Vehicle control method and device

Abstract

A vehicle control method is provided for controlling the steering wheel angle of the vehicle in real time during automatic parking of the vehicle, the method comprising: obtaining a self-correcting lateral dynamics model of the vehicle and a motion state quantity of the vehicle at a current moment, wherein the self-correcting lateral dynamics model establishes a mapping between the steering wheel angle of the vehicle and the turning curvature of the vehicle, wherein the turning curvature is a function of a self-correcting coefficient and the steering wheel angle; determining an actual turning curvature of the vehicle based on the motion state quantity; determining a theoretical turning curvature of the vehicle based on the self-correcting lateral dynamics model and the motion state quantity; determining a value of the self-correcting coefficient at a current moment based on the actual turning curvature and the theoretical turning curvature; and controlling the steering wheel angle of the vehicle based on the value of the self-correcting coefficient at a current moment and the self-correcting lateral dynamics model.

Description

Technical Field

[0001] The present disclosure relates to the field of vehicles, and in particular to a vehicle control method, a vehicle control device, a computer device, a vehicle including the above-mentioned vehicle control device or computer device, a storage medium, and a computer program product. Background Art

[0002] In recent years, with the rapid development of autonomous driving technology, parking function is becoming an important part of autonomous driving products. Motion control is one of the key modules of the parking function software system, which is responsible for manipulating the vehicle to drive according to the planned trajectory. The accuracy of motion control has a great impact on the function, performance and safety of autonomous driving. Therefore, further improving the accuracy of motion control is one of the important tasks to improve autonomous driving products.

[0003] The methods described in this section are not necessarily methods that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any method described in this section is considered to be prior art simply because it is included in this section. Similarly, unless otherwise indicated, the issues mentioned in this section should not be considered to have been recognized in any prior art. Summary of the invention

[0004] Embodiments of the present disclosure provide a vehicle control method, a vehicle control device, a computer device, a vehicle including the above-mentioned vehicle control device or computer device, a storage medium, and a computer program product.

[0005] According to one aspect of the present disclosure, a vehicle control method is provided for controlling the steering wheel angle of a vehicle in real time during automatic parking of the vehicle, the method comprising: obtaining a self-correcting lateral dynamics model of the vehicle and a motion state quantity of the vehicle at a current moment, wherein the self-correcting lateral dynamics model establishes a mapping between the steering wheel angle of the vehicle and the turning curvature of the vehicle, wherein the turning curvature is a function of a self-correcting coefficient and a steering wheel angle; determining an actual turning curvature of the vehicle based on the motion state quantity; determining a theoretical turning curvature of the vehicle based on the self-correcting lateral dynamics model and the motion state quantity; determining a value of the self-correcting coefficient at a current moment based on the actual turning curvature and the theoretical turning curvature; and controlling the steering wheel angle of the vehicle based on the value of the self-correcting coefficient at the current moment and the self-correcting lateral dynamics model.

[0006] According to another aspect of the present disclosure, a vehicle control device is provided for controlling the steering wheel angle of a vehicle in real time during automatic parking of the vehicle, the device comprising: a first module for acquiring a self-correcting lateral dynamics model of the vehicle and a motion state quantity of the vehicle at a current moment, wherein the self-correcting lateral dynamics model establishes a mapping between the steering wheel angle of the vehicle and the turning curvature of the vehicle, wherein the turning curvature is a function of the self-correcting coefficient and the steering wheel angle; a second module for determining an actual turning curvature of the vehicle based on the motion state quantity; a third module for determining a theoretical turning curvature of the vehicle based on the self-correcting lateral dynamics model and the motion state quantity; a fourth module for determining a value of the self-correcting coefficient at a current moment based on the actual turning curvature and the theoretical turning curvature; and a fifth module for controlling the steering wheel angle of the vehicle based on the value of the self-correcting coefficient at a current moment and the self-correcting lateral dynamics model.

[0007] According to another aspect of the present disclosure, a computer device is provided, which includes: at least one processor; and at least one memory on which a computer program is stored, and when the computer program is executed by the at least one processor, the at least one processor implements the above method.

[0008] According to yet another aspect of the present disclosure, a vehicle is provided. The vehicle includes the above-mentioned vehicle control device or computer equipment.

[0009] According to yet another aspect of the present disclosure, a non-transitory computer-readable storage medium storing a computer program is provided. The computer program includes instructions, which, when executed by a processor, cause the processor to execute the above method.

[0010] According to yet another aspect of the present disclosure, a computer program product is provided. The computer program product includes instructions. When the instructions are executed by a processor, the processor is caused to execute the above method.

