Lateral control method, device and equipment of autonomous vehicle and storage medium

Abstract

The application provides a kind of lateral control method, device, equipment and storage medium of automatic driving vehicle, it is related to automatic driving technical field, the method comprises: obtaining the variance of the steering control quantity of automatic driving vehicle in the preset number of continuous control periods, the steering control quantity is obtained by LQR based on the driving information of automatic driving vehicle, and LQR is obtained based on vehicle dynamics model;According to variance, determine whether the weighting matrix R of LQR is adjusted;If yes, then according to variance, adjust the weighting matrix R of LQR, obtain the adjusted weighting matrix R ′ ;According to the adjusted weighting matrix R ′ And the driving information of automatic driving vehicle, the target steering control quantity of automatic driving vehicle is obtained by LQR to control the steering actuator of automatic driving vehicle to execute target steering control quantity.The application can effectively reduce the problem of steering wheel shaking, improve the driving stability and comfort of the whole vehicle.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a lateral control method, device, equipment and storage medium for an autonomous driving vehicle. Background Art

[0002] The lateral control method of an autonomous vehicle refers to the technology used to control the autonomous vehicle to maintain the correct lane position on the road. It mainly involves controlling the steering system of the autonomous vehicle to ensure that the autonomous vehicle travels along the predetermined path and ensures driving stability and comfort.

[0003] Currently, in some scenarios, when performing lateral control on an autonomous vehicle, there is a problem of large steering wheel shaking of the autonomous vehicle. Therefore, an effective solution for lateral control of the autonomous vehicle is urgently needed. Summary of the Invention

[0004] The purpose of the present invention is to provide a lateral control method, device, equipment and storage medium for an autonomous driving vehicle to solve the problem of large steering wheel shaking of the autonomous driving vehicle when the autonomous driving vehicle is controlled laterally in some scenarios.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A lateral control method for an autonomous vehicle includes: obtaining a variance of a steering control amount of the autonomous vehicle within a preset number of consecutive control cycles, the steering control amount being obtained based on driving information of the autonomous vehicle through a linear quadratic regulator LQR, the LQR being obtained based on a vehicle dynamics model; determining whether to adjust a weighting matrix R of the LQR based on the variance; and if so, adjusting the weighting matrix R of the LQR based on the variance to obtain an adjusted weighting matrix R ′ ; According to the adjusted weight matrix R ′ and the driving information of the autonomous vehicle, and obtain the target steering control amount of the autonomous vehicle through LQR to control the steering actuator of the autonomous vehicle to execute the target steering control amount.

[0007] According to the above technical means, the weighting matrix R of LQR is dynamically adjusted based on the variance of the steering control amount of the autonomous driving vehicle within a preset number of continuous control cycles, so that the weighting matrix R after adjustment is ′The target steering control amount of the autonomous vehicle is obtained through LQR based on the driving information of the autonomous vehicle, so as to control the steering actuator of the autonomous vehicle to execute the target steering control amount. The problem of steering wheel shaking can be effectively reduced, especially when the path curvature changes greatly. The target steering control amount obtained by LQR is smoother, thereby improving the driving stability and comfort of the whole vehicle.

[0008] Further, determining whether to adjust the LQR weighting matrix R according to the variance includes: if the variance is greater than or equal to the angle threshold, determining to adjust the LQR weighting matrix R; if the variance is less than the angle threshold, determining not to adjust the LQR weighting matrix R.

[0009] Furthermore, according to the variance, the weight matrix R of LQR is adjusted to obtain the adjusted weight matrix R ′ , including: the adjusted weight matrix R ′ Satisfy the following formula:

[0010] R ′ =min(R*(σ 2 -threshold)*a,b)

[0011] Among them, σ 2 represents the variance; threshold represents the angle threshold; a represents the scaling factor; and b represents the upper limit of the weighting matrix R.

[0012] Furthermore, the driving information includes the vehicle status information, vehicle planned trajectory and vehicle position information of the autonomous driving vehicle, and the steering control amount is obtained in the following manner: coordinate conversion is performed according to the vehicle position information and the vehicle planned trajectory to obtain the local path in the vehicle body coordinate system corresponding to the autonomous driving vehicle; based on the vehicle status information, vehicle position information and local path, the steering control amount is obtained through LQR.

[0013] Furthermore, based on the vehicle state information, vehicle position information and local path, the steering control amount is obtained through LQR, including: determining the lateral deviation and heading deviation corresponding to the autonomous driving vehicle according to the vehicle position information and the local path; based on the vehicle state information, the lateral deviation and the heading deviation, the steering control amount is obtained through LQR.

[0014] Furthermore, the vehicle dynamics model is determined based on the lateral deviation, the heading deviation, and the vehicle state information, where the vehicle state information includes the front wheel cornering stiffness, the rear wheel cornering stiffness, the moment of inertia, the distance from the center of mass to the front axle, the distance from the center of mass to the rear axle, the mass of the autonomous vehicle, the longitudinal speed, and the front wheel turning angle.

