Vehicle control method and device and vehicle
By calculating the transfer function and optimization function, determining the vehicle input value, the problem of low control accuracy in vehicle autonomous driving is solved, and more stable and accurate vehicle trajectory tracking is achieved.
Patent Information
- Application Number
- CN202510479663.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-16
- Publication Date
- 2025-06-17
AI Technical Summary
The prior art is difficult to improve control accuracy in vehicle autonomous driving, especially when the curvature towards the control and planned trajectory is not smooth.
Acquisition of precise control of the vehicle's state by obtaining the preset trajectory of the target vehicle, determining multiple sampling points, and calculating transfer functions and optimization functions to determine the input value of the vehicle (such as the steering wheel rotation angle or angular velocity).
The vehicle control accuracy is improved, the stability and accuracy of the vehicle driving along the preset trajectory is enhanced, the calculation amount is reduced, and the accuracy of the input value is improved.
Smart Images
Figure CN120156549A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of vehicles, and particularly to a vehicle control method, device and vehicle. Background Art
[0002] In the field of autonomous driving, it is often necessary to control a vehicle to travel along a planned trajectory to complete functions such as lane changing and automatic parking. During this process, it is often necessary to precisely control the steering wheel, accelerator and brake of the vehicle. The control algorithm for the steering wheel is also called lateral control, and the control of the accelerator and brake is also called longitudinal control. Currently in the industry, lateral control and longitudinal control are often decoupled and algorithms are designed separately. Among them, the lateral control algorithm greatly affects the error size and stability during vehicle driving.
[0003] In related technologies, the lateral control algorithm is often designed based on the lateral distance error and curvature of the preview trajectory points. However, it is difficult to adapt to situations where the vehicle orientation control accuracy requirement is high and the planned trajectory curvature is not smooth. Moreover, when using single-point preview, the tracking effect is not stable and is greatly affected by the preview point selection method.
[0004] Therefore, how to improve the control accuracy of the vehicle has become an urgent problem to be solved. Summary of the Invention
[0005] Based on this, in view of the above technical problems, it is necessary to provide a vehicle control method, device and vehicle that can improve the control accuracy of the vehicle.
[0006] In a first aspect, the present application provides a vehicle control method, and the vehicle control method includes:
[0007] Obtain a preset trajectory of a target vehicle and determine a plurality of sampling points in the preset trajectory;
[0008] Determine the sampling angle of the target vehicle at each sampling point and the specified state value of the target vehicle, where the sampling angle includes the orientation angle value of the target vehicle relative to the sampling point, and the specified state value includes the predicted value of the target vehicle state;
[0009] Based on the specified state value and the input value of the target vehicle, calculate the transfer function of the target vehicle, where the transfer function is used to represent the state change relationship of the target vehicle;
[0010] Form an optimization function of the target vehicle, where the optimization function is used to measure the driving error of the target vehicle;
[0011] Calculate the transfer function and the optimization function, and when the optimization function has a pre-designed calculation result, determine the input value of the target vehicle as the control output value;
[0012] Control the target vehicle based on the control output value.
[0013] In one embodiment, the calculating the transfer function and the optimization function, and when the optimization function has a pre-designed calculation result, determining the input value of the target vehicle as the control output value includes:
[0014] Calculate the optimization function based on a plurality of the specified state values and the input value of the target vehicle to obtain a plurality of optimization values;
[0015] Determine the minimum value among the plurality of optimization values;
[0016] Determine the input value of the target vehicle corresponding to the minimum value as the control output value.
[0017] In one embodiment, the forming the optimization function of the target vehicle includes:
[0018] Determine the optimization function of the target vehicle based on the error between the vehicle driving angle and the sampling angle of the target vehicle, the lateral distance error, and the input value of the target vehicle.
[0019] In one embodiment, the obtaining the preset trajectory of the target vehicle and determining a plurality of sampling points in the preset trajectory includes:
[0020] Establish a trajectory coordinate system of the preset trajectory;
[0021] Establish a vehicle coordinate system of the target vehicle;
[0022] Determine the intersection point of the preset trajectory and the vehicle coordinate system as the first sampling point;
[0023] Based on the first sampling point and the sampling interval, determine a plurality of remaining sampling points in the preset trajectory.
