Vehicle turning control method, vehicle-mounted controller and automobile
By using the standard state space model and the optimization method of curb distance penalty in autonomous vehicles, the problem of the vehicle deviating from the planned centerline during cornering is solved, safer and more stable cornering control is achieved, and the vehicle's path following performance and autonomous cornering ability are improved.
Patent Information
- Application Number
- CN202510487399.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-17
- Publication Date
- 2025-06-20
AI Technical Summary
During the bending of autonomous vehicles, it is difficult for the prior art to effectively avoid vehicle steering overshooting, causing the vehicle to deviate from the planned centerline, increase the risk of collision with the curb, and reduce the ability to turn.
By determining the standard state space model of vehicle lateral movement based on the vehicle status data and reference trajectory status data within the target time period, and combining real-time vehicle position and curb position, the curb distance penalty item is determined, and the goal of the standard state space model is optimized to achieve the minimum curb distance penalty item, and then the vehicle is controlled by controlling the curve.
It effectively reduces the collision risk between the vehicle and the curb, ensures the safety and stability of the vehicle's cornering, and improves the vehicle's path following performance and autonomous cornering ability in continuous curves.
Smart Images
Figure CN120171530A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of autonomous driving technology, and in particular to a vehicle cornering control method, a vehicle-mounted controller and a vehicle. Background Art
[0002] With the development of autonomous driving technology, in the process of autonomous driving control of vehicles, the driving route of the vehicle is usually planned in advance according to the road conditions to obtain the planned center line as the ideal trajectory for controlling the vehicle's driving, so that the vehicle can drive along the planned center line as much as possible to ensure the safety performance and driving efficiency of the vehicle. In particular, under continuous curved road conditions, it is necessary to use a control algorithm to control the vehicle to control the vehicle to make stable turns along the planned center line as much as possible. For example, in the prior art, a PID (Proportional Integral Derivative) control algorithm is usually combined with real-time data collected by vehicle sensors to control the vehicle that needs to turn in real time. However, the vehicle is a multi-degree-of-freedom system. Under curved road conditions that require continuous turning, due to factors such as delays in various systems in the vehicle, signal noise, and uncertainty in the road environment, the vehicle's steering overshoot phenomenon will occur, causing the actual vehicle route generated by the vehicle during the turning process to deviate from the planned center line of the pre-planned driving route, resulting in a smaller distance between the actual position of the vehicle during the turning process and the curb, resulting in a potential collision point, increasing the risk of collision with the curb, and reducing the vehicle's turning ability. Therefore, how to improve the vehicle's cornering ability on curved roads is a technical problem that needs to be solved urgently. Summary of the invention
[0003] The embodiments of the present invention provide a vehicle cornering control method, a vehicle-mounted controller and a vehicle to solve the technical problem of how to improve the cornering ability of a vehicle under curved road conditions.
[0004] A vehicle cornering control method, comprising: Determine a standard state space model of the lateral motion of the vehicle based on the vehicle state data and the reference trajectory state data within the target time period; Based on the real-time vehicle position and the real-time roadside position within the target time period, determine the roadside distance penalty item corresponding to each moment; Determine an optimization target corresponding to the standard state space model, the optimization target corresponding to the standard state space model is determined based on a curb optimization target, and the curb optimization target is determined based on a curb distance penalty item corresponding to all moments in a target time period; When the optimization target corresponding to the standard state space model reaches a minimum value, the target control data corresponding to the standard state space model is determined, and the vehicle cornering control is performed based on the target control data.
[0005] Preferably, the method for determining the standard state space model of vehicle lateral movement based on the vehicle state data and the reference trajectory state data within a target time period includes: Determining an original error model of vehicle lateral movement based on the vehicle state data and the reference trajectory state data within a target time period; Performing a standardization transformation on the original error model of vehicle lateral movement to determine the standard state space model of vehicle lateral movement.
[0006] Preferably, the method for determining the curb distance penalty term corresponding to each moment based on the real-time vehicle position and the real-time curb position within a target time period includes: Determining the real-time curb distance corresponding to each moment based on the real-time vehicle position and the real-time curb position within a target time period; Determining the curb distance penalty term corresponding to each moment based on the real-time curb distance corresponding to each moment, where the curb distance penalty term is determined based on the real-time curb distance and a preset safety distance.
[0007] Preferably, the method for determining the curb distance penalty term corresponding to each moment based on the real-time curb distance corresponding to each moment includes: If the real-time curb distance is greater than or equal to the preset safety distance, determining that the value of the curb distance penalty term is 0; If the real-time curb distance is less than the preset safety distance, determining that the value of the curb distance penalty term is the product of a preset coefficient and a curb distance penalty value; the curb distance penalty value is the square of the difference between the preset safety distance and the real-time curb distance.
[0008] Preferably, the curb optimization target is the sum of the curb distance penalty terms corresponding to all moments within a target time period.
[0009] Preferably, the real-time curb position includes a left curb position and a right curb position; The curb optimization target is the sum of a left optimization target and a right optimization target; the left optimization target is the sum of the left curb distance penalty terms corresponding to all moments within a target time period; the right optimization target is the sum of the right curb distance penalty terms corresponding to all moments within a target time period; The left curb distance penalty term is determined based on the left curb distance and the preset safety distance, and the right curb distance penalty term is determined based on the right curb distance and the preset safety distance; the left curb distance is the distance between the real-time vehicle position and the left curb position, and the right curb distance is the distance between the real-time vehicle position and the right curb position.
[0010] Preferably, the standard state space model of vehicle lateral movement includes a control input vector; The optimization objective corresponding to the standard state - space model is the sum of the curb optimization objective, the state - error optimization objective, and the control - input optimization objective; The state - error optimization objective is the sum of the state - error penalty terms corresponding to all moments within the target time period, and the state - error penalty terms are determined based on the vehicle state data within the target time period; The control - input optimization objective is the sum of the control - input penalty terms corresponding to all moments within the target time period, and the control - input penalty terms are determined based on the control - input vector within the target time period.