[0015] Furthermore, the lateral deviation and the heading deviation are the lateral deviation and the heading deviation at a preset distance in front of the center of the rear axle of the autonomous driving vehicle.

[0016] Further, the vehicle body coordinate system takes a center point of a rear axle of the autonomous vehicle as an origin.

[0017] A lateral control device of an autonomous vehicle, comprising:

[0018] A first obtaining module, configured to obtain a variance of a steering control amount of the autonomous vehicle in a preset number of continuous control periods, the steering control amount being obtained by a linear quadratic regulator (LQR) based on driving information of the autonomous vehicle, the LQR being obtained based on a vehicle dynamics model;

[0019] A determining module, configured to determine whether to adjust a weighting matrix R of the LQR according to the variance;

[0020] An adjusting module, configured to, if yes, adjust the weighting matrix R of the LQR according to the variance to obtain an adjusted weighting matrix R ′ .

[0021] A second obtaining module, configured to obtain a target steering control amount of the autonomous vehicle by the LQR based on the adjusted weighting matrix R ′ and the driving information of the autonomous vehicle, so as to control a steering actuator of the autonomous vehicle to execute the target steering control amount.

[0022] Further, the determining module is specifically configured to: if the variance is greater than or equal to an angle threshold, determine to adjust the weighting matrix R of the LQR; and if the variance is less than the angle threshold, determine not to adjust the weighting matrix R of the LQR.

[0023] Further, the adjusting module is specifically configured to: the adjusted weighting matrix R ′ satisfies the following formula:

[0024] R ′ =min(R*(σ 2 -threshold)*a,b)

[0025] Wherein, σ 2 represents the variance; threshold represents the angle threshold; a represents a scaling coefficient; and b represents an upper limit value of the weighting matrix R.

[0026] Further, the driving information comprises vehicle state information, a vehicle planning trajectory and vehicle position information of the autonomous vehicle, and the lateral control device of the autonomous vehicle further comprises a third obtaining module, configured to obtain the steering control amount by: performing coordinate conversion according to the vehicle position information and the vehicle planning trajectory to obtain a local path of the autonomous vehicle in a vehicle body coordinate system; and obtaining the steering control amount by the LQR based on the vehicle state information, the vehicle position information and the local path.

[0027] Furthermore, when the third acquisition module is used to obtain the steering control amount through LQR based on the vehicle status information, vehicle position information and local path, it is specifically used to: determine the lateral deviation and heading deviation corresponding to the autonomous driving vehicle according to the vehicle position information and the local path; and obtain the steering control amount through LQR based on the vehicle status information, lateral deviation and heading deviation.

[0028] Furthermore, the vehicle dynamics model is determined based on the lateral deviation, the heading deviation, and the vehicle state information, where the vehicle state information includes the front wheel cornering stiffness, the rear wheel cornering stiffness, the moment of inertia, the distance from the center of mass to the front axle, the distance from the center of mass to the rear axle, the mass of the autonomous vehicle, the longitudinal speed, and the front wheel turning angle.

[0029] Furthermore, the lateral deviation and the heading deviation are the lateral deviation and the heading deviation at a preset distance in front of the center of the rear axle of the autonomous driving vehicle.

[0030] Furthermore, the vehicle body coordinate system takes the center point of the rear axle of the autonomous driving vehicle as its origin.

[0031] A vehicle includes a vehicle body, front wheels and rear wheels, wherein the vehicle body is arranged between the front wheels and the rear wheels; the vehicle also includes a lateral control device of the autonomous driving vehicle as described above in the present invention, which is arranged on the vehicle body.

[0032] An electronic device includes: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the lateral control method of an autonomous driving vehicle as described above in the present invention.

[0033] A computer-readable storage medium stores computer program instructions, which, when executed, implement the lateral control method of the autonomous driving vehicle as described above in the present invention.

[0034] A computer program product includes a computer program, which, when executed, implements the lateral control method of an autonomous driving vehicle as described above in the present invention.

[0035] Beneficial effects of the present invention: The lateral control method, device, equipment and storage medium of the autonomous driving vehicle provided by the present invention dynamically adjust the weighting matrix R of LQR according to the variance of the steering control amount of the autonomous driving vehicle within a preset number of continuous control cycles; thereby, according to the adjusted weighting matrix R ′The target steering control amount of the autonomous vehicle is obtained through LQR based on the driving information of the autonomous vehicle, so as to control the steering actuator of the autonomous vehicle to execute the target steering control amount. This can effectively reduce the problem of steering wheel shaking, especially when the path curvature changes greatly. The target steering control amount obtained through LQR is smoother, thereby improving the driving stability and comfort of the entire vehicle, while also requiring less computing power from the hardware. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of the global path of an autonomous vehicle in a garage, provided for related technologies;

[0037] Figure 2 A schematic diagram of autonomous vehicles meeting each other in an underground garage, provided for related technologies;

[0038] Figure 3 A schematic diagram of an application scenario provided by an embodiment of the present invention;

[0039] Figure 4 A flowchart of a lateral control method for an autonomous driving vehicle provided in one embodiment of the present invention;

[0040] Figure 5 A flowchart of a method for obtaining a steering control value of an autonomous driving vehicle provided by one embodiment of the present invention;

[0041] Figure 6 A schematic diagram of coordinate conversion provided by an embodiment of the present invention;

[0042] Figure 7 A schematic structural diagram of a lateral control device for an autonomous driving vehicle according to an embodiment of the present invention;

[0043] Figure 8 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following describes the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art will readily appreciate the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the various details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are intended only to illustrate the present invention and are not intended to limit the scope of protection of the present invention.