[0024] In one embodiment, the based on the first sampling point and the sampling interval, determining a plurality of remaining sampling points in the preset trajectory includes:
[0025] Determine the sampling interval based on the current speed value of the target vehicle and the sampling period;
[0026] Based on the first sampling point and the sampling interval, determine a plurality of remaining sampling points in the preset trajectory until the number of sampling points reaches a preset number.
[0027] In one embodiment, establishing the vehicle coordinate system of the target vehicle includes:
[0028] Taking the center point of the rear axle of the target vehicle as the origin of the vehicle coordinate system;
[0029] Determining the first axis of the vehicle coordinate system;
[0030] Based on the origin of the vehicle coordinate system and the first axis, determining a second axis, where the second axis is perpendicular to the first axis.
[0031] In one embodiment, determining the intersection point of the preset trajectory and the vehicle coordinate system as the first sampling point includes:
[0032] Determining the intersection point of the preset trajectory and the second axis of the vehicle coordinate system as the first sampling point.
[0033] In one embodiment, the input value of the target vehicle includes the rotation angle of the steering wheel of the target vehicle, or the angular velocity of the steering wheel.
[0034] In a second aspect, the present application also provides a vehicle control device, which includes:
[0035] An acquisition module, configured to acquire the preset trajectory of the target vehicle and determine a plurality of sampling points in the preset trajectory;
[0036] A determination module, configured to determine the sampling angle of the target vehicle at each sampling point and the specified state value of the target vehicle, where the sampling angle includes the orientation angle value of the target vehicle relative to the sampling point, and the specified state value includes the predicted value of the target vehicle state;
[0037] A first calculation module, configured to calculate the transfer function of the target vehicle based on the specified state value and the input value of the target vehicle, where the transfer function is used to represent the state change relationship of the target vehicle;
[0038] A formation module, configured to form an optimization function of the target vehicle, where the optimization function is used to measure the driving error of the target vehicle;
[0039] A second calculation module, configured to calculate the transfer function and the optimization function, and when the optimization function has a preset calculation result, determine the input value of the target vehicle as the control output value;
[0040] A control module, configured to control the target vehicle based on the control output value.
[0041] In a third aspect, the present application further provides a vehicle that executes the vehicle control method described in any of the foregoing embodiments.
[0042] For the above vehicle control method, device, and vehicle, first, through the specified state value of the target vehicle and the transfer function, the continuous form trajectory of the target vehicle can be accurately obtained. Secondly, by setting an optimization function in the present application, the input value (for example, the steering wheel rotation angle or the angular velocity of the steering wheel) when the target vehicle is closest to the preset trajectory can be quickly obtained, and the calculation amount is small. At the same time, by determining multiple sampling points in the preset trajectory in the present application, the accuracy of the input value when the target vehicle is closest to the preset trajectory can be further improved. BRIEF DESCRIPTION OF THE DRAWINGS
[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for the description of the embodiments of the present application or related technologies. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.
[0044] Figure 1 It is a schematic flowchart of the vehicle control method provided in an embodiment;
[0045] Figure 2 It is a schematic diagram of vehicle control provided in an embodiment;
[0046] Figure 3 It is a schematic diagram of the coordinate system provided in an embodiment;
[0047] Figure 4 It is a schematic diagram of the calculation of the optimization function provided in an embodiment;
[0048] Figure 5 It is a schematic flowchart of the sampling point determination method provided in an embodiment;
[0049] Figure 6 It is a schematic flowchart of the coordinate system establishment method provided in an embodiment;
[0050] Figure 7 It is a schematic flowchart of the sampling point determination method provided in another embodiment;
[0051] Figure 8 It is a structural block diagram of the vehicle control device provided in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0052] To make the objectives, technical solutions, and advantages of this application more clear and understandable, the following further elaborates on this application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for explaining this application and are not used to limit this application.
[0053] The vehicle control method provided by the embodiments of this application can be applied to movable products such as vehicles. As an example, a computer device can be integrated in movable products such as vehicles, and this computer device can control the steering wheel of the vehicle, etc. Specifically, this computer device can make the vehicle, etc., travel along a trajectory by controlling the steering wheel angle or the angular velocity of the steering wheel of the vehicle, etc.
[0054] In one embodiment, as Figure 1 shown, a vehicle control method is provided. As an example, please refer to Figure 2 , in this embodiment, the preset trajectory of the target vehicle, the specified state value, etc. can be input into the angle sampler to obtain the sampled angle, and then data such as the sampled angle is input into the controller, and the steering wheel angle or the angular velocity of the steering wheel of the target vehicle, etc. is output through the controller, so that the target vehicle can travel along the preset trajectory.