[0011] According to the embodiments of the present disclosure, the vehicle dynamics model can be calibrated online, reducing a large amount of tedious and detailed calibration work, and improving the adaptability and robustness of vehicle motion control.

[0012] These and other aspects of the disclosure will be apparent from and elucidated with reference to the embodiments described hereinafter. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In the following description of exemplary embodiments in conjunction with the accompanying drawings, more details, features and advantages of the present disclosure are disclosed. The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements. In the drawings:

[0014] Figure 1 is a schematic diagram illustrating an example system in which the various methods described herein may be implemented according to an exemplary embodiment;

[0015] Figure 2 is a flow chart illustrating a vehicle control method according to an exemplary embodiment;

[0016] Figure 3 is a flow chart illustrating a method of determining a theoretical turning curvature of a vehicle according to an exemplary embodiment;

[0017] Figure 4A and 4B are schematic block diagrams illustrating a conventional vehicle lateral dynamics model and an inverse model thereof and a vehicle self-correction lateral dynamics model and an inverse model thereof according to an exemplary embodiment, respectively;

[0018] Figure 5 is a block diagram illustrating a vehicle control device according to an exemplary embodiment; and

[0019] Figure 6 is a block diagram illustrating an exemplary computer device that can be applied to the exemplary embodiments. DETAILED DESCRIPTION

[0020] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0021] The terms used in the description of various examples described in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. As used herein, the term "plurality" means two or more, and the term "based on" should be interpreted as "based at least in part on". In addition, the terms "and / or" and "at least one of..." cover any one of the listed items and all possible combinations.

[0022] Motion control is one of the key modules of the parking function software system, which is responsible for manipulating the vehicle to drive along the planned trajectory. The accuracy of motion control has a great impact on the function, performance and safety of autonomous driving. Existing control schemes are generally composed of feedforward plus feedback. Specifically, first, the vehicle's inverse dynamic model is used to calculate the feedforward of a steering wheel angle in an open-loop manner according to the curvature of the trajectory; then, according to the position angle error between the vehicle and the trajectory, PID (Proportional Integral Derivative) control or other algorithms are used to calculate the feedback of a steering wheel angle; finally, the two are summed to obtain the control amount of the steering wheel. The role of feedforward is to make the vehicle turn, and the role of feedback is to correct the error and improve the anti-interference performance.

[0023] In parking scenarios, the turning radius of the planned trajectory is often large, and the vehicle needs to be controlled to turn along the maximum turning radius. At this time, the control amount of the steering wheel is mainly the feedforward amount. If the vehicle dynamics model is inaccurate, the vehicle control accuracy will decrease. Although feedback can eliminate errors within a certain range, the effect of feedback cannot be infinitely amplified, otherwise it will cause oscillation. Therefore, a lot of calibration work is done in engineering to calibrate the vehicle's dynamics model to improve the accuracy of feedforward control.

[0024] In order to solve the above technical problems, according to one or more embodiments of the present disclosure, a new vehicle control method is proposed. The method determines the current value of the self-correction coefficient based on the actual turning curvature and theoretical turning curvature of the vehicle, and controls the steering wheel angle of the vehicle based on the current value of the self-correction coefficient and the self-correction lateral dynamics model, so as to correct the vehicle's lateral dynamics model during the parking process, so that the model is consistent with the actual situation, improve the accuracy of the steering wheel feedforward calculation of the vehicle's lateral control, and thus improve the lateral control accuracy of the vehicle. Through the above method, the vehicle's lateral dynamics model can be calibrated online, reducing the dependence of the precise control involved in the vehicle parking process on manual calibration, and improving the adaptability and robustness of the vehicle motion control algorithm. The exemplary embodiments of the present disclosure are described in detail below in conjunction with the accompanying drawings.

[0025] Figure 1 is a schematic diagram illustrating an example system 100 in which the various methods described herein may be implemented, according to an example embodiment.

[0026] refer to Figure 1 The system 100 includes an in-vehicle system 110, a server 120, and a network 130 that communicatively couples the in-vehicle system 110 and the server 120.