[0011] Preferably, when the optimization objective corresponding to the standard state - space model reaches the minimum value, determining the target control data corresponding to the standard state - space model includes: Performing a quadratic - form transformation on the curb optimization objective to determine the quadratic form corresponding to the curb optimization objective; Performing an expansion process on the standard state - space model of the vehicle's lateral motion to determine an extended model, and performing a quadratic - form transformation on the state - error optimization objective based on the extended model to obtain a first quadratic form corresponding to the control - input vector; Performing a quadratic - form transformation on the control - input optimization objective to obtain a second quadratic form corresponding to the control - input vector; Performing a global minimum - value solution on the sum of the quadratic form corresponding to the curb optimization objective, the first quadratic form, and the second quadratic form. When the optimization objective corresponding to the standard state - space model reaches the minimum value, determining the target control data corresponding to the control - input vector.
[0012] A vehicle - mounted controller includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above - mentioned vehicle cornering control method is implemented.
[0013] A vehicle includes the above - mentioned vehicle - mounted controller.
[0014] The above - mentioned vehicle cornering control method, vehicle - mounted controller, and vehicle consider the curb - distance penalty term in the optimization objective corresponding to the standard state - space model, determine the target control data for controlling vehicle cornering when the optimization objective corresponding to the standard state - space model reaches the minimum value, and control vehicle cornering based on the target control data at each moment within the target time period. This can reduce the collision risk between the vehicle and the curb, ensure the safety and stability of vehicle cornering, enable the vehicle to follow the preset planned centerline when driving on a curved road condition that requires continuous turning, improve the path - following performance of the vehicle in continuous curves, and the vehicle's autonomous cornering ability when driving on a curved road condition that requires continuous turning. Description of the Drawings
[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments of the present invention. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.
[0016] Figure 1 is a flowchart of a vehicle cornering control method in an embodiment of the present invention; Figure 2 is another flowchart of a vehicle cornering control method in an embodiment of the present invention; Figure 3 is another flowchart of a vehicle cornering control method in an embodiment of the present invention; Figure 4 is another flowchart of a vehicle cornering control method in an embodiment of the present invention; Figure 5 is another flowchart of a vehicle cornering control method in an embodiment of the present invention; Figure 6 is a comparison diagram of the planned center line of the driving route and the actual vehicle route in the case of a curved road condition where continuous cornering is required in an embodiment of the present invention. Detailed implementation manners
[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.
[0018] The vehicle cornering control method provided by the embodiments of the present invention can be applied to an in-vehicle controller to improve the cornering ability of the vehicle in a curved road condition. Here, the in-vehicle controller is a controller provided on the vehicle.
[0019] In one embodiment, as Figure 1 shown, a vehicle cornering control method is provided. Taking the application of this method to an in-vehicle controller provided on the vehicle as an example, the method includes the following steps: S101: Based on the vehicle state data and the reference trajectory state data within the target time period, determine the standard state space model of the vehicle's lateral movement; S102: Based on the real-time vehicle position and the real-time road edge position within the target time period, determine the road edge distance penalty term corresponding to each moment; S103: Determine the optimization objective corresponding to the standard state-space model. The optimization objective corresponding to the standard state-space model is determined based on the curb optimization objective, and the curb optimization objective is determined based on the curb distance penalty terms corresponding to all moments within the target time period. S104: When the optimization objective corresponding to the standard state-space model reaches the minimum value, determine the target control data corresponding to the standard state-space model, and perform vehicle cornering control based on the target control data.
[0020] Among them, the target time period refers to a period of control time during which the vehicle needs to be controlled to turn. Understandably, during the process of vehicle autonomous driving, the vehicle is continuously controlled according to multiple consecutive target time periods to ensure the continuity and stability of vehicle control. For example, set the target time period to a seconds (where a > 0), and each time the vehicle is controlled to turn within a seconds. After a seconds end, continue to control the vehicle to turn and drive within the target time period corresponding to the next a seconds to ensure the continuity and stability of vehicle cornering control. Vehicle state data refers to the data used to characterize the running state of the vehicle. Reference trajectory state data refers to the vehicle's running state data when the vehicle travels along the planned center line obtained by pre-planning the vehicle's driving route according to the curved road conditions. The standard state-space model refers to a standard model used to characterize the error between the vehicle state data at each moment and the reference trajectory state data at the same moment.
[0021] As an example, in step S101, the in-vehicle controller obtains the planned center line of the vehicle under the curved road conditions planned in advance, and determines the state data of the vehicle running at each moment corresponding to the planned center line as the reference trajectory state data. Understandably, if the vehicle is controlled to travel along the pre-planned center line, reference trajectory state data corresponding to each moment will be generated. For example, the reference trajectory state data includes the position coordinate data, heading angle, and heading angular velocity, etc. of the vehicle at each moment in the pre-planned center line. These reference trajectory state data are used as the reference data for the vehicle's actual driving within the target time period to determine whether the vehicle deviates significantly from the pre-set planned center line during the actual cornering process and whether there is a potential collision risk, so as to adjust and control the vehicle input in real time to ensure the safety performance of the vehicle and the vehicle's cornering ability. In this example, the in-vehicle controller performs error processing and standardization processing on the vehicle state data at each moment and the reference trajectory state data at each moment within the target time period, and obtains the standard state-space model of the vehicle's lateral deviation from the pre-set planned center line within the target time period, that is, obtains the standard state-space model of the vehicle's lateral movement.
[0022] The real-time vehicle position refers to the position of the vehicle at each moment in the target time period. The real-time curb position refers to the position of the curb closest to the real-time vehicle position at each moment. The curb distance penalty term is used to control the distance between the real-time vehicle position and the nearest real-time curb position to avoid collision between the vehicle and the curb. Figure 6 As shown in FIG. 1 , a comparison diagram of the planned center line of the driving route and the actual vehicle route under a curved road condition requiring continuous turning is shown. Under a curved road condition requiring continuous turning, the existing control algorithm, such as the PID control algorithm, usually causes the actual vehicle route of the vehicle under the curved road condition to deviate from the planned center line, causing the vehicle to produce the following in the process of turning: Figure 6 In order to prevent the actual vehicle route from deviating from the planned center line during continuous turning and generating potential collision points, and to ensure the path following ability between the vehicle and the planned center line during driving, it is necessary to optimize the distance between the real-time vehicle position and the nearest real-time curb position. Therefore, based on the real-time vehicle position and the real-time curb position within the target time period, a curb distance penalty item that needs to be optimized is set to avoid the situation where the vehicle route deviates from the planned center line and causes a collision due to the small distance between the real-time vehicle position and the nearest real-time curb position.
