle. The tracking control parameters of the target vehicle are solved based on the target tracking point, and the target vehicle is controlled to perform tracking driving according to the tracking control parameters.

2. The method according to claim 1, characterized in that, The step of acquiring multiple parameters of the target vehicle, and calculating the estimated forward-looking distance of the target vehicle based on the multiple parameters, includes: Obtain the target vehicle's current speed, maximum braking acceleration, abnormal reaction distance, and minimum turning radius; The estimated forward sight distance of the target vehicle is calculated based on the current speed, the maximum braking acceleration, the abnormal reaction distance, and the minimum turning radius.

3. The method according to claim 1, characterized in that, The step of optimizing the estimated forward-looking distance based on the curvature of the tracking trajectory to obtain the final forward-looking distance of the target vehicle, and determining the final forward-looking area of ​​the target vehicle based on the final forward-looking distance, includes: Based on the forward-looking distance and speed of the target vehicle, as well as the proportional relationship between the forward-looking distance and the curvature of the tracking trajectory, the influence coefficients of the target vehicle at different speeds and the influence coefficients of the target vehicle at different road curvatures are determined, so as to obtain the speed influence coefficient and road curvature influence coefficient of the estimated forward-looking distance according to the current speed of the target vehicle and the curvature of the tracking trajectory. By combining the vehicle speed influence coefficient, the road curvature influence coefficient, and the tracking trajectory curvature, the estimated forward look distance is optimized to obtain the final forward look distance; The target amount is increased and the target amount is decreased at the final forward look-ahead distance, respectively, so as to determine the final forward look-ahead area based on the area between the target amount increased and the target amount decreased at the final forward look-ahead distance.

4. The method according to claim 1, characterized in that, The step of traversing the path points of the final look-ahead region to obtain the target tracking point of the target vehicle includes: Obtain the current location of the vehicle; Based on the current position, the path points of the final look-ahead area are traversed to obtain the target tracking point of the target vehicle.

5. The method according to claim 1, characterized in that, The step of solving the tracking control parameters of the target vehicle based on the target tracking point includes: Based on the target tracking point, solve for at least one of the following: the target vehicle's steering angle, steering angular velocity, heading angle deviation correction, lateral deviation correction, and lateral acceleration. The tracking control parameters are determined based on at least one of the following: steering angle, steering angular velocity, heading angle deviation correction, lateral deviation correction, and lateral acceleration.

6. A vehicle tracking control device, characterized in that, include: The solution module is used to acquire multiple parameters of the target vehicle, to solve the estimated forward look-ahead distance of the target vehicle based on the multiple parameters, and to determine the estimated forward look-ahead area of ​​the target vehicle based on the estimated forward look-ahead distance. An optimization module is used to calculate the tracking trajectory curvature of the estimated look-ahead region, optimize the estimated look-ahead distance based on the tracking trajectory curvature, obtain the final look-ahead distance of the target vehicle, and determine the final look-ahead region of the target vehicle based on the final look-ahead distance. The control module is used to traverse the path points of the final look-ahead region to obtain the target tracking point of the target vehicle, solve the tracking control parameters of the target vehicle based on the target tracking point, and control the target vehicle to perform tracking driving according to the tracking control parameters.

7. The apparatus according to claim 6, characterized in that, The solution module includes: The acquisition unit is used to acquire the target vehicle's current speed, maximum braking acceleration, abnormal reaction distance, and minimum turning radius. The solution unit is used to solve for the estimated forward sight distance of the target vehicle based on the current speed, the maximum braking acceleration, the abnormal reaction distance, and the minimum turning radius.

8. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the vehicle tracking control method as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the vehicle tracking control method as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the vehicle tracking control method as described in any one of claims 1-5.

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