[0027] The vehicle-mounted system 110 includes a display 114 and an application (APP) 112 that can be displayed via the display 114. The application 112 may be an application installed by default in the vehicle-mounted system 110 or downloaded and installed by the user 102, or a small program as a lightweight application. In the case where the application 112 is a small program, the user 102 can directly run the application 112 on the vehicle-mounted system 110 by searching for the application 112 in the host application (for example, by the name of the application 112, etc.) or scanning the graphic code of the application 112 (for example, a barcode, a QR code, etc.), without installing the application 112. In some embodiments, the vehicle-mounted system 110 may include one or more processors and one or more memories (not shown), and the vehicle-mounted system 110 is implemented as a vehicle-mounted computer. In some embodiments, the vehicle-mounted system 110 may include more or fewer display screens 114 (for example, excluding the display screen 114), and / or one or more speakers or other human-computer interaction devices. In some embodiments, the vehicle-mounted system 110 may not communicate with the server 120.

[0028] Server 120 may represent a single server, a cluster of multiple servers, a distributed system, or a cloud server that provides basic cloud services (such as cloud database, cloud computing, cloud storage, cloud communication). It will be understood that although Figure 1 , the server 120 is shown to communicate with only one in-vehicle system 110 , but the server 120 can provide background services for multiple in-vehicle systems at the same time.

[0029] The network 130 allows wireless communication and information exchange between vehicles and X ("X" means vehicle, road, pedestrian or Internet, etc.) according to the agreed communication protocol and data exchange standard. Examples of the network 130 include a combination of a local area network (LAN), a wide area network (WAN), a personal area network (PAN), and / or a communication network such as the Internet. The network 130 can be a wired or wireless network. In one example, the network 130 can be an in-vehicle network, an inter-vehicle network and / or an in-vehicle mobile Internet.

[0030] For the purpose of the embodiments of this disclosure, Figure 1In the example of , the application 112 may be an electronic map application, which may provide various functions based on the electronic map, such as navigation, route query, location search, parking location search, etc. Correspondingly, the server 120 may be a server used together with the electronic map application. The server 120 may provide online map services, such as online navigation, online route query, and online location search, etc., to the application 112 running in the vehicle system 110 based on the road network data. Alternatively, the server 120 may also provide the road network data to the vehicle system 110, and the application 112 running in the vehicle system 110 may provide local map services based on the road network data.

[0031] Figure 2 2 is a flow chart illustrating a vehicle control method 200 according to an exemplary embodiment. The method 200 may be implemented in a vehicle-mounted system (eg, Figure 1 The execution of each step of the method 200 may be performed at the vehicle-mounted system 110 shown in FIG. 1 . That is, the execution subject of each step of the method 200 may be Figure 1 In some embodiments, the method 200 may be performed on a server (e.g., Figure 1 In some embodiments, method 200 may be performed by a vehicle-mounted system (e.g., vehicle-mounted system 110) and a server (e.g., server 120) in combination. Hereinafter, the various steps of method 200 are described by taking the vehicle-mounted system 110 as an example. According to an embodiment of the present disclosure, method 200 may be used to control the steering wheel angle of a vehicle in real time during automatic parking of the vehicle. Figure 2 As shown, the method 200 includes:

[0032] Step S210, obtaining a self-correcting lateral dynamics model of the vehicle and a motion state of the vehicle at a current moment, wherein the self-correcting lateral dynamics model establishes a mapping between a steering wheel angle of the vehicle and a turning curvature of the vehicle, wherein the turning curvature is a function of a self-correcting coefficient and a steering wheel angle;

[0033] Step S220, determining the actual turning curvature of the vehicle based on the motion state quantity;

[0034] Step S230, determining a theoretical turning curvature of the vehicle based on the self-correcting lateral dynamics model and the motion state quantity;

[0035] Step S240, determining the value of the self-correction coefficient at the current moment based on the actual turning curvature and the theoretical turning curvature; and

[0036] Step S250, controlling the steering wheel angle of the vehicle based on the value of the self-correction coefficient at the current moment and the self-correction lateral dynamics model.

[0037] Each step of method 200 is described in detail below.

[0038] In step S210, the self-corrected lateral dynamics model of the vehicle and the motion state of the vehicle at the current moment may be first obtained. Figure 4A , which shows a schematic block diagram of a conventional vehicle lateral dynamics model and its inverse model. Generally speaking, the vehicle lateral dynamics model may refer to any suitable vehicle lateral dynamics model that establishes a mapping between the vehicle's steering wheel angle and the vehicle's turning curvature. Thus, the steering wheel angle δ Vehicle turning curvature κ Vehicle lateral dynamics model and its inverse model :