[0023] As an example, in step S102, the on-board controller performs a distance analysis on the real-time vehicle position at each moment in the target time period and the real-time curb position at the same moment, obtains the distance data between the real-time vehicle position and the real-time curb position at each moment, and determines the curb distance penalty item based on the distance data between the real-time vehicle position at each moment and the real-time curb position at the same moment, so as to control the distance between the real-time vehicle position and the real-time curb position at each moment in the target time period through the curb distance penalty item, so as to avoid the situation in which the vehicle collides with the curb due to a large deviation from the pre-planned center line during driving, thereby ensuring the safety performance of the vehicle when turning and improving the vehicle's path following capability of the planned center line at each moment.
[0024] The optimization objective corresponding to the standard state-space model refers to the optimization objective for minimizing the error of the standard state-space model.
[0025] As an example, in step S103, the vehicle-mounted controller processes the curb distance penalty term to determine the optimization objective corresponding to the standard state space model within the target time period. Understandably, based on the curb distance penalty term, the optimization objective corresponding to the standard state space model is determined to optimize the distance data between the vehicle and the curb at each moment within the target time period, ensuring that the vehicle does not collide with the curb under curved road conditions, improving the smoothness and safety of the vehicle during continuous cornering, and enhancing the cornering ability and path following ability of the vehicle under curved road conditions.
[0026] Among them, the target control data refers to the data for controlling the vehicle to turn.
[0027] As an example, in step S104, based on the standard state space model of the vehicle's lateral movement within the target time period, the vehicle-mounted controller converts the optimization objective corresponding to the standard state space model into a quadratic programming problem, and uses the quadratic programming solver corresponding to the quadratic programming problem to solve for the minimum value of the optimization objective corresponding to the standard state space model, obtaining the target control data at each moment within the target time period. The target control data at each moment is used as the input data for vehicle control to control the vehicle to smoothly turn under continuous curved road conditions, achieving real-time path following of the pre-planned center line of the plan under continuous curved road conditions, avoiding collisions between the vehicle and the curb, ensuring the safety and smoothness of vehicle control, and enhancing the cornering ability of the vehicle under continuous curved road conditions.
[0028] In this embodiment, the curb distance penalty term is considered in the optimization objective corresponding to the standard state space model. When the optimization objective corresponding to the standard state space model reaches the minimum value, the target control data for controlling the vehicle to turn is determined, and within the target time period, the vehicle turning is controlled based on the target control data at each moment, which can reduce the collision risk between the vehicle and the curb, ensure the safety and smoothness of vehicle turning, enable the vehicle to follow the preset planned center line under curved road conditions that require continuous turning, improve the path following performance of the vehicle in continuous curves, and the autonomous cornering ability of the vehicle under curved road conditions.
[0029] In one embodiment, as Figure 2 shown, step S101, that is, based on the vehicle state data and the reference trajectory state data within the target time period, determining the standard state space model of the vehicle's lateral movement, includes: S201: Based on the vehicle state data and the reference trajectory state data within the target time period, determining the original error model of the vehicle's lateral movement; S202: Performing a standardization transformation on the original error model of the vehicle's lateral movement to determine the standard state space model of the vehicle's lateral movement.
[0030] Among them, the original error model refers to the relationship model between the error vectors at two adjacent moments. Among them, the error vector is a vector formed by the difference between the vehicle state data and the reference trajectory state data at each moment.
[0031] As an example, in step S201, after the vehicle-mounted controller obtains the vehicle state data and the reference trajectory state data within the target time period, it performs a difference operation on the vehicle state data and the reference trajectory state data corresponding to each moment to determine the error data corresponding to each moment, and converts the error data corresponding to each moment into an error vector corresponding to each moment; alternatively, the vehicle state data and the reference trajectory state data corresponding to each moment can be respectively converted into a vehicle state vector and a reference trajectory state vector, and a difference operation is performed on the vehicle state vector and the reference trajectory state vector corresponding to each moment to determine the error vector corresponding to each moment; then, based on the error vectors corresponding to two adjacent moments, the original error model of the vehicle's lateral movement is determined, that is, the original error model is: +B , where is the error vector at the (k + 1)-th moment within the target time period, is the error vector at the k-th moment within the target time period, is the system state matrix, B is the control matrix, is the control input vector at the k-th moment within the target time period. Among them, the control input vector refers to the input vector used to control the vehicle to drive through a curve.
[0032] In this example, = , = , A = , B = , = .
[0033] Among them, is the vehicle state data at the k-th moment within the target time period, is the reference trajectory state data at the k-th moment within the target time period. = , = .
[0034] Among them, is the vehicle position abscissa data at the k-th moment within the target time period, is the vehicle position ordinate data at the k-th moment within the target time period, is the vehicle heading angle at the k-th moment within the target time period, is the vehicle heading angular velocity at the k-th moment within the target time period. is the abscissa data of the vehicle position when the vehicle travels along the pre-planned center line of the plan at the k-th moment within the target time period, is the ordinate data of the vehicle position when the vehicle travels along the pre-planned center line of the plan at the k-th moment within the target time period, is the vehicle heading angle when the vehicle travels along the pre-planned center line of the plan at the k-th moment within the target time period, is the vehicle heading angular velocity when the vehicle travels along the pre-planned center line of the plan at the k-th moment within the target time period. is the time difference between two adjacent moments within the target time period. is the front wheel steering angle of the vehicle at the k-th moment within the target time period, is the vehicle acceleration at the k-th moment within the target time period. The vehicle is controlled to run with the front wheel steering angle and vehicle acceleration corresponding to each moment, generating the vehicle state data corresponding to each moment.
[0035] Among them, the standardization transformation refers to the transformation process of converting the original error model into a standard state space model.