[0045] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0046] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and corresponding operation entrances must be provided for users to choose to authorize or refuse.

[0047] Lateral control methods for autonomous vehicles control the vehicle's steering system to ensure it follows a predetermined path, maintaining stability and comfort. A variety of lateral control methods exist, including the Proportional-Integral-Derivative Controller (PID), LQR, Model Predictive Control (MPC), and pure tracking control.

[0048] Currently, the following method is commonly used to perform lateral control on autonomous vehicles: the optimal matrix is ​​determined based on LQR, and the steering actuator of the autonomous vehicle is controlled to execute the steering control variable obtained by multiplying the optimal matrix and the state matrix. However, in some scenarios, such as autonomous driving scenarios in underground garages, the roads in underground garages are complex, the turning radius of most garage roads is small, and at the same time, oncoming vehicles and parking in front will cause large changes in the local path curvature. Therefore, when using the above method to perform lateral control on autonomous vehicles, the steering wheel of the autonomous vehicle often switches frequently between small and large angles, resulting in large steering wheel shaking, which in turn affects the riding experience and comfort.

[0049] In one example, Figure 1 A schematic diagram of the global path of an autonomous vehicle in a garage provided for related technologies, such as Figure 1 As shown, 101 is the global path of the autonomous vehicle in the garage. Due to the complex garage environment, the autonomous vehicle often makes large-angle turns, which easily causes the steering wheel to shake, affecting the ride comfort. In another example, Figure 2 A schematic diagram of an autonomous vehicle meeting another vehicle in an underground garage, provided for related technologies, such as Figure 2As shown, due to the characteristics of relatively narrow road and small path curvature radius, the path curvature change provided by the local path planning of the automatic driving vehicle (i.e. the vehicle) is large, which causes the sharp change of the steering wheel and easily causes the shaking of the steering wheel.

[0050] In addition, in the lateral control method of the automatic driving vehicle, LQR is a widely used control method, but it has the disadvantages of poor robustness and overshoot when the curvature of the trajectory changes quickly, mainly manifested in that the steering wheel will have a large shaking, which will cause the vehicle body to be unstable when the speed of the automatic driving vehicle is high, affecting the riding experience and comfort.

[0051] Based on the above problems, the present application provides a lateral control method of an automatic driving vehicle, which dynamically adjusts the weighting matrix R of LQR based on the variance of the steering control amount of the automatic driving vehicle in a preset number of consecutive control periods, to effectively reduce the problem of steering wheel shaking, especially in the case of large path curvature change, the output of LQR control can be smoother, so as to improve the driving stability and comfort of the whole vehicle; at the same time, the requirement of hardware computing power is low.

[0052] It can be seen that the present application can be applied to an automated valet parking (AVP) system, and the main application scenario is an underground garage. The present application can be applied to the lateral control of a complex garage environment, and can ensure the stability of the steering wheel of the automatic driving vehicle and improve the driving stability of the automatic driving vehicle in the case of large path curvature change.

[0053] Hereinafter, first, the application scenario of the scheme provided by the present application is exemplarily illustrated.

[0054] Figure 3 The application scenario provided by an embodiment of the present application is shown in the figure. Figure 3 As shown, in the application scenario, when the automatic driving vehicle automatically parks in the underground garage, the target steering control amount obtained by the lateral control method of the automatic driving vehicle provided by the present application is used to control the steering actuator of the automatic driving vehicle to execute the target steering control amount, so as to make the automatic driving vehicle smoothly enter the parking space and complete the parking process.

[0055] It should be noted that, Figure 3 The figure is only a schematic diagram of an application scenario provided by an embodiment of the present application, and the embodiment of the present application does not limit the devices included in the figure, nor the positional relationship between the devices in the figure. Figure 3 The figure is only a schematic diagram of an application scenario provided by an embodiment of the present application, and the embodiment of the present application does not limit the devices included in the figure, nor the positional relationship between the devices in the figure. Figure 3 The figure is only a schematic diagram of an application scenario provided by an embodiment of the present application, and the embodiment of the present application does not limit the devices included in the figure, nor the positional relationship between the devices in the figure.