[0055] Specifically, the vehicle control method can include the following steps:
[0056] Step S10: Obtain the preset trajectory of the target vehicle and determine multiple sampling points in the preset trajectory.
[0057] The preset trajectory of the target vehicle (as shown by the green line in Figure 3 ) can include the forward trajectory or the reverse trajectory of the target vehicle (for example, in a parking scenario), etc. The preset trajectory can be determined according to factors such as the environment where the target vehicle is located and the size of the target vehicle.
[0058] Multiple sampling points can be determined in the preset trajectory. As an example, 10 or 20 sampling points can be determined in the preset trajectory. There can be a preset distance between adjacent sampling points. As an example, the distance between adjacent sampling points can be 0.1 m, 0.5 m, etc. The number of the above sampling points and the distance between adjacent sampling points are only for illustrative purposes.
[0059] Step S20: Determine the sampling angle of the target vehicle at each sampling point and the specified state value of the target vehicle. The sampling angle includes the orientation angle value of the target vehicle relative to the sampling point, and the specified state value includes the predicted value of the state of the target vehicle.
[0060] The sampling angle can be used to represent the orientation angle of the target vehicle when it travels to this sampling point based on the preset trajectory.
[0061] The specified state value of the target vehicle may include the lateral distance error, etc. Specifically, the lateral distance error may refer to the distance by which the target vehicle laterally deviates from the sampling point. It can be understood that if the lateral distance error is too large, it may cause the target vehicle to deviate from the sampling point or collide with other vehicles. If the lateral distance error is small, it indicates that the target vehicle is traveling along the preset trajectory.
[0062] Step S30: Calculate the transfer function of the target vehicle based on the specified state value and the input value of the target vehicle. The transfer function is used to represent the relationship of the state change of the target vehicle.
[0063] The transfer function of the target vehicle can describe how the state of the target vehicle changes over time. As an example, a vehicle dynamics or kinematics model, etc., can be used, in combination with the specified state value and the input value (such as the rotation angle of the steering wheel of the target vehicle, or the angular velocity of the steering wheel, etc.), to calculate the transfer function. The specific form of the transfer function is not limited in this embodiment.
[0064] Step S40: Form an optimization function for the target vehicle. The optimization function is used to measure the driving error of the target vehicle.
[0065] As an example, an optimization function can be formed based on the sampling angle, the specified state value, and possible other constraints (such as road boundaries, speed limits, etc.). Specifically, the optimization function can be expressed as the sum or weighted sum of the driving errors of the target vehicle.
[0066] In a possible example, step S40 may include:
[0067] Step S41: Determine the optimization function of the target vehicle based on the error between the vehicle driving angle and the sampling angle of the target vehicle, the lateral distance error, and the input value of the target vehicle.
[0068] As an example, the optimization function of the target vehicle can be expressed using the following formula:
[0069]
[0070] The above optimization function can be a quadratic programming function. Among them, R1 = C TQC…C can represent the observation matrix. For example, at this time, it can be stipulated that there must be a row whose value in the L-th column (L can represent the row number where θ is located) is 1, the value in the (m + 2)-th column is -1 (m is the number of additional states), and the values in the remaining columns are all zero. The values in the remaining columns are all zero, and this row represents the observation result of the angular error. Q is a diagonal positive definite matrix, representing the weights of each observation state. R2 is a real number greater than zero, representing the weight of u(k). Specifically, from the above optimization function, the optimization function J can be expressed as the weighted sum of the error between the driving angle of the target vehicle and the sampling angle, the lateral distance error, and the input value of the target vehicle. The first two are at N moments, and the last one is at N - 1 moments.
[0071] Of course, the optimization function of this application is not limited to the above formula.
[0072] Step S50: Calculate the transfer function and the optimization function, and when the optimization function has a preset calculation result, determine the input value of the target vehicle as the control output value.
[0073] Specifically, the optimization function can be used to determine the optimal input value of the target vehicle so that the target vehicle is as close as possible to the preset trajectory form. Further, during the optimization process, the input value of the target vehicle can be continuously adjusted until the optimization function reaches the preset calculation result.