[0036] As an example, in step S202, the vehicle-mounted controller linearizes the original error model of the vehicle lateral movement to determine the standard state space model of the vehicle lateral movement. In this example, the standard state space model is: +B , where, is the vehicle state data at the (k + 1)-th moment within the target time period, is the vehicle state data at the k-th moment within the target time period. It can be understood that since the original error model is used to characterize the error vector and between the differences, = - , = - , and are both related to the vehicle state data and the reference trajectory state data at the corresponding moments and are not linearized error models. And the linearized error model is more convenient for optimization. Therefore, it is necessary to linearize the original error model to obtain the linearized standard state space model.
[0037] In one embodiment, as shown in Figure 3 , step S102, that is, based on the real-time vehicle position and the real-time curb position within the target time period, determining the curb distance penalty term corresponding to each moment includes: S301: Based on the real-time vehicle position and the real-time curb position within the target time period, determine the real-time curb distance corresponding to each moment. S302: Based on the real-time curb distance corresponding to each moment, determine the curb distance penalty term corresponding to each moment, where the curb distance penalty term is determined based on the real-time curb distance and the preset safety distance.
[0038] Among them, the real-time curb distance refers to the distance between the real-time vehicle position at each moment and the corresponding real-time curb position at the same moment.
[0039] As an example, in step S301, the in-vehicle controller calculates the distance between the real-time vehicle position at each moment and the real-time curb position at the same moment within the target time period, and determines the real-time curb distance corresponding to the vehicle at each moment. In this example, the real-time curb distance at each moment is: , where is the real-time curb distance at the k-th moment within the target time period, is the abscissa of the real-time vehicle position at the k-th moment within the target time period, is the ordinate of the real-time vehicle position at the k-th moment within the target time period, is the abscissa in the real-time curb position at the k-th moment within the target time period, is the ordinate in the real-time curb position at the k-th moment within the target time period. It can be understood that since the real-time curb position is the position data corresponding to the curb position closest to the real-time vehicle position at each moment, therefore, based on the real-time curb distance at each moment and the real-time curb position at the same moment, the real-time curb distance at each moment is determined to represent the closest distance between the vehicle and the curb at each moment, so as to facilitate judging whether there is a risk of the vehicle colliding with the curb under the current curved road conditions according to the real-time curb distance.
[0040] Among them, the preset safety distance refers to the safe value of the distance between the vehicle and the curb. The curb distance penalty term is used to ensure that the real-time curb distance between the vehicle and the curb is greater than the preset safety distance to avoid the vehicle colliding with the curb during continuous turning.
[0041] As an example, in step S302, the vehicle-mounted controller processes the real-time curb distance and the preset safety distance at the same moment within the target time period to obtain the curb distance penalty term corresponding to each moment within the target time period, which is used to control the size difference between the real-time curb distance and the preset safety distance at the same moment. Understandably, when the real-time curb distance is greater than or equal to the preset safety distance, it can ensure that the vehicle has a strong path following ability between the planned center line when turning under the road conditions of continuous curves, avoiding potential collision points. While when the real-time curb distance is less than the preset safety distance, the distance between the vehicle and the curb is small, there is a risk of potential collision. To avoid potential collision points during the continuous turning process of the vehicle and ensure the path following ability between the vehicle and the planned center line during the driving process, it is necessary to monitor and optimize the real-time curb distance between the real-time vehicle position and the real-time curb position at the same moment to avoid collisions caused by too small a distance between the real-time vehicle position and the nearest curb position. In this example, by processing the real-time curb distance and the preset safety distance, the curb distance penalty term at each moment is determined to monitor the situation where the real-time curb distance is less than the preset safety distance, so as to adjust the real-time vehicle position of the vehicle in a timely manner according to the size relationship between the real-time curb distance and the preset safety distance, improve the vehicle's following ability to the planned center line, and ensure that the real-time curb distance between the vehicle and the curb is greater than the preset safety distance at each moment under the road conditions of continuous curves to avoid potential collision points and cause the vehicle to collide with the curb when turning.
[0042] In this embodiment, according to the real-time curb distance and the preset safety distance corresponding to each moment within the target time period, the real-time curb distance at each moment is determined. According to the real-time curb distance and the preset safety distance corresponding to each moment within the target time period, the curb distance penalty term corresponding to each moment within the target time period is determined, so as to subsequently control the vehicle to maintain a preset safety distance from the curb according to the curb distance penalty term, and control the vehicle to turn in real time to avoid collision with the curb, and improve the vehicle's turning ability under the road conditions that require continuous turning.
[0043] In one embodiment, as Figure 4 shown, step S302, that is, determining the curb distance penalty term corresponding to each moment based on the real-time curb distance corresponding to each moment, includes: S401: If the real-time curb distance is greater than or equal to the preset safety distance, it is determined that the value of the curb distance penalty term is 0; S402: If the real-time curb distance is less than the preset safety distance, it is determined that the value of the curb distance penalty term is the product of the preset coefficient and the curb distance penalty value; the curb distance penalty value is the square of the difference between the preset safety distance and the real-time curb distance.
[0044] As an example, in step S401, the vehicle-mounted controller monitors the magnitude relationship between the real-time curb distance and the preset safety distance at each moment within the target time period. When it is determined that the real-time curb distance is greater than or equal to the preset safety distance, the value of the curb distance penalty term is determined to be 0. That is, if the real-time curb distance at a certain moment is less than the preset safety distance, the value of the curb distance penalty term at that moment is determined to be 0. Understandably, when the real-time curb distance is greater than or equal to the preset safety distance, it indicates that the real-time curb distance between the vehicle and the curb under the current curved road condition is relatively large, and there is no risk of potential collision. At this time, there is no need to penalize the real-time curb distance, and the value of the curb distance penalty term is 0.
[0045] Among them, the curb distance penalty value is used to characterize the difference between the real-time curb distance and the preset safety distance when the real-time curb distance is less than the preset safety distance. The preset coefficient refers to the preset coefficient for correcting the target distance difference.
[0046] As an example, in step S402, the vehicle-mounted controller monitors the magnitude relationship between the real-time curb distance and the preset safety distance at each moment within the target time period. When it is determined that the real-time curb distance is less than the preset safety distance, the square of the difference between the preset safety distance and the real-time curb distance is determined as the curb distance penalty value, and the curb distance penalty value is corrected using the preset coefficient to obtain the curb distance penalty term corresponding to the moment when the real-time curb distance is less than the preset safety distance within the target time period. In this example, for the moment when the real-time curb distance is less than the preset safety distance , the curb distance penalty term is: . Among them, is the preset coefficient, is the preset safety distance, is the real-time curb distance at the moment when the real-time curb distance is less than the preset safety distance within the target time period .