[0056] The technical solution of the present invention is described in detail below through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0057] Figure 4 This is a flow chart of a lateral control method for an autonomous vehicle provided by an embodiment of the present invention. The lateral control method for an autonomous vehicle can be executed by software and / or hardware devices. For example, the hardware device can be a lateral control device for an autonomous vehicle, and the lateral control device for an autonomous vehicle can be an electronic device or a processing chip in an electronic device. Figure 4 As shown, the method of the embodiment of the present invention includes:

[0058] S401. Obtain a variance of a steering control amount of the autonomous driving vehicle within a preset number of consecutive control cycles, where the steering control amount is obtained based on driving information of the autonomous driving vehicle through LQR, which is obtained based on a vehicle dynamics model.

[0059] In an embodiment of the present invention, the preset number is, for example, 10. It is understood that if the preset number is too small, the changing trend of the steering control amount (steering wheel angle) may not be accurately obtained, and the obtained variance may also be a random deviation. The control period is, for example, 10ms. The driving information of the autonomous vehicle may include, for example, the vehicle state information, the vehicle planned trajectory, and the vehicle position information of the autonomous vehicle. The vehicle state information may include, for example, the longitudinal speed of the autonomous vehicle and the vehicle status. The vehicle planned trajectory is obtained based on the current path and the autonomous vehicle's environmental information. The autonomous vehicle's environmental information may include, for example, perception information obtained through forward vision, perimeter vision, surround vision, and two laser radars arranged on both sides of the vehicle's front. After sensor fusion processing, the output is a free space (FS) point (i.e., a passable boundary point), target information (such as information about the vehicle in front of the autonomous vehicle, information about oncoming vehicles, etc.), and ultrasonic ranging information. The vehicle position information is vehicle positioning information, such as the latitude and longitude of the current point output by the Global Positioning System (GPS), and the plane coordinate point obtained through internal conversion. The LQR is obtained based on the vehicle dynamics model. For details on the specific vehicle dynamics model and how to obtain the LQR based on the vehicle dynamics model, please refer to the subsequent embodiments. The steering control amount can be obtained using the LQR based on the driving information of the autonomous vehicle. For details on how to obtain the steering control amount, please refer to the subsequent embodiments.

[0060] In this step, for example, the steering control amount of the autonomous vehicle within 10 consecutive control cycles can be obtained, and the variance of the steering control amount within the 10 consecutive control cycles can be obtained. It is understood that the variance of the steering control amount within the 10 consecutive control cycles can be obtained sequentially in chronological order using a sliding window method.

[0061] S402: Determine whether to adjust the LQR weighting matrix R according to the variance.

[0062] It is understood that the control parameters of LQR include weighting matrix R and weighting matrix Q. The embodiment of the present invention adjusts the LQR weighting matrix R. In this step, after obtaining the variance, it can be determined whether to adjust the LQR weighting matrix R based on the variance.

[0063] Further, optionally, determining whether to adjust the LQR weighting matrix R based on the variance may include: if the variance is greater than or equal to an angle threshold, determining to adjust the LQR weighting matrix R; if the variance is less than the angle threshold, determining not to adjust the LQR weighting matrix R.

[0064] For example, the angle threshold is 200 degrees. If the variance is greater than or equal to 200 degrees, the LQR weighting matrix R is adjusted. If the variance is less than 200 degrees, the LQR weighting matrix R is not adjusted, that is, R = [1]. In this case, the tracking accuracy of the lateral error and heading error of the autonomous vehicle can be guaranteed.

[0065] S403: If yes, adjust the LQR weighting matrix R according to the variance to obtain the adjusted weighting matrix R ′ .

[0066] In this step, after determining the weighted matrix R for adjusting the LQR, the weighted matrix R for adjusting the LQR can be adjusted according to the variance to obtain the adjusted weighted matrix R ′ .

[0067] Furthermore, optionally, the adjusted weight matrix R ′ Satisfy the following formula 1:

[0068] R ′ =min(R*(σ 2 -threshold)*a,b) Formula 1

[0069] Among them, σ 2 represents the variance; threshold represents the angle threshold; a represents the scaling factor; and b represents the upper limit of the weighting matrix R.

[0070] For example, if threshold is 200 degrees, a is 1 / 50, and b is 15, then the weight matrix R and variance σ of LQR are2 Inputting the above formula one, the adjusted weighting matrix R can be obtained ′ It can be understood that increasing the weighting matrix R, the request angle of the steering wheel of the autonomous vehicle will be smoother, thereby improving the comfort of the lateral control. The embodiment of the present application suggests that the threshold is 200 degrees, a is 1 / 50, and b is 15.

[0071] S404, according to the adjusted weighting matrix R ′ and the driving information of the autonomous vehicle, the target steering control amount of the autonomous vehicle is obtained through the LQR to control the steering actuator of the autonomous vehicle to execute the target steering control amount.

[0072] In this step, after obtaining the adjusted weighting matrix R ′ , the weighting matrix R of the LQR can be replaced by the adjusted weighting matrix R ′ , so that the target steering control amount of the autonomous vehicle is obtained through the LQR according to the adjusted weighting matrix R ′ and the driving information of the autonomous vehicle. After obtaining the target steering control amount, the target steering control amount can be output to the steering actuator of the autonomous vehicle through the controller area network (CAN) bus, so as to control the steering actuator to execute the target steering control amount, effectively reduce the problem of steering wheel shaking, improve the vehicle smoothness and comfort, and the requirement for the computing power of the hardware is also low.

