Lateral control method, device and equipment for reversing of vehicle, medium and product

Abstract

The invention discloses a lateral control method, device and equipment for vehicle reversing, a medium and a product, and relates to the technical field of automatic driving and deep learning. The method comprises the following steps: acquiring target vehicle information of a target vehicle, target trajectory information of a tracking target trajectory, and forward preview time of a simulation driver; the target vehicle information comprises a longitudinal driving speed, a course angle and a current position of the vehicle; the target track information comprises a track preview position point and a preset distance; determining a lateral distance deviation between the target vehicle and the tracking target trajectory according to the target vehicle information and the target trajectory information; determining a target lateral acceleration according to the lateral distance deviation and the forward preview time; and based on a tracking reversing control model, determining a target steering wheel turning angle according to the target lateral acceleration, and performing vehicle control based on the target steering wheel turning angle. According to the technical scheme, the precision and robustness of vehicle transverse control are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic driving and deep learning, and in particular to a lateral control method, device, equipment, medium and product for vehicle reversing. BACKGROUND

[0002] In recent years, the number of vehicles is increasing, and with the development of vehicle intelligence and networking, the auxiliary driving technology has entered a rapid development stage. In the daily driving process of drivers, there are many narrow road conditions, and reversing in narrow road conditions increases the difficulty of driver operation.

[0003] In related technologies, the loop tracking reversing (also known as returning along the original path) function can be implemented in a narrow space. When the driver drives on a narrow road and needs to turn around and return, the vehicle can be reversed along the original path to return. However, the forward-looking technical research on the loop tracking reversing function is still in its infancy, and the efficiency of realizing the memory driving track and following the track is low, and the cost is high, which needs to be improved. SUMMARY

[0004] The present application provides a lateral control method, device, equipment, medium and product for vehicle reversing, to improve the accuracy and robustness of vehicle lateral control, and thus improve the flexibility of reversing.

[0005] According to an aspect of the present application, a lateral control method for vehicle reversing is provided, comprising:

[0006] obtaining target vehicle information of a target vehicle, target trajectory information of a loop tracking target trajectory, and a forward preview time of a simulated driver; the target vehicle information includes a longitudinal driving speed, a heading angle and a current position of the vehicle; the target trajectory information includes a trajectory preview position point and a preset distance;

[0007] determining a lateral distance deviation between the target vehicle and the loop tracking target trajectory according to the target vehicle information and the target trajectory information;

[0008] determining a target lateral acceleration according to the lateral distance deviation and the forward preview time;

[0009] determining a target steering wheel angle according to the target lateral acceleration based on a loop tracking reversing control model, and performing vehicle control based on the target steering wheel angle.

[0010] According to another aspect of the present application, a lateral control device for vehicle reversing is provided, comprising:

[0011] an information obtaining module, configured to obtain target vehicle information of a target vehicle, target trajectory information of a target trajectory, and a forward preview time of a simulated driver, wherein the target vehicle information comprises a longitudinal driving speed, a heading angle, and a current position of the vehicle, and the target trajectory information comprises a trajectory preview position point and a preset distance;

[0012] a lateral distance deviation determining module, configured to determine a lateral distance deviation between the target vehicle and the target trajectory according to the target vehicle information and the target trajectory information;

[0013] a lateral acceleration determining module, configured to determine a target lateral acceleration according to the lateral distance deviation and the forward preview time;

[0014] a vehicle control module, configured to determine a target steering wheel rotation angle according to the target lateral acceleration based on a target trajectory reversing control model, and perform vehicle control based on the target steering wheel rotation angle.

[0015] According to another aspect of the present application, an electronic device is provided, which comprises:

[0016] at least one processor; and

[0017] a memory connected to the at least one processor in communication; wherein,

[0018] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the lateral control method for vehicle reversing according to any one of the embodiments of the present application.

[0019] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to perform the lateral control method for vehicle reversing according to any one of the embodiments of the present application when executed by the processor.