[0039] ,

[0040] ,

[0041] in, i is the transmission ratio from steering wheel angle to front wheel angle, L is the vehicle wheelbase, both of which are inherent properties of the vehicle and are constant values. Figure 4A As shown, the vehicle lateral dynamics model The input is the steering wheel angle δ , the output is the vehicle turning curvature κ ; The inverse model of vehicle lateral dynamics The input is the vehicle turning curvature κ , the output is the steering wheel angle δ Therefore, when the steering wheel is turned δ Input vehicle lateral dynamics model Then, the vehicle turning curvature can be obtained. κ On the contrary, when the vehicle is turned to a curvature κ Input to the inverse vehicle lateral dynamics model Then, the steering wheel angle can be obtained δ Although conventional vehicle lateral dynamics models and their inverse models establish a direct mapping between the vehicle's steering wheel angle and the vehicle's turning curvature, such models and their inverse models do not incorporate a self-correction mechanism, making it impossible to self-correct the model's inaccuracies caused by various interference factors.

[0042] It should be noted that in the context of the present disclosure, the term lateral dynamics model of the vehicle may refer to both the lateral dynamics model of the vehicle and its mathematically transformed inverse model. Therefore, when the lateral dynamics model of the vehicle is mentioned, if there is no clear distinction, it is a general term for the lateral dynamics model of the vehicle and its inverse model.

[0043] It should also be noted that the vehicle lateral dynamics model in equation 1-2 above and its inverse model The expression is merely exemplary, and relevant technicians can select any appropriate vehicle lateral dynamics model for establishing a mapping between the steering wheel angle of the vehicle and the turning curvature of the vehicle.

[0044] According to an embodiment of the present disclosure, the vehicle lateral dynamics model from the steering wheel angle δ to the vehicle curvature κ can be Based on this, a vehicle lateral dynamics model with a self-correction coefficient is established, that is, the vehicle's self-correction lateral dynamics model. Figure 4B , which illustrates a schematic block diagram of a vehicle self-correction lateral dynamics model and its inverse model according to an exemplary embodiment. In order to distinguish it from the traditional vehicle lateral dynamics model and its inverse model ,use and To represent the vehicle self-correction lateral dynamics model and its inverse model:

[0045] ,

[0046] ,

[0047] Among them, as mentioned above, i is the transmission ratio from steering wheel angle to front wheel angle, L is the vehicle wheelbase, both of which are inherent properties of the vehicle and are constant; and is the self-correction coefficient, and its initial value can be set to 1. Figure 4B As shown, the vehicle self-correction lateral dynamics model The input is the steering wheel angle δ , the output is the vehicle turning curvature κ ; The vehicle self-correction lateral dynamics inverse model The input is the vehicle turning curvature κ , the output is the steering wheel angle δ When you turn the steering wheel δ Input vehicle self-correcting lateral dynamics model Then, the vehicle turning curvature can be obtained. κ On the contrary, when the vehicle is turned to a curvature κ Input to the inverse model of vehicle self-correction lateral dynamics Then, the steering wheel angle can be obtained δ However, compared with the traditional vehicle lateral dynamics model The difference is that the vehicle self-corrects the lateral dynamics model Output κThe value depends on the steering wheel angle δ and self-correction coefficient Similarly, for the inverse model, the vehicle self-correction lateral dynamics inverse model Output δ The value depends on the vehicle turning curvature κ and self-correction coefficient According to an embodiment of the present disclosure, based on the vehicle self-correction lateral dynamics model and its inverse model, the turning curvature κ So the self-correction coefficient and steering wheel angle δ is a function of, and the steering wheel angle δ is the self-correction coefficient and curvature κ Therefore, during the vehicle parking process, the vehicle lateral dynamics model can be corrected by adjusting the self-correction coefficient, eliminating the reliance of the precise control of vehicle parking on manual calibration.

[0048] It should be noted that, in the context of the present disclosure, unless otherwise explicitly indicated, the term self-correcting lateral dynamics model of a vehicle may refer to both the self-correcting lateral dynamics model of a vehicle and its mathematically transformed inverse model. Therefore, when referring to the self-correcting lateral dynamics model of a vehicle, if there is no explicit distinction, it is a general term for the self-correcting lateral dynamics model of a vehicle and its inverse model.

[0049] It should also be noted that the vehicle self-correction lateral dynamics model in equation 3-4 above and its inverse model The expression is merely exemplary, and relevant technicians can select any suitable vehicle self-correction lateral dynamics model that is used to establish a mapping between the steering wheel angle of the vehicle and the turning curvature of the vehicle and has a self-correction mechanism.

[0050] According to an embodiment of the present disclosure, the self-correcting lateral dynamics model establishes a mapping between a steering wheel angle of a vehicle and a turning curvature of the vehicle, and the turning curvature is a function of a self-correcting coefficient and a steering wheel angle.