[0073] The lateral control method of the autonomous vehicle provided by the embodiment of the present application can dynamically adjust the weighting matrix R of the LQR according to the variance of the steering control amount of the autonomous vehicle in a preset number of continuous control periods; and obtain the target steering control amount of the autonomous vehicle through the LQR according to the adjusted weighting matrix R ′ and the driving information of the autonomous vehicle, so as to control the steering actuator of the autonomous vehicle to execute the target steering control amount. The problem of steering wheel shaking can be effectively reduced, especially in the case of large path curvature change, the target steering control amount obtained through the LQR is more smooth, thereby the driving stability and comfort of the vehicle can be improved, and the requirement for the computing power of the hardware is also low.

[0074] On the basis of the above embodiment, Figure 5 the flowchart of the method for obtaining the steering control amount of the autonomous vehicle provided by an embodiment of the present application is shown. As Figure 5 shown, the method of the embodiment of the present application can include:

[0075] S501, obtaining the driving information of the autonomous vehicle, the driving information including the vehicle state information, the vehicle planning trajectory and the vehicle position information of the autonomous vehicle.

[0076] For example, the vehicle status information of an autonomous vehicle may include the longitudinal speed of the autonomous vehicle, the vehicle status, etc. The vehicle planning trajectory is obtained based on the current path and the autonomous vehicle's environmental information. The autonomous vehicle's environmental information may include, for example, perception information obtained through forward vision, perimeter vision, surround vision, and two lidars arranged on both sides of the vehicle's front. After sensor fusion processing, the output includes FS points (i.e., passable boundary points), target information (such as information about the vehicle in front of the autonomous vehicle, information about oncoming vehicles, etc.), and ultrasonic ranging information. Vehicle position information is vehicle positioning information, such as the latitude and longitude of the current point output by GPS, and the plane coordinate points obtained through internal conversion.

[0077] S502: Perform coordinate conversion based on the vehicle position information and the planned vehicle trajectory to obtain a local path in the vehicle body coordinate system corresponding to the autonomous driving vehicle.

[0078] It can be understood that the coordinate transformation of the vehicle planned trajectory is performed according to the vehicle position information, and the trajectory coordinate points are converted from the global coordinate system (corresponding to the global path) to the trajectory coordinate points in the vehicle body coordinate system (corresponding to the local path). This can be used more conveniently to obtain the corresponding lateral deviation and heading deviation of the autonomous driving vehicle.

[0079] Optionally, the vehicle coordinate system takes the center point of the rear axle of the autonomous driving vehicle as its origin.

[0080] For example, the center point of the rear axle of the autonomous vehicle is used as the origin of the vehicle coordinate system. Then, coordinate transformation can be performed based on the vehicle position information and the vehicle's planned trajectory to obtain the local path in the vehicle coordinate system corresponding to the autonomous vehicle. The specific coordinate transformation formula is as follows:

[0081]

[0082] Where X represents the horizontal coordinate of the autonomous vehicle in the global coordinate system; Y represents the vertical coordinate of the autonomous vehicle in the global coordinate system; p Represents the horizontal coordinate of the autonomous vehicle's positioning in the global coordinate system; p represents the positioning ordinate of the autonomous vehicle in the global coordinate system; h represents the heading angle of the autonomous vehicle in the global coordinate system; x v Represents the horizontal coordinate of the autonomous vehicle in the vehicle coordinate system; y v Represents the vertical coordinate of the autonomous vehicle in the vehicle coordinate system.

[0083] For example, Figure 6 A schematic diagram of coordinate conversion provided by an embodiment of the present invention is shown in FIG. Figure 6As shown in the figure, X represents the horizontal coordinate of the autonomous vehicle in the global coordinate system, and Y represents the vertical coordinate of the autonomous vehicle in the global coordinate system. x represents the horizontal coordinate of the autonomous vehicle in the vehicle body coordinate system, and y represents the vertical coordinate of the autonomous vehicle in the vehicle body coordinate system. h represents the heading angle of the autonomous vehicle in the global coordinate system. The planned vehicle trajectory can be converted from the global coordinate system to trajectory coordinate points in the vehicle body coordinate system based on the vehicle position information.

[0084] S503 : Based on the vehicle state information, the vehicle position information and the local path, obtain the steering control amount through LQR.

[0085] In this step, after obtaining the local path in the vehicle coordinate system corresponding to the autonomous driving vehicle, the steering control amount can be obtained through LQR based on the vehicle status information, vehicle position information and local path.

[0086] Furthermore, optionally, obtaining a steering control amount through LQR based on vehicle status information, vehicle position information and a local path may include: determining a lateral deviation and a heading deviation corresponding to the autonomous driving vehicle according to the vehicle position information and the local path; and obtaining a steering control amount through LQR based on the vehicle status information, the lateral deviation and the heading deviation.