[0020] According to another aspect of the present application, a computer program product is provided, which comprises a computer program for enabling a processor to perform the lateral control method for vehicle reversing according to any one of the embodiments of the present application when executed by the processor.

[0021] The technical scheme of the embodiment of the present application comprises the following steps: obtaining target vehicle information of a target vehicle, target trajectory information of a target trajectory, and forward preview time of a driver; the target vehicle information comprises longitudinal driving speed, a heading angle and a current position of the vehicle; the target trajectory information comprises a trajectory preview position point and a preset distance; determining a lateral distance deviation between the target vehicle and the target trajectory according to the target vehicle information and the target trajectory information; determining a target lateral acceleration according to the lateral distance deviation and the forward preview time; determining a target steering wheel turning angle according to the target lateral acceleration based on a target trajectory reversing control model, and performing vehicle control based on the target steering wheel turning angle. The above technical scheme does not need to establish an accurate vehicle dynamics model, and effectively controls the complex system of vehicle steering by simulating the thinking process of human driving preview, improves the precision and robustness of vehicle lateral control, and further improves the flexibility of the target trajectory reversing system of the intelligent vehicle and the driving safety of the driver.

[0022] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0024] Figure 1 is a flow chart of a vehicle reversing lateral control method according to an embodiment of the present application;

[0025] Figure 2 is a flow chart of a vehicle reversing lateral control method according to an embodiment of the present application;

[0026] Figure 3 is a schematic diagram of a framework for lateral control based on a single neuron network control algorithm according to an embodiment of the present application;

[0027] Figure 4 is a schematic diagram of a vehicle reversing lateral control device according to an embodiment of the present application;

[0028] Figure 5 is a schematic diagram of an electronic device for implementing a vehicle reversing lateral control method according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort should fall into the protection scope of the present application.

[0030] It should be noted that the terms "first", "second" and the like in the description and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in other than the order illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a list of steps or units need not be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to such processes, methods, products or devices.

[0031] In addition, it should be noted that in the technical solutions of the present application, the collection, storage, use, processing, transmission, provision and disclosure of related data such as vehicle information and trajectory information comply with relevant laws and regulations and do not violate public order and good customs.

[0032] Figure 1 A flowchart of a lateral control method for vehicle reversing is provided according to an embodiment of the present application. The embodiment can be applied to the case of how to reverse in a narrow road condition. The method can be executed by a lateral control device for vehicle reversing, which can be realized in the form of hardware and / or software. The device can be configured in an electronic device that carries the lateral control function for vehicle reversing, such as an automatic driving controller. As shown in the figure, the method comprises the following steps. Figure 1

[0033] S110, obtaining target vehicle information of a target vehicle, target trajectory information of a target trajectory for tracking, and a forward preview time of a simulated driver.

[0034] In the embodiment, the target vehicle refers to a vehicle that needs to be controlled for reversing. The target vehicle information includes longitudinal driving speed, heading angle and current position of the vehicle. The target trajectory for tracking refers to a desired reversing driving trajectory. The target trajectory information includes a trajectory preview position point and a preset distance. The trajectory preview position point refers to a preview point on the target trajectory for tracking.

[0035] ​The target vehicle information of the target vehicle is acquired, including: sensing the surrounding environment of the target vehicle by the surround-view camera to obtain a first distance between the target vehicle and an obstacle; and determining the target vehicle information of the target vehicle according to the first distance.

[0036] Specifically, when the gear position of the vehicle is D, the vehicle speed is lower than a first threshold value, and the tracking reversing system switch is set to "ON", the system function self-checking is started. As a kind of priority selection, the first threshold value can be 30km / h. After self-checking without fault, the surrounding environment is sensed by the surround-view camera, the distance information between the vehicle and the obstacle is sent to the tracking reversing controller, the controller carries out data fusion to obtain the target vehicle information, for example, including longitudinal driving speed, heading angle and current position of the vehicle.