[0051] In the context of the present disclosure, the term vehicle's motion state quantity may refer to a physical quantity related to the vehicle's motion state or may refer to a physical quantity related to the motion state of the vehicle's components that control its motion (including speed, steering, etc.). When the vehicle's motion state quantity refers to a physical quantity related to the vehicle's motion state, the motion state quantity may, for example, include the vehicle's speed, the vehicle's yaw rate, etc. When the vehicle's motion state quantity refers to a physical quantity related to the motion state of the vehicle's components that control its motion, the motion state quantity may, for example, include the vehicle's actual steering wheel angle, etc. It should be noted that the value of the vehicle's motion state quantity generally changes over time, that is, the value of the vehicle's motion state quantity at different times may be different.

[0052] Return to reference Figure 2 In step S220, the actual turning curvature of the vehicle may be determined based on the motion state of the vehicle. It is understood that, as a representation of the actual turning degree of the vehicle, the actual turning curvature of the vehicle may be obtained based on monitoring the motion state of the vehicle.

[0053] In step S230, the theoretical turning curvature of the vehicle can be determined based on the self-correcting lateral dynamics model and the motion state quantity of the vehicle. It can be understood that as a representation of the theoretical turning degree of the vehicle, the theoretical turning curvature of the vehicle can be obtained at least partially based on the self-correcting lateral dynamics model of the vehicle. Further, the input of the self-correcting lateral dynamics model of the vehicle comes from the motion state quantity of the monitored vehicle. Therefore, the theoretical turning curvature of the vehicle depends on both the self-correcting lateral dynamics model and the motion state quantity of the vehicle.

[0054] In step S240, the value of the self-correction coefficient at the current moment can be determined based on the actual turning curvature and the theoretical turning curvature. When there is a difference between the actual turning curvature of the vehicle and the theoretical turning curvature, it means that the self-correction lateral dynamics model of the vehicle may be inaccurate due to some interference factor(s). At this time, it is necessary to resort to the self-correction mechanism of the model itself for self-correction. That is, the value of the self-correction coefficient at the current moment is different from the value of the self-correction coefficient at the previous moment. When there is (almost) no difference between the actual turning curvature of the vehicle and the theoretical turning curvature, it means that the self-correction lateral dynamics model of the vehicle is accurate. At this time, there is no need to resort to any self-correction mechanism to self-correct the model. That is, the value of the self-correction coefficient at the current moment can be maintained as the value of the self-correction coefficient at the previous moment.

[0055] In step S250, the steering wheel angle of the vehicle may be controlled based on the value of the self-correction coefficient at the current moment and the self-correction lateral dynamics model.

[0056] According to the embodiment of the present disclosure, the above method 200 overcomes the defect of calibrating the dynamic model of the vehicle through a large amount of tedious and meticulous calibration work in order to improve the accuracy of the feedforward control in the related art. The method 200 determines the current value of the self-correction coefficient based on the actual turning curvature and theoretical turning curvature of the vehicle, and controls the steering wheel angle of the vehicle based on the current value of the self-correction coefficient and the self-correction lateral dynamic model, thereby correcting the lateral dynamic model of the vehicle during parking, so that the model tends to be consistent with the actual, improving the accuracy of the steering wheel feedforward calculation of the vehicle lateral control, and thus improving the lateral control accuracy of the vehicle.

[0057] Through the above method 200, the vehicle lateral dynamics model can be calibrated online, reducing the dependence of the precise control involved in the vehicle parking process on manual calibration, and improving the adaptability and robustness of the vehicle motion control algorithm.

[0058] According to an embodiment of the present disclosure, the vehicle's motion state at the current moment may include the vehicle's speed and the vehicle's yaw rate Further, based on the motion state quantity, determining the actual turning curvature of the vehicle may include: Calculated as the vehicle's yaw rate The speed of the vehicle That is:

[0059] .

[0060] Since the vehicle speed and yaw rate can be obtained and / or calculated by means of sensors carried by the vehicle itself, the actual turning curvature of the vehicle can be obtained in a simple manner.