[0087] For example, the lateral deviation and heading deviation corresponding to the autonomous vehicle can be determined based on the vehicle position information and the local path. Optionally, the lateral deviation and heading deviation are the lateral deviation and heading deviation at a preset distance in front of the center of the rear axle of the autonomous vehicle. For example, the preset distance is, for example, 1 meter. It will be understood that in order to obtain the lateral deviation and heading deviation close to the center of mass of the autonomous vehicle, the lateral deviation and heading deviation at 1 meter in front of the center of the rear axle of the autonomous vehicle are usually obtained to approximate the lateral deviation and heading deviation at the center of mass.

[0088] The LQR is obtained based on a vehicle dynamics model. Optionally, the vehicle dynamics model is determined based on lateral deviation, heading deviation, and vehicle state information, including the autonomous vehicle's front wheel cornering stiffness, rear wheel cornering stiffness, moment of inertia, distance from the center of mass to the front axle, distance from the center of mass to the rear axle, mass of the autonomous vehicle, longitudinal velocity, and front wheel steering angle.

[0089] For example, refer to Figure 6 , the vehicle dynamics model can be obtained as the following formula 3:

[0090]

[0091] Where, e1 represents the lateral deviation between positioning (i.e., vehicle position information) and path (i.e., local path); e2 represents the heading deviation; C αf Indicates the front wheel cornering stiffness; Cαr Represents the rear wheel cornering stiffness; I z represents the moment of inertia of the autonomous vehicle; l f Indicates the distance from the center of mass to the front axle; l r represents the distance from the center of mass to the rear axle; m represents the mass of the autonomous vehicle; v x represents the longitudinal speed; δ represents the front wheel turning angle.

[0092] The above formula 3 is simplified as the following formula 4:

[0093]

[0094] Based on the vehicle dynamics model, the objective function of LQR control can be defined as the following formula 5:

[0095]

[0096] Where J represents the function name of the objective function; k represents the kth control cycle; δ(k) represents the steering control amount.

[0097] The optimal control output based on the objective function is as follows:

[0098] δ(k)=-Kx(k) Formula 6

[0099] in, Where P satisfies the following Riccati equation (Formula 7):

[0100]

[0101] Among them, T represents transposition; A d represents the discretized form of A; B d Represents the discretized form of B.

[0102] By solving Formula 7, P can be obtained, and then K can be obtained, and then the final optimal steering control amount can be obtained through Formula 6.

[0103] It can be understood that according to the LQR control theory, the larger the diagonal coefficient of the weighting matrix Q is, the larger the corresponding state variable (i.e. )The faster the convergence speed, the smaller the diagonal coefficient of the weighted matrix Q, and the corresponding state variable (i.e. ) The slower the convergence speed. The larger the coefficients of the weighting matrix R, the smaller the control output of the LQR will be, ensuring that J in the objective function (Formula 5) is minimized. To make the control output of the LQR smoother, the present invention dynamically adjusts the coefficients of the weighting matrix R based on the variance of the LQR control output (i.e., the steering control amount) within 10 control cycles, so that the variance of the control output remains below a certain limit, thereby improving the driving stability and comfort of the entire vehicle.

[0104] The method for obtaining the steering control amount of an autonomous driving vehicle provided by an embodiment of the present invention obtains driving information of the autonomous driving vehicle, where the driving information includes vehicle status information, vehicle planned trajectory and vehicle position information of the autonomous driving vehicle; coordinate conversion is performed according to the vehicle position information and the vehicle planned trajectory to obtain a local path in the vehicle body coordinate system corresponding to the autonomous driving vehicle; based on the vehicle status information, vehicle position information and the local path, the steering control amount is obtained through LQR, so that the steering control amount can be accurately obtained.

[0105] Based on the above embodiments, you can refer to Figure 5 In the embodiment shown, after obtaining the adjusted weight matrix R ′ Then, replace the weight matrix R in Formula 5 with the adjusted weight matrix R ′ , so that the adjusted weight matrix R ′ The target steering control amount of the autonomous vehicle can be obtained through LQR based on the driving information of the autonomous vehicle, and then the steering actuator can be controlled to execute the target steering control amount, which can effectively reduce the problem of steering wheel shaking and improve the driving stability and comfort of the whole vehicle.

[0106] The following are embodiments of the apparatus of the present invention, which can be used to implement the method embodiments of the present invention. For details not disclosed in the apparatus embodiments of the present invention, please refer to the method embodiments of the present invention.

[0107] Figure 7 This is a schematic diagram of the structure of a lateral control device for an autonomous driving vehicle provided by one embodiment of the present invention. Figure 7 As shown, the lateral control device 700 of the autonomous driving vehicle according to the embodiment of the present invention includes: a first acquisition module 701, a determination module 702, an adjustment module 703, and a second acquisition module 704.

[0108] The first acquisition module 701 is used to obtain the variance of the steering control amount of the autonomous driving vehicle within a preset number of continuous control cycles. The steering control amount is obtained based on the driving information of the autonomous driving vehicle through a linear quadratic regulator LQR, and the LQR is obtained based on the vehicle dynamics model.