[0074] In a possible example, please refer to Figure 4 , step S50 may include:
[0075] Step S51: Calculate the optimization function based on multiple specified state values and the input value of the target vehicle, and obtain multiple optimization values.
[0076] As an example, each optimization value can correspond one-to-one to each input value of the target vehicle.
[0077] Step S52: Determine the minimum value among the multiple optimization values.
[0078] As an example, the multiple optimization values can be sorted, and the smallest optimization value can be determined among the multiple optimization values.
[0079] Step S53: Determine the input value of the target vehicle corresponding to the minimum value as the control output value.
[0080] At this time, the smallest optimization value can be used to represent that the error between the target vehicle and the preset trajectory is the smallest. Correspondingly, at this time, the input value of the target vehicle corresponding to the minimum value can be represented as the input value when the target vehicle is closest to the preset trajectory. Therefore, the input value of the target vehicle corresponding to the minimum value can be determined as the control output value.
[0081] Step S60: Control the target vehicle based on the control output value.
[0082] At this time, the calculated control output value can be applied to the target vehicle control system to enable the target vehicle to travel along a preset trajectory. As an example, the control output value can be sent to a vehicle driving module or the like.
[0083] In this embodiment, first, through the specified state value of the target vehicle and the transfer function, the continuous form trajectory of the target vehicle can be accurately obtained. Secondly, in this embodiment, by setting an optimization function, the input value (for example, the steering wheel rotation angle or the steering wheel angular velocity) when the target vehicle is closest to the preset trajectory can be obtained, and the calculation amount is small. At the same time, in this embodiment, by determining a plurality of sampling points in the preset trajectory, the accuracy of the input value when the target vehicle is closest to the preset trajectory can be further improved.
[0084] In one embodiment, please refer to Figure 5 , step S10 may include:
[0085] Step S11: Establish a trajectory coordinate system for the preset trajectory.
[0086] As an example, the trajectory coordinate system may be a world coordinate system.
[0087] Step S12: Establish a vehicle coordinate system for the target vehicle.
[0088] As an example, in the trajectory coordinate system, the initial position of the target vehicle can be determined. At this time, the target vehicle can be represented as a point on the preset trajectory. After that, a vehicle coordinate system can be established with this point as the origin.
[0089] Specifically, in a possible example, please refer to Figure 6 , step S12 may include:
[0090] Step S121: Use the center point of the rear axle of the target vehicle as the origin of the vehicle coordinate system.
[0091] The center point of the rear axle of the target vehicle can better reflect the overall movement trend of the target vehicle. Of course, other positions can also be selected as the origin of the vehicle coordinate system.
[0092] Step S122: Determine the first axis of the vehicle coordinate system.
[0093] As an example, at this time, the first axis of the vehicle coordinate system can be determined according to the direction from the center of the rear axle of the vehicle to the center of the front axle (the axis direction).
[0094] In addition, when there is a moving speed in the transfer function, the direction of the first axis of the vehicle coordinate system does not change as the vehicle moves forward or backward. When the vehicle moves forward, the moving speed in the transfer function can be positive. When the vehicle moves backward, the moving speed in the transfer function can be negative.
[0095] Step S123: Determine a second axis based on the origin and the first axis of the vehicle coordinate system, where the second axis is perpendicular to the first axis.
[0096] As an example, the second axis can be the Y-axis. Further, the positive direction of the second axis can be defined as the left side of the first axis, and the negative direction of the second axis can be defined as the right side of the first axis. At this time, the position of the target vehicle can be represented by unique (X, Y) coordinates.
[0097] Step S13: Determine the first sampling point as the intersection point of the preset trajectory and the vehicle coordinate system.
[0098] As an example, at this time, the intersection point of the preset trajectory and the second axis (Y-axis) of the vehicle coordinate system can be determined as the first sampling point, and the angle information of the trajectory at this point in the trajectory coordinate system can be obtained (The angle information can be expressed as the target orientation of the target vehicle).
[0099] Step S14: Determine multiple other sampling points in the preset trajectory based on the first sampling point and the sampling interval.
[0100] As an example, sampling can be continued at regular intervals along the driving direction of the preset trajectory until the number of all sampled points reaches the preset number N. At this time, each sampling point can be used as a preview point, so that a series of current and future changes of the target vehicle can be determined.