[0061] Figure 3 is a flow chart illustrating a method 300 for determining a theoretical turning curvature of a vehicle according to an exemplary embodiment. The method 300 may be performed in a vehicle-mounted system (eg, Figure 1 The execution of each step of the method 300 may be performed at the vehicle-mounted system 110 shown in FIG. 1 . That is, the execution subject of each step of the method 300 may be Figure 1 In some embodiments, the method 300 may be performed on a server (e.g., Figure 1 In some embodiments, method 300 may be executed by a vehicle-mounted system (e.g., vehicle-mounted system 110) and a server (e.g., server 120) in combination. In the following, the steps of method 300 are described by taking the vehicle-mounted system 110 as an example. According to an embodiment of the present disclosure, the motion state of the vehicle at the current moment may include the actual steering wheel angle of the vehicle. .like Figure 3 As shown, the method 300 includes:

[0062] Step S310, obtaining the value of the self-correction coefficient at a previous time, where the previous time is a past time immediately before the current time; and

[0063] Step S320, by comparing the value of the self-correction coefficient at the previous moment with the actual steering wheel angle of the vehicle Substitute into the self-correcting lateral dynamics model , get the theoretical turning curvature of the vehicle .Right now:

[0064] .

[0065] Due to the vehicle's self-correcting lateral dynamics model Output κ The value depends on the steering wheel angle δ and self-correction coefficient Assuming that the model does not need to enable the self-correction mechanism due to some interference factors (i.e., the value of the self-correction coefficient at the current moment is maintained as the value of the self-correction coefficient at the previous moment), the value of the self-correction coefficient at the previous moment and the actual steering wheel angle of the vehicle can be used to calculate the self-correction coefficient. Substitute into the self-correcting lateral dynamics model To obtain the theoretical turning curvature of the vehicle at the current moment , thereby obtaining a benchmark that can be compared with the actual turning curvature of the vehicle to determine whether to adjust the self-correction coefficient of the vehicle's self-correction lateral dynamics model.

[0066] According to the embodiments of the present disclosure, the self-correction coefficient can be adjusted based on the actual turning curvature and theoretical turning curvature of the vehicle, thereby realizing the self-correction of the vehicle self-correction lateral dynamics model. As an example but not a limitation, generally, the self-correction coefficient can be determined according to the following formula 7-8 and adjustment amount : ,

[0067] ,

[0068] Among them, as mentioned above, is the theoretical turning curvature of the vehicle, is the actual turning curvature of the vehicle; and Adjust the gain for the self-correction factor, which can be obtained, for example, by actual debugging; is the self-correction threshold, which can be set to 0.01, for example, indicating that the real-time self-correction is enabled only when the vehicle is turning, and is the time step. It should be noted that when the vehicle is moving straight, the steering wheel angle is almost 0, so ,at this time Therefore, self-correction must be prohibited when the vehicle is moving straight, otherwise it will lead to incorrect estimation. value.

[0069] According to an embodiment of the present disclosure, as a variation of Formula 7, the value of the self-correction coefficient at the current moment can be determined, for example, according to Formula 9: :

[0070] ,

[0071] Among them, as mentioned above, is the theoretical turning curvature of the vehicle, is the actual turning curvature of the vehicle, The gain is adjusted by a self-correction factor, which can be obtained, for example, by actual debugging, is the self-correction threshold, is the time step; is the value of the self-correction coefficient at the previous moment.

[0072] It should be noted that the self-correction coefficient expression or the adjustment amount of the self-correction coefficient in equation 7-9 above is The expression of or the value of the self-correction coefficient at the current moment The expressions are merely exemplary, and relevant technicians can conceive of various suitable variations.

[0073] According to an embodiment of the present disclosure, based on the actual turning curvature and the theoretical turning curvature, determining the value of the self-correction coefficient at the current moment may include: in response to determining that the actual turning curvature of the vehicle is greater than or equal to the preset turning curvature, calculating the value of the self-correction coefficient at the current moment as the sum of the value of the self-correction coefficient at the previous moment and a self-correction coefficient adjustment amount. Additionally and / or alternatively, the self-correction coefficient adjustment amount is proportional to the difference obtained by subtracting the actual turning curvature from the theoretical turning curvature. In this way, the vehicle lateral dynamics model can be corrected during the parking process so that the model tends to be consistent with the actual situation, thereby improving the accuracy of the steering wheel feedforward calculation of the vehicle lateral control.

[0074] According to an embodiment of the present disclosure, based on the actual turning curvature and the theoretical turning curvature, determining the value of the self-correction coefficient at the current moment may include: in response to determining that the actual turning curvature of the vehicle is less than the preset turning curvature, setting the value of the self-correction coefficient at the current moment to the value of the self-correction coefficient at the previous moment. In this way, miscalibration of the vehicle lateral dynamics model when the vehicle is traveling straight is avoided.