[0109] The determination module 702 is configured to determine whether to adjust the weight matrix R of the LQR according to the variance.

[0110] Adjustment module 703 is used to adjust the weight matrix R of LQR according to the variance to obtain the adjusted weight matrix R ′ .

[0111] The second acquisition module 704 is used to obtain the weight matrix R ′ and the driving information of the autonomous vehicle, and obtain the target steering control amount of the autonomous vehicle through LQR to control the steering actuator of the autonomous vehicle to execute the target steering control amount.

[0112] Furthermore, the determination module 702 may be specifically configured to: if the variance is greater than or equal to the angle threshold, determine to adjust the LQR weighting matrix R; if the variance is less than the angle threshold, determine not to adjust the LQR weighting matrix R.

[0113] Furthermore, the adjustment module 703 can be specifically used to: adjust the weight matrix R ′ Satisfy the following formula:

[0114] R ′ =min(R*(σ 2 -threshold)*a,b)

[0115] Among them, σ 2 represents the variance; threshold represents the angle threshold; a represents the scaling factor; and b represents the upper limit of the weighting matrix R.

[0116] Furthermore, the driving information includes vehicle status information, vehicle planned trajectory and vehicle position information of the autonomous driving vehicle. The lateral control device 700 of the autonomous driving vehicle may also include a third acquisition module (not shown in the figure) for obtaining the steering control amount in the following manner: performing coordinate conversion according to the vehicle position information and the vehicle planned trajectory to obtain the local path in the vehicle body coordinate system corresponding to the autonomous driving vehicle; based on the vehicle status information, vehicle position information and local path, obtaining the steering control amount through LQR.

[0117] Furthermore, when the third acquisition module is used to obtain the steering control amount through LQR based on the vehicle status information, vehicle position information and local path, it can be specifically used to: determine the lateral deviation and heading deviation corresponding to the autonomous driving vehicle based on the vehicle position information and the local path; and obtain the steering control amount through LQR based on the vehicle status information, lateral deviation and heading deviation.

[0118] Furthermore, the vehicle dynamics model is determined based on the lateral deviation, the heading deviation, and the vehicle state information, where the vehicle state information includes the front wheel cornering stiffness, the rear wheel cornering stiffness, the moment of inertia, the distance from the center of mass to the front axle, the distance from the center of mass to the rear axle, the mass of the autonomous vehicle, the longitudinal speed, and the front wheel turning angle.

[0119] Furthermore, the lateral deviation and the heading deviation are the lateral deviation and the heading deviation at a preset distance in front of the center of the rear axle of the autonomous driving vehicle.

[0120] Furthermore, the vehicle body coordinate system takes the center point of the rear axle of the autonomous driving vehicle as its origin.

[0121] The device of the embodiment of the present invention can be used to execute the technical solution of any of the above-mentioned method embodiments. Its implementation principles and technical effects are similar and will not be repeated here.

[0122] The present invention also provides a vehicle comprising a vehicle body, front wheels and rear wheels, wherein the vehicle body is arranged between the front wheels and the rear wheels; the vehicle also comprises a lateral control device of the autonomous driving vehicle as described above in the embodiment of the present invention, which is arranged on the vehicle body.

[0123] Figure 8 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 8 As shown, the electronic device 800 may include: at least one processor 801 and a memory 802.

[0124] The memory 802 is used to store programs. Specifically, the programs may include program codes, and the program codes include computer-executable instructions.

[0125] The memory 802 may include a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0126] The processor 801 is used to execute the computer-executable instructions stored in the memory 802 to implement the lateral control method of the autonomous driving vehicle described in the aforementioned method embodiment. Among them, the processor 801 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. Specifically, when implementing the lateral control method of the autonomous driving vehicle described in the aforementioned method embodiment, the electronic device may be, for example, an electronic power steering system (EPS) of the steering wheel of the autonomous driving vehicle.

[0127] Optionally, the electronic device 800 may further include a communication interface 803. In a specific implementation, if the communication interface 803, the memory 802, and the processor 801 are implemented independently, the communication interface 803, the memory 802, and the processor 801 may be interconnected via a bus and communicate with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be divided into address buses, data buses, control buses, etc., but this does not mean that there is only one bus or only one type of bus.

[0128] Optionally, in a specific implementation, if the communication interface 803, the memory 802 and the processor 801 are integrated on a chip, the communication interface 803, the memory 802 and the processor 801 can complete communication through an internal interface.

[0129] The present invention also provides a computer-readable storage medium, which stores computer program instructions. When a processor executes the computer program instructions, the above-mentioned lateral control method for an autonomous driving vehicle is implemented.

[0130] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for lateral control of an autonomous driving vehicle.

[0131] The computer-readable storage medium may be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The computer-readable storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0132] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Alternatively, the readable storage medium may be an integral part of the processor. The processor and the readable storage medium may be located in an application-specific integrated circuit. Alternatively, the processor and the readable storage medium may be discrete components within a lateral control device for an autonomous vehicle.