[0037] Optionally, the target trajectory information of the tracking target trajectory is acquired, including: acquiring a preset forward trajectory of the target vehicle; and performing reverse processing on the preset forward trajectory to obtain the target trajectory information.

[0038] Specifically, when the vehicle travels to a calibration value (for example, the calibration value is 50m), the forward trajectory of the last 50m of the vehicle is automatically recorded, when the driver needs to return, the system prompts the driver to switch the gear position to R in the stopped state of the vehicle, or the driver manually switches the gear position to R to activate the tracking reversing function. The preset forward trajectory, such as the driving trajectory array, is reversely processed to obtain the tracking target trajectory.

[0039] S120, according to the target vehicle information and the target trajectory information, determining the lateral distance deviation between the target vehicle and the tracking target trajectory.

[0040] Optionally, according to the longitudinal driving speed, the forward preview time and the preset distance, the forward preview distance of the simulated driver driving is determined; and according to the forward preview distance, the trajectory preview position point, the heading angle and the current position of the vehicle, the lateral distance deviation between the target vehicle and the tracking target trajectory is determined. Specifically, the lateral distance deviation e can be determined by the following formula y :

[0041]

[0042] l d =v x *T pre +l0;

[0043] Wherein, v x is the longitudinal driving speed of the vehicle, T pre is the forward preview time of the simulated driver, l0 is the preset distance, is the heading angle of the vehicle, l d is the forward preview distance of the simulated driver driving, (E x , Ey ) is the coordinate of the current position of the vehicle, (P x , P y ) is the coordinate of the trajectory preview position point.

[0044] S130, determining a target lateral acceleration according to the lateral distance deviation and the forward preview time.

[0045] In this embodiment, the target lateral acceleration refers to the expected lateral acceleration.

[0046] Specifically, the target lateral acceleration can be determined based on the following formula

[0047] S140, determining a target steering wheel angle according to the target lateral acceleration based on the tracking reverse control model, and performing vehicle control based on the target steering wheel angle.

[0048] In this embodiment, the tracking reverse control model refers to a single neuron model trained in advance for determining the steering wheel angle. The target steering wheel angle refers to the expected steering wheel angle.

[0049] Specifically, the target lateral acceleration is input into the tracking reverse control model, and the target steering wheel angle is obtained through model operation. The target steering wheel angle is output to the vehicle nonlinear system dynamics, i.e., to the electric power steering system controller. The vehicle generates a lateral acceleration and related motion state quantity changes, which are then fed back into the lateral acceleration previewer and the tracking reverse system controller, so that the vehicle is controlled to follow the expected reverse trajectory in each cycle.

[0050] The technical scheme of the embodiment of the application comprises the following steps: obtaining target vehicle information of a target vehicle, target trajectory information of a tracking target trajectory, and forward preview time of a simulated driver; the target vehicle information comprises a longitudinal driving speed, a heading angle, and a current position of the vehicle; the target trajectory information comprises a trajectory preview position point and a preset distance; determining a lateral distance deviation between the target vehicle and the tracking target trajectory according to the target vehicle information and the target trajectory information; determining a target lateral acceleration according to the lateral distance deviation and the forward preview time; determining a target steering wheel angle according to the target lateral acceleration based on a tracking reverse control model, and performing vehicle control based on the target steering wheel angle. The above technical scheme does not need to establish an accurate vehicle dynamics model, but simulates the thinking process of human driving preview, effectively controls the complex system of vehicle steering, improves the precision and robustness of vehicle lateral control, and further improves the flexibility of the tracking reverse system of the intelligent vehicle and the driving safety of the driver.

[0051] Figure 2It is a flow chart of a lateral control method for vehicle reversing according to an embodiment of the present application, and the embodiment is based on the above-mentioned embodiment and describes the specific training process of the tracking reversing control model. Figure 2 As shown in the figure, the method comprises:

[0052] In S210, target vehicle information of a target vehicle, target trajectory information of a tracking target trajectory, and a forward preview time of a simulated driver are acquired.