[0075] According to an embodiment of the present disclosure, the preview time can be As the index, find the corresponding preview trajectory point on the planned trajectory and extract the curvature on the trajectory point , and then the corrected vehicle lateral dynamics model is used to calculate the steering wheel angle feedforward control quantity to control the vehicle. That is:

[0076] .

[0077] In the context of this disclosure, the term preview time has the meaning known in the art. In the process of the driver driving the vehicle, it is assumed that the time when the vehicle reaches point A is , and the driver sees point B ahead, the time when the vehicle actually reaches point B is , then the preview time can be The preview time reflects the driver's forward-looking strategy, that is, the judgment and processing of the vehicle's expected driving trajectory.

[0078] According to an embodiment of the present disclosure, the above method 200 may optionally include the following steps: obtaining the planned parking trajectory and preview time of the vehicle; determining the preview trajectory point corresponding to the preview time on the planned parking trajectory; and determining the curvature at the preview trajectory point. Further, based on the value of the self-correction coefficient at the current moment and the self-correction lateral dynamics model, controlling the steering wheel angle of the vehicle includes: substituting the value of the self-correction coefficient at the current moment and the determined curvature at the preview trajectory point into the self-correction lateral dynamics model to obtain the steering wheel angle of the vehicle at the preview time. Thereby, the lateral control accuracy of the vehicle in the parking scenario is further improved.

[0079] According to an embodiment of the present disclosure, any of the method embodiments described above can be implemented in response to determining that the vehicle speed is greater than a preset vehicle speed. Thus, miscalibration of the vehicle lateral dynamics model when the vehicle is stationary or parked can be avoided, thereby saving computing and storage resources of the vehicle system.

[0080] Figure 5is a schematic block diagram illustrating a vehicle control device 500 according to an exemplary embodiment. The device 500 includes: a first module 510, for obtaining a self-correcting lateral dynamics model of a vehicle and a motion state quantity of the vehicle at a current moment, wherein the self-correcting lateral dynamics model establishes a mapping between a steering wheel angle of the vehicle and a turning curvature of the vehicle, wherein the turning curvature is a function of a self-correcting coefficient and a steering wheel angle; a second module 520, for determining an actual turning curvature of the vehicle based on the motion state quantity; a third module 530, for determining a theoretical turning curvature of the vehicle based on the self-correcting lateral dynamics model and the motion state quantity; a fourth module 540, for determining a value of the self-correcting coefficient at a current moment based on the actual turning curvature and the theoretical turning curvature; and a fifth module 550, for controlling a steering wheel angle of the vehicle based on the value of the self-correcting coefficient at a current moment and the self-correcting lateral dynamics model.

[0081] It should be understood that Figure 5 The various modules of the apparatus 500 shown in FIG. Figure 2 The various steps in the described method 200 correspond to each other. Therefore, the operations, features and advantages described above for the method 200 are also applicable to the apparatus 500 and the modules included therein.

[0082] According to the embodiments of the present disclosure, the above-mentioned device 500 overcomes the defect of calibrating the dynamic model of the vehicle through a large amount of tedious and meticulous calibration work in order to improve the accuracy of the feedforward control in the related art. The device 500 determines the current value of the self-correction coefficient based on the actual turning curvature and theoretical turning curvature of the vehicle, and controls the steering wheel angle of the vehicle based on the current value of the self-correction coefficient and the self-correction lateral dynamic model, thereby correcting the lateral dynamic model of the vehicle during parking, so that the model tends to be consistent with the actual situation, improving the accuracy of the steering wheel feedforward calculation of the vehicle lateral control, and thus improving the lateral control accuracy of the vehicle.

[0083] Although specific functions are discussed above with reference to specific modules, it should be noted that the functions of the various modules discussed herein may be divided into multiple modules, and / or at least some of the functions of multiple modules may be combined into a single module. For example, the second module 520 and the third module 530 may be combined into a single module to determine both the actual turning curvature of the vehicle and the theoretical turning curvature of the vehicle. The specific module discussed herein performing an action includes the specific module itself performing the action, or alternatively the specific module calling or otherwise accessing another component or module that performs the action (or performs the action in conjunction with the specific module). Therefore, the specific module that performs an action may include the specific module itself that performs the action and / or another module that the specific module calls or otherwise accesses to perform the action.

[0084] As used herein, the phrase “based on A, B, and C, performing action Z” may mean performing action Z based only on A, only on B, only on C, based on A and B, based on A and C, based on B and C, or based on A, B, and C.