[0101] In a possible example, please refer to Figure 7 , step S14 may include:
[0102] Step S141: Determine the sampling interval based on the current speed value of the target vehicle and the sampling period.
[0103] As an example, the result of multiplying the current speed value v of the target vehicle by the sampling period T s can be used as the sampling interval ds, that is, ds = v * T s . Among them, T s can be a preset value.
[0104] Step S142: Determine multiple other sampling points in the preset trajectory based on the first sampling point and the sampling interval until the number of sampling points reaches the preset quantity.
[0105] As an example, 10 or 50 sampling points can be set.
[0106] In this embodiment, by separately setting the trajectory coordinate system of the preset trajectory and the vehicle coordinate system of the target vehicle, the respective sampling points of the target vehicle can be accurately determined, which is conducive to calculating the transfer function and the optimization function.
[0107] The following is an exemplary description of the vehicle control method provided in this application. Specifically, the sampling angle can be expressed as:
[0108]
[0109] And a sampling state can be established based on the sampling angle:
[0110]
[0111] In the sampling state, the superscript T represents taking the transpose, and this sampling state has N sub-states. The set value of N is the same as the set value of the number of samples in the previous sampler.
[0112] After that, a state including the current vehicle orientation θ (vehicle angle) can also be established:
[0113]
[0114] Among them, m is the number of specified state values of the target vehicle, θ is the value of the current vehicle orientation in the trajectory coordinate system, the superscript T represents taking the transpose, and other states (x1, x2... x m etc.) can include the lateral distance error. At this time, for the convenience of description, θ is fixed in the first row.
[0115] The transfer function can be expressed as:
[0116] X(k + 1) = A d X(k) + B d u(k)
[0117] The transfer function can represent that the state X(k) of the target vehicle (for example, the lateral distance error) is converted into a new state X(k + 1) after a time T s later, where u(k) represents the control input to the vehicle, and the actual control can be taken as the angle or angular velocity of the steering wheel. Specifically, at this time, when calculating the transfer function, the specified state value of the target vehicle and the sampling angle can be combined into an augmented state vector:
[0118]
[0119] At the same time, the augmented transfer function can be expressed as:
[0120]
[0121] Among them, E represents the transfer relationship between the original state and the sampling state. Given that θ has nothing to do with the sampling state Therefore, all the values in the row of the augmented state corresponding to the θ state and the corresponding E row value (i.e., the first row) are zero. The other rows of E will be determined according to the specific definition of the lateral distance error. Specifically, the linear MPC will use this augmented transfer function as the linear transfer function.
[0122] The optimization function can be determined as:
[0123]
[0124] The optimization function can be a quadratic programming function. R1 = C T QC C represents the observation matrix. It is stipulated that there must be a row whose value in the L-th column (L represents the row number where θ is located) is 1, the value in the (m + 2)-th column is -1 (m is the number of lateral distance errors), and the values in the remaining columns are all zero. This row represents the observation result of the angular error. Q is a diagonal positive definite matrix, representing the weights of the observed lateral distance errors. R2 is a real number greater than zero, representing the weight of u(k).
[0125] So far, any unconstrained quadratic programming linear MPC can be adopted, and the input can output u(0) to u(N - 1). At this time, when the optimization function has the minimum value min(J), the corresponding u(0) can be determined as the output of this controller. Among them, X(0) is the measured value of the original state at the current moment. As the state vector arranged in order according to the output result of the sampler.
[0126] Furthermore, this application provides a specific example to illustrate the vehicle control method in this application. For example, the specified state value can be taken as:
[0127]
[0128] The specified state value can be e d , and the specified state can represent the lateral distance error (for example, it can be defined that the lateral error is positive when the road is on the right side of the vehicle). As an example, at this time, N = 4 can be taken, that is, the sampling state is four points: The output u is taken as the tangent function of the front wheel steering angle. Taking the Ackerman motion model as the vehicle motion model and the transfer equation obtained by first-order discretization as an example for detailed description. At this time, there is:
[0129]
[0130] Among them, v xis the speed of the vehicle in the x - direction in the vehicle coordinate system, and I is the 2×2 identity matrix. As mentioned above, the direction of the first axis of the vehicle coordinate system does not change with the vehicle moving forward or backward. When the vehicle is moving forward, v x can be a positive number. When the vehicle is moving backward, v x can be a negative number.
[0131]
[0132] where L is the wheelbase of the vehicle's front and rear wheels.