[0047] In this embodiment, the vehicle-mounted controller can perform activation processing on the curb distance penalty term using an activation function to implement the functions in the above steps S401 and S402, that is, when the real-time curb distance is greater than or equal to the preset safety distance, the value of the curb distance penalty term is determined to be 0; when the real-time curb distance is less than the preset safety distance, the value of the curb distance penalty term is the curb distance penalty value. Among them, the activation function can be selected as the max function. In this example, within the target time period, the curb distance penalty term at the moment is . From the above curb distance penalty term , it can be seen that at the moment When the real-time curb distance is less than the preset safety distance, it indicates that at this moment, the real-time curb distance between the vehicle and the curb is too small, and there is a risk of colliding with the curb. The curb distance penalty term corresponding to this moment is activated and is , so as to control the vehicle to maintain a preset safety distance from the nearest point of the curb according to the curb distance penalty term. When the moment When the real-time curb distance is greater than the preset safety distance, it indicates that there is no collision risk at this moment, and the curb distance penalty term corresponding to this moment is 0.
[0048] In this embodiment, when the real-time curb distance is less than the preset safety distance, the curb distance penalty term is activated, so as to control the vehicle to maintain a preset safety distance from the curb according to the curb distance penalty term, and to control the vehicle to turn in real time to avoid the vehicle colliding with the curb.
[0049] In one embodiment, the curb optimization target is the sum of the curb distance penalty terms corresponding to all moments within the target time period.
[0050] As an example, when the on-vehicle controller determines that the curb distance penalty term corresponding to each moment within the target time period is , the sum of the curb penalty terms for all moments within the target time period is determined as the curb optimization target, and the curb optimization target is , where k is the kth moment within the target time period, and there are N moments within the target time period.
[0051] In this embodiment, determining the sum of the curb distance penalty terms corresponding to all moments within the target time period as the curb optimization target can more comprehensively reflect the distance between the vehicle and the curb at each moment. So, after optimizing the curb optimization target, within the target time period, the distance between the vehicle and the curb at each moment can be controlled to avoid generating potential collision points and ensure the vehicle turns smoothly.
[0052] In one embodiment, the real-time curb position includes the left curb position and the right curb position; The curb optimization target is the sum of the left optimization target and the right optimization target; the left optimization target is the sum of the left curb distance penalty terms corresponding to all moments within the target time period; the right optimization target is the sum of the right curb distance penalty terms corresponding to all moments within the target time period; The left curb distance penalty term is determined based on the left curb distance and the preset safety distance, and the right curb distance penalty term is determined based on the right curb distance and the preset safety distance; the left curb distance is the distance between the real-time vehicle position and the left curb position, and the right curb distance is the distance between the real-time vehicle position and the right curb position.
[0053] Among them, the left curb position refers to the position of the nearest curb to the left side of the vehicle in the lateral direction of the vehicle. The right curb position refers to the position of the nearest curb to the right side of the vehicle in the lateral direction of the vehicle. The left curb distance is the distance between the real-time vehicle position and the left curb position, and the right curb distance is the distance between the real-time vehicle position and the right curb position. The left curb distance penalty term is used to ensure that the left curb distance between the vehicle and the left curb is greater than the preset safety distance. The right curb distance penalty term is used to ensure that the right curb distance between the vehicle and the right curb is greater than the preset safety distance.
[0054] In an example, at the k-th moment within the target time period , the left curb distance between the real-time vehicle position of the vehicle and the left curb position is: ; where is the abscissa in the left curb position at the k-th moment, is the ordinate in the left curb position at the k-th moment. In this example, the abscissa of the vehicle center position can be used, or the abscissa of the left side of the vehicle can be used. The ordinate of the vehicle center position can be used, or the ordinate of the left side of the vehicle can be used.
[0055] Similarly, in an example, at the k-th moment within the target time period , the right curb distance between the real-time vehicle position of the vehicle and the right curb position is: ; where is the abscissa in the right curb position at the k-th moment, is the ordinate in the right curb position at the k-th moment. In this example, the abscissa of the vehicle center position can be used, or the abscissa of the right side of the vehicle can be used. The ordinate of the vehicle center position can be used, or the ordinate of the right side of the vehicle can be used.
[0056] Understandably, when controlling a vehicle to turn, it is necessary to control both the left side and the right side of the vehicle in the lateral direction of the vehicle so that there are no potential collision points with the roadside, and to avoid collisions between both sides of the vehicle in the lateral direction and the roadside. Therefore, it is necessary to determine the left roadside distance and the right roadside distance at each moment within the target time period, so as to control the left roadside distance between the real-time vehicle position and the left roadside position, and the right roadside distance between the real-time vehicle position and the right roadside position to be greater than or equal to the preset safety distance according to the left roadside distance and the right roadside distance at each moment, in order to control the vehicle to turn in real time, avoid collisions between the vehicle and the roadside on both sides, improve the real-time following performance of the vehicle along the pre-planned center line in a continuous curve, and improve the vehicle's autonomous turning ability.
[0057] In this embodiment, if the left roadside distance is greater than or equal to the preset safety distance, it is determined that the value of the left roadside distance penalty term is 0; if the left roadside distance is less than the preset safety distance, it is determined that the value of the left roadside distance penalty term is the product of the first preset coefficient and the left roadside distance penalty value; the left roadside distance penalty value is the square of the difference between the preset safety distance and the left roadside distance. If the right roadside distance is greater than or equal to the preset safety distance, it is determined that the value of the right roadside distance penalty term is 0; if the right roadside distance is less than the preset safety distance, it is determined that the value of the right roadside distance penalty term is the product of the second preset coefficient and the right roadside distance penalty value; the right roadside distance penalty value is the square of the difference between the preset safety distance and the right roadside distance.
[0058] Among them, the first preset coefficient is used to correct the left roadside distance penalty value. The second preset coefficient is used to correct the left roadside distance penalty value.