[0053] The target vehicle information comprises a longitudinal driving speed, a heading angle and a current position of the vehicle, and the target trajectory information comprises a trajectory preview position point and a preset distance.

[0054] In S220, a lateral distance deviation between the target vehicle and the tracking target trajectory is determined according to the target vehicle information and the target trajectory information.

[0055] In S230, a target lateral acceleration is determined according to the lateral distance deviation and the forward preview time.

[0056] In S240, a target steering wheel turning angle is determined according to the target lateral acceleration based on a tracking reversing control model, and vehicle control is performed based on the target steering wheel turning angle.

[0057] Optionally, the tracking reversing control model is trained by determining a tracking sample trajectory of a sample vehicle and sample vehicle information, wherein the sample vehicle information comprises steering wheel turning angles at different time points and sample actual lateral accelerations; determining a performance index function according to the tracking sample trajectory, the sample vehicle information and a weighting coefficient; and performing gradient descent optimization training on a single neuron network based on the performance index function to obtain the tracking reversing control model, wherein the single neuron comprises an input layer, a hidden layer and an output layer.

[0058] It can be understood that the lane keeping system controller designed by using the single neuron network realizes adaptive and self-organizing functions by adjusting the weighting coefficient. The structure is simple, and the controller can adapt to environmental changes and has strong robustness. When the controller is applied to the lateral control of vehicle tracking reversing, the nonlinear control of the vehicle on the trajectory following is well realized. In addition, the intelligent vehicle tracking reversing controller designed by using the single neuron network method has simple and clear logic, does not require a controller chip with higher computing power, and has low cost and is easy to mass produce and use in real vehicles.

[0059] According to the tracked sample trajectory, the sample vehicle information and the weighting coefficient, the performance index function is determined, including: determining the sample expected lateral acceleration according to the tracked sample trajectory and the sample vehicle information; determining the first performance index according to the sample expected lateral acceleration and the sample actual lateral acceleration; determining the second performance index according to the steering wheel rotation angles at different time points; and determining the performance index function according to the first performance index, the second performance index and the weighting coefficient.

[0060] The sample vehicle refers to a vehicle used for model training. The sample expected lateral acceleration refers to the expected lateral acceleration of the sample vehicle. The sample actual lateral acceleration refers to the actual lateral acceleration of the sample vehicle.

[0061] Specifically, the sample expected lateral acceleration can be determined according to the tracked sample trajectory and the sample vehicle information based on the above method of determining the target lateral acceleration, and then the first performance index J1 can be determined according to the sample expected lateral acceleration and the sample actual lateral acceleration based on the following formula: The second performance index J2 can be determined according to the steering wheel rotation angles at different time points based on the following formula:

[0062]

[0063] The performance index function J can be determined according to the first performance index, the second performance index and the weighting coefficient based on the following formula:

[0064] wherein, and is the expected lateral acceleration input and the actual lateral acceleration output of the vehicle at time k, δ sw (k) and δ sw (k-1) are the steering wheel rotation angles at time k and k-1, and P and Q are the weighting coefficients of the trajectory following error and the steering wheel change rate.

[0065] Then, the single neuron network is optimized and trained based on the performance index function to obtain the tracked reversing control model, specifically: according to the performance index function of the neural network controller of the vehicle tracked reversing system control, the gradient descent method is used to optimize and design the established index function to make it tend to be minimum, that is, to make the connection weight change tend to be 0; so as to realize the correction of the connection weight of the neuron. The gradient descent method is an iterative optimization algorithm for finding the minimum value of a certain performance index function, and the basic idea is to correct the connection weight w i (k) in the decreasing direction, that is:

[0066]

[0067] The specific control rule and learning algorithm of the single neuron adaptive controller are as follows:

[0068]

[0069] The actual steering wheel turning angle of the vehicle tracking reversing system controller, i.e. the tracking reversing control model calculation output, is obtained as follows:

[0070] δ sw (k) = δ sw (k-1) + K(w P (k) x P (k) + w I (k) x I (k) + w D (k) x D (k) ) ;

[0071] wherein w is the weight value of neuron input, η P , η I , η D are the learning rates of proportion, integral and differential respectively, K is the neuron proportion coefficient, and b0 is the output response value of the control system at the initial state. The greater K is, the better the rapidity is, but the overshoot is large, and even the system can be unstable. When the time delay of the controlled object is increased, the value of K must be reduced to ensure the stability of the system. If the value of K is too small, the rapidity of the system will be poor.