[0133] Meanwhile, we can take u(k)=tan(δ fk ), where δ fk is the front - wheel steering angle of the vehicle at time k. In the augmented transfer function, there is:
[0134]
[0135] Meanwhile, we can also take the observation matrix:
[0136]
[0137] The observation matrix C can determine part of the physical meaning of the optimization function J. For example, one row of the observation matrix C can represent an observation. The first row represents the angle error observation (the vehicle's driving angle minus the sampling angle), and the second row can represent the lateral error observation.
[0138]
[0139] where q1 and q2 are both real numbers greater than zero. The optimization function can be expressed as:
[0140]
[0141] Using a linear MPC solver, input Solve using the above - mentioned augmented transfer function and J to obtain the output u(0). Take the arctangent function atan(u(0)), and then the required front - wheel steering angle δ f of the vehicle at this time can be obtained. Specifically, the front - wheel steering angle δ f can be mapped to the steering - wheel angle and then output. The present application does not limit the mapping method and will not elaborate in detail here.
[0142] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.
[0143] Based on the same inventive concept, an embodiment of the present application further provides a vehicle control device for implementing the vehicle control method involved above. The implementation solutions provided by this device for solving problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the vehicle control device provided below can refer to the limitations on the vehicle control method in the above text, and will not be elaborated here.
[0144] In an exemplary embodiment, as Figure 8 shown, a vehicle control device is provided, including: an acquisition module, a determination module, a first calculation module, a formation module, a second calculation module, and a control module, where:
[0145] The acquisition module is used to acquire the preset trajectory of the target vehicle and determine a plurality of sampling points in the preset trajectory.
[0146] The determination module is used to determine the sampling angle of the target vehicle at each sampling point and the specified state value of the target vehicle. The sampling angle includes the orientation angle value of the target vehicle relative to the sampling point, and the specified state value includes the predicted value of the target vehicle state.
[0147] The first calculation module is used to calculate the transfer function of the target vehicle based on the specified state value and the input value of the target vehicle. The transfer function is used to represent the state change relationship of the target vehicle.
[0148] The formation module is used to form the optimization function of the target vehicle. The optimization function is used to measure the driving error of the target vehicle.
[0149] The second calculation module is used to calculate the transfer function and the optimization function, and when the optimization function has a preset calculation result, determine the input value of the target vehicle as the control output value.
[0150] The control module is used to control the target vehicle based on the control output value.
[0151] In one embodiment, the second calculation module is configured to calculate an optimization function based on a plurality of specified state values and input values of the target vehicle, obtain a plurality of optimization values; determine the minimum value among the plurality of optimization values; and determine the input value of the target vehicle corresponding to the minimum value as the control output value.
[0152] In one embodiment, the forming module is configured to determine an optimization function of the target vehicle based on the error between the vehicle driving angle and the sampling angle of the target vehicle, the lateral distance error, and the input value of the target vehicle.
[0153] In one embodiment, the obtaining module is configured to establish a trajectory coordinate system of a preset trajectory; establish a vehicle coordinate system of the target vehicle; determine a first sampling point as the intersection of the preset trajectory and the vehicle coordinate system; and determine a plurality of other sampling points in the preset trajectory based on the first sampling point and the sampling interval.
[0154] In one embodiment, the obtaining module is configured to determine the sampling interval based on the current speed value and the sampling period of the target vehicle; and determine a plurality of other sampling points in the preset trajectory based on the first sampling point and the sampling interval until the number of sampling points reaches a preset number.
[0155] In one embodiment, the center point of the rear axle of the target vehicle is used as the origin of the vehicle coordinate system; the first axis of the vehicle coordinate system is determined; and based on the origin of the vehicle coordinate system and the first axis, a second axis is determined, and the second axis is perpendicular to the first axis.
[0156] In one embodiment, the input value of the target vehicle includes the rotation angle of the steering wheel of the target vehicle, or the angular velocity of the steering wheel.
[0157] Each module in the above vehicle control device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or stored in a memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
[0158] In one embodiment, a vehicle is provided, and the vehicle can execute the vehicle control method formed by any one embodiment and the combination of multiple embodiments in this application.
[0159] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0160] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.
[0161] In one embodiment, a computer program product is provided, including a computer program which, when executed by a processor, implements the steps in the above method embodiments.