[0059] As an example, if at a certain moment the left roadside distance , it is determined that the value of the left roadside distance penalty term at this moment is 0. If at a certain moment the left roadside distance < , it is determined that the value of the left roadside distance penalty term at this moment is . The vehicle-mounted controller can select the max() function to implement the above function and determine that the left roadside distance penalty term at a certain moment is . From the above left roadside distance penalty term , it can be seen that when the left roadside distance at moment is less than the preset safety distance, it indicates that at this moment, the left roadside distance between the vehicle and the roadside is too small, and there is a risk of colliding with the roadside. The left roadside distance penalty term corresponding to this moment is activated and is , so as to control the vehicle to maintain a preset safe distance from the nearest point of the left curb according to the left curb distance penalty term. When the left curb distance is greater than the preset safe distance, it indicates that there is no collision risk between the vehicle and the left curb at this moment, and the left curb distance penalty term corresponding to this moment is 0.
[0060] If at a moment the right curb distance is greater than or equal to the right curb distance < , it is determined that the value of the right curb distance penalty term at this moment is 0. If at a moment . The vehicle-mounted controller can select the max function to implement the above function and determine that the right curb distance penalty term at a moment is . From the above right curb distance penalty term , it can be seen that when the right curb distance at a moment is less than the preset safe distance, it indicates that at this moment, the right curb distance between the vehicle and the curb is too small, and there is a risk of colliding with the curb. The right curb distance penalty term corresponding to this moment is activated and is , so as to control the vehicle to maintain a preset safe distance from the nearest point of the right curb according to the right curb distance penalty term. When the right curb distance is greater than the preset safe distance, it indicates that there is no collision risk between the vehicle and the right curb at this moment, and the right curb distance penalty term corresponding to this moment is 0.
[0061] In this embodiment, the vehicle-mounted controller sums up the left curb distance penalty terms at each moment within the target time period to determine the left optimization target within the target time period. The vehicle-mounted controller sums up the right curb distance penalty terms at each moment within the target time period to determine the right optimization target within the target time period. The vehicle-mounted controller sums up the left optimization target and the right optimization target corresponding to all moments within the target time period to determine that the curb optimization target is: That is, the curb optimization target is: .
[0062] In this embodiment, on the left and right sides of the vehicle in the lateral direction of the vehicle, the left curb distance and the right curb distance are respectively determined, which can more accurately reflect the distances between the vehicle and the nearest points on both sides of the curb at each moment. Based on the relatively accurate left curb distance and right curb distance, the curb distance penalty term can be accurately determined. In the lateral direction of the vehicle, the left optimization target is determined according to the left curb distance penalty term, and the right optimization target is determined according to the right curb distance penalty term. The curb optimization target is determined according to the left optimization target and the right optimization target. By taking into account the distances between the vehicle and the curbs on both sides, the curb optimization target can more accurately characterize the relationship between the vehicle and the curbs on both sides, so that after optimizing the curb optimization target subsequently, the vehicle can be precisely controlled to turn, and collisions with the curbs on both sides can be avoided when the vehicle is in a continuous turning road condition.
[0063] In one embodiment, the standard state space model of the lateral movement of the vehicle includes a control input vector; The optimization target corresponding to the standard state space model is the sum of the curb optimization target, the state error optimization target, and the control input optimization target; The state error optimization target is the sum of the state error penalty terms corresponding to all moments within the target time period, and the state error penalty term is determined based on the vehicle state data within the target time period; The control input optimization target is the sum of the control input penalty terms corresponding to all moments within the target time period, and the control input penalty term is determined based on the control input vector within the target time period.
[0064] Among them, the state error optimization target is used to optimize the error between the vehicle state data and the reference trajectory state data at each moment within the target time period. The control input optimization target is used to optimize the control input vector at each moment within the target time period. The state error penalty term is used to reduce the error between the vehicle state data and the reference trajectory state data. The control input penalty term is used to control the control input vector when the error between the vehicle state data and the reference trajectory state data is large. It can be understood that when the vehicle receives the input control of the control input vector, corresponding vehicle state data will be generated. When the vehicle state data error is large, the control input vector needs to be controlled to reduce the vehicle state data corresponding to the control input vector and the generated corresponding vehicle state data.
[0065] As an example, the vehicle-mounted controller uses the first preset weight matrix to perform a quadratic transformation process on the vehicle state data at each moment within the target time period, and determines the state error penalty term at each moment within the target time period. Among them, the first preset weight matrix is a quadratic matrix, which is used to perform a quadratic transformation process on the vehicle state data to obtain the state error penalty term corresponding to the vehicle state data. In this example, the state error penalty term is: , where, For the vehicle state data at the th moment within the target time period, denotes transpose, is the first preset weight matrix, where the target time period contains N moments, 0 ≤ ≤ N - 1. The vehicle-mounted controller determines the sum value of the state error penalty terms at all moments within the target time period as the state error optimization target within the target time period, where the target time period includes N moments such as 0, 1, ,, (N - 1).
[0066] As an example, the vehicle-mounted controller uses the second preset weight matrix to perform a quadratic transformation on the control input vector at each moment within the target time period, and determines the control input penalty term at each moment within the target time period. Among them, the second preset weight matrix is a quadratic matrix, which is used to perform a quadratic transformation on the control input vector to obtain the control input penalty term corresponding to the control input vector. In this example, the control input penalty term is: where, is the control input vector at the kth moment within the target time period, denotes transpose, is the second preset weight matrix, where the target time period contains N moments, 0 ≤ ≤ N - 1. The vehicle-mounted controller determines the sum value of the control input penalty terms at all moments within the target time period as the control input optimization target within the target time period, where the target time period includes N moments such as 0, 1, ,, (N - 1).
[0067] As an example, the vehicle-mounted controller obtains the sum value of the target time period, the state error optimization target , the control input optimization target , and the curb optimization target : The minimum value function min() is used to process the above sum value to obtain the optimization target corresponding to the standard state space model within the target time period: Understandably, the optimization objective corresponding to the standard state space model is used to ensure the minimization of the error corresponding to the state error optimization objective, control the control input vector corresponding to the control input optimization objective, and ensure the minimization of the penalty corresponding to the curb optimization objective, so as to minimize the deviation between the actual vehicle route generated during actual driving and the planned center line corresponding to the pre-planned ideal path during the vehicle cornering process, and improve the cornering ability of the vehicle under the curved road conditions that require continuous turning and the path following ability to the planned center line.