[0133] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0134] Finally, it should be noted that the above embodiments are only preferred embodiments for fully illustrating the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention.

Claims

1. A lateral control method for an autonomous driving vehicle, characterized in that: include: Obtaining a variance of a steering control variable of the autonomous vehicle within a preset number of consecutive control cycles, the steering control variable being obtained based on driving information of the autonomous vehicle through a linear quadratic regulator (LQR), wherein the LQR is obtained based on a vehicle dynamics model; Determining whether to adjust the weighting matrix R of the LQR according to the variance; If yes, then adjust the LQR weighting matrix R according to the variance to obtain the adjusted weighting matrix R ′ ; According to the adjusted weight matrix R ′ and the driving information of the autonomous driving vehicle, and obtain the target steering control amount of the autonomous driving vehicle through the LQR to control the steering actuator of the autonomous driving vehicle to execute the target steering control amount.

2. The lateral control method of an autonomous driving vehicle according to claim 1, characterized in that: The determining, based on the variance, whether to adjust the weighting matrix R of the LQR includes: If the variance is greater than or equal to an angle threshold, determining to adjust a weighting matrix R for the LQR; If the variance is less than the angle threshold, it is determined not to adjust the weighting matrix R of the LQR.

3. The lateral control method of an autonomous driving vehicle according to claim 2, characterized in that: The weight matrix R of the LQR is adjusted according to the variance to obtain the adjusted weight matrix R ′ ,include: The adjusted weight matrix R ′ Satisfy the following formula: R ′ =min(R*(σ 2 -threshold)*a,b) Among them, σ 2 represents the variance; threshold represents the angle threshold; a represents the scaling factor; b represents the upper limit value of the weighting matrix R.

4. The lateral control method for an autonomous driving vehicle according to any one of claims 1 to 3, characterized in that: The driving information includes vehicle state information, vehicle planning trajectory, and vehicle position information of the autonomous driving vehicle, and the steering control amount is obtained by: Performing coordinate conversion based on the vehicle position information and the planned vehicle trajectory to obtain a local path in the vehicle coordinate system corresponding to the autonomous driving vehicle; The steering control amount is acquired through the LQR based on the vehicle state information, the vehicle position information, and the local path.

5. The lateral control method of an autonomous driving vehicle according to claim 4, characterized in that: The acquiring the steering control amount through the LQR based on the vehicle state information, the vehicle position information, and the local path includes: Determining a lateral deviation and a heading deviation corresponding to the autonomous driving vehicle based on the vehicle position information and the local path; The steering control amount is acquired through the LQR based on the vehicle state information, the lateral deviation and the heading deviation.

6. The lateral control method of an autonomous driving vehicle according to claim 5, characterized in that: The vehicle dynamics model is determined based on the lateral deviation, the heading deviation, and the vehicle state information, wherein the vehicle state information includes the front wheel cornering stiffness, rear wheel cornering stiffness, moment of inertia, distance from the center of mass to the front axle, distance from the center of mass to the rear axle, mass of the autonomous vehicle, longitudinal speed, and front wheel turning angle.

7. The lateral control method of an autonomous driving vehicle according to claim 6, characterized in that: The lateral deviation and the heading deviation are the lateral deviation and the heading deviation at a preset distance in front of the rear axle center of the autonomous driving vehicle.

8. The lateral control method of an autonomous driving vehicle according to claim 4, characterized in that: The vehicle body coordinate system takes the center point of the rear axle of the autonomous driving vehicle as its origin.

9. A lateral control device for an autonomous driving vehicle, characterized in that: include: a first acquisition module, configured to acquire a variance of a steering control variable of the autonomous vehicle within a preset number of consecutive control cycles, wherein the steering control variable is obtained based on driving information of the autonomous vehicle through a linear quadratic regulator (LQR), wherein the LQR is obtained based on a vehicle dynamics model; a determination module, configured to determine whether to adjust the weighting matrix R of the LQR according to the variance; An adjustment module is configured to adjust the weight matrix R of the LQR according to the variance to obtain an adjusted weight matrix R ′ ; The second acquisition module is used to obtain the weight matrix R after adjustment. ′ and the driving information of the autonomous driving vehicle, and obtain the target steering control amount of the autonomous driving vehicle through the LQR to control the steering actuator of the autonomous driving vehicle to execute the target steering control amount.

10. A vehicle, characterized in that: The vehicle comprises a vehicle body, front wheels and rear wheels, wherein the vehicle body is arranged between the front wheels and the rear wheels; the vehicle also comprises a lateral control device for an autonomous driving vehicle as claimed in claim 9, which is arranged on the vehicle body.

11. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the lateral control method of the autonomous driving vehicle according to any one of claims 1 to 8.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, which, when executed, implement the lateral control method for an autonomous driving vehicle according to any one of claims 1 to 8.

13. A computer program product comprising a computer program, characterized in that When the computer program is executed, the lateral control method of the autonomous driving vehicle according to any one of claims 1 to 8 is implemented.

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