[0072] It can be understood that the gradient descent algorithm is adopted to optimize the established driving evaluation index function, the design of the controller of the tracking reversing system lateral control based on the optimal combination of the proportion-integral-differential three kinds of control effects of the neural network is realized, and thus the adaptive control is established without identifying the complex vehicle dynamics nonlinear system, the precision and stability of the trajectory tracking control of the vehicle during tracking reversing are greatly improved.

[0073] As shown in Figure 3 , the single neuron network control algorithm is used to simulate the driving operation of the real driver, and the flow chart is shown in Figure 3 .

[0074] The technical scheme of the embodiment of the present application obtains target vehicle information of a target vehicle, target trajectory information of a target trajectory, and a forward preview time of a simulated driver, the target vehicle information includes a longitudinal driving speed, a heading angle and a current position of the vehicle, the target trajectory information includes a trajectory preview position point and a preset distance, a lateral distance deviation between the target vehicle and the target trajectory is determined according to the target vehicle information and the target trajectory information, a target lateral acceleration is determined according to the lateral distance deviation and the forward preview time, a target steering wheel angle is determined according to the target lateral acceleration based on a target trajectory reversing control model, and vehicle control is performed based on the target steering wheel angle. The above technical scheme does not need to establish an accurate vehicle dynamics model, effectively controls the complex system of vehicle steering by simulating the thinking process of human driving preview, improves the accuracy and robustness of vehicle lateral control, and further improves the flexibility of the target trajectory reversing system of the intelligent vehicle and the driving safety of the driver.

[0075] The control logic described in the present application is clear and reliable, solves the problem that in the related art, the forward-looking technical research on the target trajectory reversing function is still in the initial stage, and the efficiency of the memory driving trajectory and the trajectory following is low, and the cost is high.

[0076] Secondly, the present application solves the problem that the vehicle tire and the steering system have highly nonlinear and parameter time-varying characteristics, the tire mechanical properties of the vehicle during driving show serious non-steady-state nonlinear characteristics, the traditional adaptive control has difficulty in identifying the parameters of the controlled object, the controller parameter online real-time identification and setting are poor, the real-time performance of the controller cannot be met, and the engineering practicability is poor.

[0077] Furthermore, the present application adopts a but neuron network algorithm to design the target trajectory reversing controller, and adopts a gradient descent rule to optimize the design of the vehicle steering stability index function, realizes adaptive control of the vehicle motion direction, avoids the problem that the traditional adaptive control must identify the complex vehicle direction dynamics nonlinear system as the controlled object, improves the robustness and adaptability of the target trajectory reversing control system, has good trajectory following performance, and is very similar to the actual driver's steering behavior.

[0078] Finally, the control process described in the present application can be independently integrated in an automatic driving controller, is easy to be jointly developed with a vehicle controller, can be more beneficial to the deployment and application on mass-produced vehicles, and has wider practical value.

[0079] Figure 4It is a structural schematic diagram of a vehicle reversing lateral control device provided by an embodiment of the present application. The embodiment can be applied to the case of how to reverse in a narrow road condition. The vehicle reversing lateral control device can be implemented in the form of hardware and / or software. The device can be configured in an electronic device that bears the function of controlling the lateral movement of a vehicle, such as an automatic driving controller. As shown in the figure, the device includes: Figure 4

[0080] an information acquisition module 410 configured to acquire target vehicle information of a target vehicle, target trajectory information of a target trajectory, and a forward preview time of a simulated driver; the target vehicle information includes a longitudinal driving speed, a heading angle, and a current position of the vehicle; the target trajectory information includes a trajectory preview position point and a preset distance;

[0081] a lateral distance deviation determination module 420 configured to determine a lateral distance deviation between the target vehicle and the target trajectory according to the target vehicle information and the target trajectory information;

[0082] a lateral acceleration determination module 430 configured to determine a target lateral acceleration according to the lateral distance deviation and the forward preview time;

[0083] a vehicle control module 440 configured to determine a target steering wheel rotation angle according to the target lateral acceleration based on a target trajectory reversing control model, and perform vehicle control based on the target steering wheel rotation angle.