[0068] In this embodiment, the error corresponding to the state error optimization objective, the control input vector corresponding to the control input optimization objective, and the penalty corresponding to the curb optimization objective are considered within the optimization objective corresponding to the standard state space model, and the state error, the control input vector, and the real-time curb distance between the real-time vehicle position and the real-time curb position corresponding to the nearest curb at all times during the target time period are controlled, so as to improve the stability, safety, and cornering continuity of vehicle operation, and enhance the autonomous cornering ability and path following performance of the vehicle under continuous curved road conditions.
[0069] In one embodiment, as Figure 5 shown, step S104, that is, when the optimization objective corresponding to the standard state space model reaches the minimum value, determine the target control data corresponding to the standard state space model, including: S501: Perform a quadratic form transformation on the curb optimization objective to determine the quadratic form corresponding to the curb optimization objective; S502: Perform an expansion process on the standard state space model of vehicle lateral motion to determine the expanded model, and perform a quadratic form transformation on the state error optimization objective based on the expanded model to obtain the first quadratic form corresponding to the control input vector; S503: Perform a quadratic form transformation on the control input optimization objective to obtain the second quadratic form corresponding to the control input vector; S504: Solve the global minimum value of the sum of the quadratic form corresponding to the curb optimization objective, the first quadratic form, and the second quadratic form, and determine the target control data corresponding to the control input vector when the optimization objective corresponding to the standard state space model reaches the minimum value.
[0070] Among them, the quadratic form transformation is used to transform the curb optimization objective into a quadratic form. The quadratic form corresponding to the curb optimization objective refers to the linear form corresponding to the curb optimization objective.
[0071] As an example, in step S501, the vehicle-mounted controller sets the first slack variable whose meaning is , and sets the second slack variable whose meaning is , wherein the first slack variable and the second slack variable are preset slack variables for performing a quadratic form transformation on the left optimization target and the right optimization target in the curb optimization target. The first slack variable is used and the second slack variable to perform a quadratic form transformation on the curb optimization target to obtain the quadratic form corresponding to the curb optimization target . Understandably, the curb optimization target is in a non-linear form, which is not conducive to optimization. It is necessary to transform the non-linear form of the curb optimization target into a linear form to facilitate the optimization of the curb optimization target.
[0072] Among them, the extended model refers to the model obtained by extending the standard state space model of vehicle lateral motion within the target time period. Understandably, within the target time period, the standard state space model of vehicle lateral motion is +B , which can represent the relationship between the vehicle state data between two adjacent moments within the target time period. Within the target time period, for every two adjacent moments, there is a corresponding standard state space model. Representing all the standard state space models corresponding to two adjacent moments within the target time period in the form of a time series, the extended model is obtained.
[0073] Understandably, since the control input vector at the k-th moment within the target time period is then the sequence U of the control input vector at each moment within the target time period is: U = Since the vehicle state data at the k-th moment within the target time period is then the sequence X of the vehicle state data at each moment within the target time period is: X = Understandably, since when performing predictive control on the vehicle state data at each moment within the target time period, the vehicle state data at the initial moment is known data, and the extended model is represented by the vehicle state data sequence represented by the vehicle state data at the initial moment and the sequence of control input vectors, therefore, the first vehicle state data in the vehicle state data sequence X is used to predict each vehicle state data in the vehicle state data sequence X within the target time period through the extended model.
[0074] In this embodiment, the vehicle state data = wherein, is the abscissa of the real-time vehicle position at the k-th moment within the target time period, is the ordinate of the real-time vehicle position at the k-th moment within the target time period, is the vehicle heading angle at the k-th moment within the target time period, is the vehicle heading angular velocity at the k-th moment within the target time period.
[0075] Among them, the quadratic transformation refers to the way of transforming the optimization objective into a quadratic form related to the control input vector. The first quadratic form refers to the quadratic form of the control input vector corresponding to the state error optimization objective.
[0076] As an example, in step S502, the vehicle-mounted controller expands the standard state space model of the vehicle's lateral movement within the target time period to obtain an extended model that represents the vehicle state data based on the vehicle state data and the control input vector at the initial moment within the target time period. In this example, the extended model is: + . Among them, is the extended matrix of the system state matrix, is the extended matrix of the control matrix.
[0077] = , = The vehicle-mounted controller uses the sequence of vehicle state data to represent , and obtains = . Among them, is the extended matrix corresponding to it within the target time period. Using the extended model + to perform quadratic transformation on , and obtain the first quadratic form represented by the sequence of control input vectors : = ( + ) That is = +2 + Among them, the second quadratic form refers to the quadratic form after the expansion of the control input optimization objective within the target time period.
[0078] As an example, in step S503, the vehicle-mounted controller uses the sequence of control input vectors , and within the target time period, for the control input quadratic term Perform a quadratic form transformation to obtain a sequence of control input vectors The corresponding extended second quadratic form: = , where is the extended matrix of
[0079] As an example, in step S504, the vehicle-mounted controller obtains the sum of the quadratic form, the first quadratic form, and the second quadratic form corresponding to the curb optimization target according to the curb optimization target: The vehicle-mounted controller arranges the above formula into a quadratic programming algorithm +r corresponding representation: ( +R)U+ + + where the control input vectors in the sequence of control input vectors are independent variables.
[0080] Let = +R, = , r = Select a quadratic programming solver in the quadratic programming algorithm to perform global minimum optimization for the representation corresponding to the above quadratic programming algorithm. When there is a global minimum in the representation corresponding to the above quadratic programming algorithm, the optimization target corresponding to the standard state space model also reaches the minimum value. At this time, in the sequence of control input vectors within the target time period, the target control data corresponding to the control input vector at each moment is obtained.
[0081] In this example, the target control data includes vehicle front wheel steering angle data and vehicle acceleration data. The vehicle front wheel steering angle data refers to the value corresponding to the vehicle front wheel steering angle. The vehicle acceleration data refers to the value corresponding to the vehicle acceleration. After the vehicle-mounted controller obtains the target control data corresponding to the control input vector at each moment within the target time period, it obtains the vehicle front wheel steering angle data and vehicle acceleration data corresponding to the target control data at each moment, and controls the vehicle to travel at the vehicle front wheel steering angle data and vehicle acceleration data corresponding to that moment at each moment within the target time period, realizing a smooth and continuous cornering under continuous curved road conditions and improving the vehicle's autonomous cornering ability.