[0162] Those of ordinary skill in the art can understand that all or part of the processes in the above method 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 above method embodiments. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.
[0163] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded in the present application.
[0164] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims
1. A vehicle control method, characterized in that: The vehicle control method comprises: Obtaining a preset trajectory of the target vehicle and determining a plurality of sampling points in the preset trajectory; Determine a sampling angle of the target vehicle at each sampling point and a specified state value of the target vehicle, wherein the sampling angle includes a direction angle value of the target vehicle relative to the sampling point, and the specified state value includes a predicted value of the state of the target vehicle; Based on the specified state value and the input value of the target vehicle, calculating a transfer function of the target vehicle, wherein the transfer function is used to represent a state change relationship of the target vehicle; forming an optimization function of the target vehicle, wherein the optimization function is used to measure the driving error of the target vehicle; calculating the transfer function and the optimization function, and determining the input value of the target vehicle as a control output value when the optimization function has a preset calculation result; The target vehicle is controlled based on the control output value.
2. The vehicle control method according to claim 1, characterized in that: The calculating the transfer function and the optimization function, and determining the input value of the target vehicle as the control output value when the optimization function has a preset calculation result, includes: Calculating the optimization function based on the plurality of specified state values and the input value of the target vehicle to obtain a plurality of optimization values; determining a minimum value among the plurality of optimized values; The input value of the target vehicle corresponding to the minimum value is determined as a control output value.
3. The vehicle control method according to claim 1, characterized in that: The optimization function for forming the target vehicle comprises: An optimization function of the target vehicle is determined based on an error between a vehicle travel angle of the target vehicle and a sampling angle, a lateral distance error, and an input value of the target vehicle.
4. The vehicle control method according to claim 1, characterized in that: The step of obtaining a preset trajectory of the target vehicle and determining a plurality of sampling points in the preset trajectory includes: Establishing a trajectory coordinate system of the preset trajectory; Establishing a vehicle coordinate system of the target vehicle; Determine the intersection of the preset trajectory and the vehicle coordinate system as a first sampling point; Based on the first sampling point and the sampling interval, a plurality of remaining sampling points are determined in the preset trajectory.
5. The vehicle control method according to claim 4, characterized in that: The determining of a plurality of remaining sampling points in the preset trajectory based on the first sampling point and the sampling interval includes: Determining the sampling interval based on the current speed value of the target vehicle and the sampling period; Based on the first sampling point and the sampling interval, a plurality of remaining sampling points are determined in the preset trajectory until the number of the sampling points reaches a preset number.
6. The vehicle control method according to claim 4, characterized in that: The establishing of the vehicle coordinate system of the target vehicle comprises: Taking the rear axle center point of the target vehicle as the origin of the vehicle coordinate system; determining a first axis of the vehicle coordinate system; A second axis is determined based on the origin of the vehicle coordinate system and the first axis, wherein the second axis is perpendicular to the first axis.
7. The vehicle control method according to claim 6, characterized in that: The step of determining the intersection point of the preset trajectory and the vehicle coordinate system as the first sampling point includes: An intersection point of the preset trajectory and the second axis of the vehicle coordinate system is determined as a first sampling point.
8. The vehicle control method according to claim 1, characterized in that: The input value of the target vehicle includes a rotation angle of a steering wheel of the target vehicle, or an angular velocity of the steering wheel.
9. A vehicle control device, characterized in that: The vehicle control device comprises: An acquisition module, used to acquire a preset trajectory of the target vehicle and determine a plurality of sampling points in the preset trajectory; A determination module, used to determine a sampling angle of the target vehicle at each sampling point and a specified state value of the target vehicle, wherein the sampling angle includes a direction angle value of the target vehicle relative to the sampling point, and the specified state value includes a predicted value of the state of the target vehicle; A first calculation module, used for calculating a transfer function of the target vehicle based on the specified state value and the input value of the target vehicle, wherein the transfer function is used for representing a state change relationship of the target vehicle; A forming module, used to form an optimization function of the target vehicle, wherein the optimization function is used to measure the driving error of the target vehicle; A second calculation module, configured to calculate the transfer function and the optimization function, and determine the input value of the target vehicle as a control output value when the optimization function has a preset calculation result; A control module is used to control the target vehicle based on the control output value.
10. A vehicle, characterized in that: The vehicle executes the vehicle control method according to any one of claims 1 to 8.