[0084] The technical scheme of the embodiment of the present application includes the following steps: acquiring target vehicle information of a target vehicle, target trajectory information of a target trajectory, and a forward preview time of a simulated driver; the target vehicle information includes a longitudinal driving speed, a heading angle, and a current position of the vehicle; the target trajectory information includes a trajectory preview position point and a preset distance; determining a lateral distance deviation between the target vehicle and the target trajectory according to the target vehicle information and the target trajectory information; determining a target lateral acceleration according to the lateral distance deviation and the forward preview time; and determining a target steering wheel rotation angle according to the target lateral acceleration based on a target trajectory reversing control model, and performing vehicle control based on the target steering wheel rotation angle. The above technical scheme does not need to establish an accurate vehicle dynamics model. By simulating the thinking process of human driving preview, the vehicle steering system, which is a complex system, is effectively controlled, the accuracy and robustness of vehicle lateral control are improved, and the flexibility of the target trajectory reversing system of an intelligent vehicle and the driving safety of a driver are improved.

[0085] Optionally, the device includes a model training module configured to:

[0086] determine a target trajectory of a sample vehicle and sample vehicle information; the sample vehicle information includes steering wheel rotation angles at different time points and sample actual lateral accelerations;

[0087] ​According to the tracking sample trajectory, the sample vehicle information and the weighting coefficient, a performance index function is determined.

[0088] According to the performance index function, a single neuron network is trained by gradient descent optimization to obtain a tracking reverse control model; wherein the single neuron includes an input layer, a hidden layer and an output layer.

[0089] Optionally, the model training module is specifically used for:

[0090] According to the tracking sample trajectory and the sample vehicle information, a sample expected lateral acceleration is determined.

[0091] According to the sample expected lateral acceleration and the sample actual lateral acceleration, a first performance index is determined.

[0092] According to the steering wheel angle at different time, a second performance index is determined.

[0093] According to the first performance index, the second performance index and the weighting coefficient, a performance index function is determined.

[0094] Optionally, the information acquisition module 410 is used for:

[0095] The preset forward trajectory of the target vehicle is acquired.

[0096] The preset forward trajectory is reversely processed to obtain target trajectory information.

[0097] Optionally, the lateral distance deviation determination module 420 is used for:

[0098] According to the longitudinal driving speed, the forward preview time and the preset distance, a forward preview distance driven by a simulated driver is determined.

[0099] According to the forward preview distance, the trajectory preview position point, the heading angle and the current position of the vehicle, a lateral distance deviation between the target vehicle and the tracking target trajectory is determined.

[0100] Optionally, the information acquisition module 410 is used for:

[0101] The surrounding environment of the target vehicle is perceived by the surround-view camera to obtain a first distance between the target vehicle and an obstacle.

[0102] According to the first distance, target vehicle information of the target vehicle is determined.

[0103] The lateral control device for vehicle reverse provided in the embodiments of the present application can execute the lateral control method for vehicle reverse provided in any embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0104] According to an embodiment of the present application, the present application also provides an electronic device, a readable storage medium and a computer program product.

[0105] Figure 5 is a structural schematic diagram of an electronic device for realizing a lateral control method of vehicle reversing according to an embodiment of the present application. Figure 5 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.

[0106] As shown in Figure 5 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., connected to the at least one processor 11 in communication, wherein the memory stores a computer program executable by the at least one processor 11, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0107] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunications networks.