[0082] In this embodiment, by performing a quadratic form transformation on the curb optimization objective and quadratic form transformations on the state error optimization objective and the control input optimization objective, a representation form of the optimization objective with respect to quadratic programming solution is obtained. A quadratic programming solver is used to solve the global minimum of the representation form of the optimization objective with respect to quadratic programming solution, so as to solve the minimum value of the optimization objective and obtain the target control data corresponding to the control input vector for controlling the vehicle to turn. This method can obtain the target control data for the vehicle to turn at each moment within the target time period without complicated processing of the optimization objective corresponding to the standard state space model, which is relatively convenient and fast. When controlling the vehicle to turn at the corresponding moment within the target time period based on the target control data, the autonomous turning ability of the vehicle within the target time period can be improved, and the continuity and stability of vehicle control can be ensured.
[0083] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
[0084] In one embodiment, an in-vehicle controller is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the vehicle turning control method in the above embodiment is implemented, for example Figure 1 as shown in S101 - S104, or Figures 2 to 5 as shown in, to avoid repetition, it will not be elaborated here.
[0085] In one embodiment, an automobile is provided, including the above in-vehicle controller.
[0086] In another embodiment, a vehicle turning control system is provided, including the above in-vehicle controller for controlling the vehicle to turn.
[0087] In another embodiment, an automobile is provided, including the above vehicle turning control system.
[0088] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.
[0089] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.
[0090] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention and should all be included in the protection scope of the present invention.
Claims
1. A vehicle cornering control method, characterized in that: include: Determine a standard state space model of the lateral motion of the vehicle based on the vehicle state data and the reference trajectory state data within the target time period; Based on the real-time vehicle position and the real-time roadside position within the target time period, determine the roadside distance penalty item corresponding to each moment; Determine an optimization target corresponding to the standard state space model, the optimization target corresponding to the standard state space model is determined based on a curb optimization target, and the curb optimization target is determined based on a curb distance penalty item corresponding to all moments in a target time period; When the optimization target corresponding to the standard state space model reaches a minimum value, the target control data corresponding to the standard state space model is determined, and the vehicle cornering control is performed based on the target control data.
2. The vehicle cornering control method according to claim 1, characterized in that: The method of determining a standard state space model of the lateral motion of the vehicle based on the vehicle state data and the reference trajectory state data within the target time period includes: Determine an original error model of the lateral motion of the vehicle based on the vehicle state data and the reference trajectory state data within the target time period; The original error model of the vehicle's lateral motion is standardized and transformed to determine the standard state space model of the vehicle's lateral motion.
3. The vehicle cornering control method according to claim 1, characterized in that: The step of determining the roadside distance penalty item corresponding to each moment based on the real-time vehicle position and the real-time roadside position within the target time period includes: Based on the real-time vehicle position and the real-time curb position within the target time period, determine the real-time curb distance corresponding to each moment; Based on the real-time curb distance corresponding to each moment, a curb distance penalty item corresponding to each moment is determined, and the curb distance penalty item is determined based on the real-time curb distance and a preset safety distance.
4. The vehicle cornering control method according to claim 3, characterized in that: The determining of the curb distance penalty item corresponding to each moment based on the real-time curb distance corresponding to each moment includes: If the real-time curb distance is greater than or equal to the preset safety distance, the curb distance penalty item is determined to be 0; If the real-time curb distance is less than the preset safety distance, the curb distance penalty item is determined to be the product of a preset coefficient and a curb distance penalty value; the curb distance penalty value is the square of the difference between the preset safety distance and the real-time curb distance.
5. The vehicle cornering control method according to claim 1, characterized in that: The curb optimization target is the sum of the curb distance penalty items corresponding to all moments in the target time period.
6. The vehicle cornering control method according to claim 1, characterized in that: The real-time curb position includes a left curb position and a right curb position; The curb optimization target is the sum of the left side optimization target and the right side optimization target; The left side optimization target is the sum of the left side curb distance penalty items corresponding to all moments in the target time period; The right side optimization target is the sum of the right side curb distance penalty items corresponding to all moments in the target time period; The left curb distance penalty item is determined based on the left curb distance and a preset safety distance, and the right curb distance penalty item is determined based on the right curb distance and a preset safety distance; The left curb distance is the distance between the real-time vehicle position and the left curb position, and the right curb distance is the distance between the real-time vehicle position and the right curb position.
7. The vehicle cornering control method according to claim 6, characterized in that: The standard state-space model of the lateral motion of the vehicle includes a control input vector; The optimization target corresponding to the standard state space model is the sum of the curb optimization target, the state error optimization target and the control input optimization target; The state error optimization target is the sum of the state error penalty items corresponding to all moments in the target time period, and the state error penalty item is determined based on the vehicle state data in the target time period; The control input optimization target is the sum of the control input penalty items corresponding to all moments in the target time period, and the control input penalty item is determined based on the control input vector in the target time period.
8. The vehicle cornering control method according to claim 7, characterized in that: When the optimization target corresponding to the standard state space model reaches the minimum value, determining the target control data corresponding to the standard state space model includes: Performing a secondary form conversion on the curb optimization target to determine a secondary form corresponding to the curb optimization target; Performing an expansion process on the standard state space model of the lateral motion of the vehicle to determine the expansion model, and performing a quadratic form conversion on the state error optimization target based on the expansion model to obtain a first quadratic form corresponding to a control input vector; Performing a quadratic transformation on the control input optimization target to obtain a second quadratic form corresponding to the control input vector; The quadratic form corresponding to the curb optimization target, the sum of the first quadratic form and the second quadratic form are globally solved, and when the optimization target corresponding to the standard state space model reaches the minimum value, the target control data corresponding to the control input vector is determined.
9. A vehicle-mounted controller, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the vehicle cornering control method according to any one of claims 1 to 8 is implemented.
10. An automobile, characterized in that: Includes the vehicle-mounted controller as described in claim 9.