[0108] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the lateral control method for vehicle reversing.

[0109] In some embodiments, the lateral control method for vehicle reversing can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the lateral control method for vehicle reversing described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the lateral control method for vehicle reversing by any other suitable means, such as by means of firmware.

[0110] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0111] Computer programs used to implement the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor of the machine, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0112] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0113] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0114] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0115] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0116] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in the present application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, which are not limited herein.

[0117] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method of side control of a vehicle backing up, characterized by, The method comprises: obtaining target vehicle information of a target vehicle, target trajectory information of a target trajectory, and a forward preview time of a simulated driver; the target vehicle information comprises a longitudinal driving speed, a heading angle, and a current position of the vehicle; the target trajectory information comprises a trajectory preview position point and a preset distance; determining a lateral distance deviation between the target vehicle and the target trajectory according to the target vehicle information and the target trajectory information; determining a target lateral acceleration according to the lateral distance deviation and the forward preview time; determining a target steering wheel angle according to the target lateral acceleration based on a target trajectory reversing control model, and performing vehicle control based on the target steering wheel angle.

2. The method of claim 1, wherein, The target trajectory reversing control model is trained in the following manner: determining a target trajectory and sample vehicle information of a sample vehicle; the sample vehicle information comprises steering wheel angles at different time points and a sample actual lateral acceleration; determining a performance index function according to the target trajectory, the sample vehicle information, and a weighting coefficient; performing gradient descent optimization training on a single neuron network based on the performance index function to obtain the target trajectory reversing control model; the single neuron comprises an input layer, a hidden layer, and an output layer.

3. The method of claim 2, wherein, determining a performance index function according to the target trajectory, the sample vehicle information, and a weighting coefficient, comprises: determining a sample expected lateral acceleration according to the target trajectory and the sample vehicle information; determining a first performance index according to the sample expected lateral acceleration and the sample actual lateral acceleration; determining a second performance index according to the steering wheel angles at different time points; determining a performance index function according to the first performance index, the second performance index, and a weighting coefficient.

4. The method of claim 1, wherein, obtaining target trajectory information of a target trajectory, comprises: obtaining a preset forward trajectory of the target vehicle; performing reverse processing on the preset forward trajectory to obtain the target trajectory information.

5. The method of claim 1, wherein, determining a lateral distance deviation between the target vehicle and the target trajectory according to the target vehicle information and the target trajectory information, comprises: determining a forward preview distance of a simulated driver according to the longitudinal driving speed, the forward preview time, and the preset distance; determining a lateral distance deviation between the target vehicle and the target trajectory according to the forward preview distance, the trajectory preview position point, the heading angle, and the current position of the vehicle.

6. The method of claim 1, wherein, obtaining target vehicle information of a target vehicle, comprises: perceiving a surrounding environment of the target vehicle through a surround-view camera to obtain a first distance between the target vehicle and an obstacle; determining target vehicle information of the target vehicle according to the first distance.

7. A lateral control device for a vehicle backing up, characterized by The method comprises: an information acquisition module is configured to obtain target vehicle information of a target vehicle, target trajectory information of a target trajectory, and a forward preview time of a simulated driver; the target vehicle information comprises a longitudinal driving speed, a heading angle, and a current position of the vehicle; the target trajectory information comprises a trajectory preview position point and a preset distance; a lateral distance deviation determination module configured to determine a lateral distance deviation between the target vehicle and the target trajectory according to the target vehicle information and the target trajectory information; a lateral acceleration determination module configured to determine a target lateral acceleration according to the lateral distance deviation and the forward preview time; a vehicle control module configured to determine a target steering wheel angle according to the target lateral acceleration based on a trajectory following reverse control model, and perform vehicle control based on the target steering wheel angle.

8. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the lateral control method for vehicle reverse driving according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the lateral control method for vehicle reverse driving according to any one of claims 1-6 when executed by the processor.

10. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program implements the lateral control method for vehicle reverse driving according to any one of claims 1-6 when executed by the processor.

Images