[0085] It should also be understood that various techniques may be described herein in the general context of software hardware elements or program modules. Figure 5 The modules described can be implemented in hardware or in hardware in combination with software and / or firmware. For example, these modules can be implemented as computer program codes / instructions, which are configured to be executed in one or more processors and stored in a computer-readable storage medium. Alternatively, these modules can be implemented as hardware logic / circuits. For example, in some embodiments, one or more of the first module 510 to the fifth module 550 can be implemented together in a system on chip (System on Chip, SoC). SoC may include an integrated circuit chip (which includes a processor (e.g., a central processing unit (CPU), a microcontroller, a microprocessor, a digital signal processor (DSP), etc.), a memory, one or more communication interfaces, and / or one or more components in other circuits), and may optionally execute the received program code and / or include embedded firmware to perform functions.

[0086] According to one aspect of the present disclosure, a computing device is provided. The computing device includes at least one memory, at least one processor, and a computer program stored on the at least one memory. The at least one processor is configured to execute the computer program to implement the steps of any method embodiment described above.

[0087] According to one aspect of the present disclosure, a vehicle is provided, which includes the vehicle control device or computer apparatus as described above.

[0088] According to one aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any method embodiment described above are implemented.

[0089] According to one aspect of the present disclosure, a computer program product is provided, which includes a computer program. When the computer program is executed by a processor, the steps of any method embodiment described above are implemented.

[0090] In the following, combined Figure 6 Illustrative examples of such a computer device, non-transitory computer-readable storage medium, and computer program product are described.

[0091] Figure 6 An example configuration of a computer device 600 that can be used to implement the methods described herein is shown. For example, Figure 1 The server 120 and / or the vehicle system 110 shown in FIG. 5 may include an architecture similar to the computer device 600. The above-mentioned apparatus 500 or computer device may also be implemented in whole or at least in part by the computer device 600 or a similar device or system.

[0092] Computer device 600 may include at least one processor 602, memory 604, communication interface(s) 606, a display device 608, other input / output (I / O) devices 610, and one or more mass storage devices 612, all capable of communicating with one another, such as via a system bus 614 or other appropriate connections.

[0093] The processor 602 may be a single processing unit or multiple processing units, all of which may include a single or multiple computing units or multiple cores. The processor 602 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and / or any device that manipulates signals based on operating instructions. Among other capabilities, the processor 602 may be configured to obtain and execute computer-readable instructions stored in the memory 604, mass storage device 612, or other computer-readable media, such as program code of an operating system 616, program code of an application program 618, program code of other programs 620, and the like.

[0094] The memory 604 and the mass storage device 612 are examples of computer-readable storage media for storing instructions that are executed by the processor 602 to implement the various functions described above. For example, the memory 604 may generally include both volatile memory and non-volatile memory (e.g., RAM, ROM, etc.). In addition, the mass storage device 612 may generally include a hard drive, a solid-state drive, a removable medium, including external and removable drives, a memory card, a flash memory, a floppy disk, an optical disk (e.g., a CD, a DVD), a storage array, a network attached storage, a storage area network, etc. The memory 604 and the mass storage device 612 may all be collectively referred to herein as memory or computer-readable storage media, and may be a non-transitory medium capable of storing computer-readable, processor-executable program instructions as computer program code, which may be executed by the processor 602 as a specific machine configured to implement the operations and functions described in the examples herein.

[0095] A plurality of programs may be stored on the mass storage device 612. These programs include an operating system 616, one or more application programs 618, other programs 620, and program data 622, and they may be loaded into the memory 604 for execution. Examples of such application programs or program modules may include, for example, computer program logic (e.g., computer program code or instructions) for implementing the following method steps / component functions: method 200, method 300 and optional additional steps thereof, apparatus 500, and / or other embodiments described herein.

[0096] Although in Figure 6 604 of the computer device 600, but modules 616, 618, 620, and 622, or portions thereof, may be implemented using any form of computer-readable media accessible by the computer device 600. As used herein, "computer-readable media" includes at least two types of computer-readable media, namely, computer-readable storage media and communication media.

[0097] Computer-readable storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information, such as computer-readable instructions, data structures, program modules or other data. Computer-readable storage media include but are not limited to RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD), or other optical storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other non-transmission media that can be used to store information for access by a computer device. In contrast, communication media can specifically implement computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transmission mechanism. Computer-readable storage media defined herein do not include communication media.

[0098] One or more communication interfaces 606 are used to exchange data with other devices, such as through a network, a direct connection, etc. Such communication interfaces can be one or more of the following: any type of network interface (e.g., a network interface card (NIC)), a wired or wireless (such as IEEE 802.11 wireless LAN (WLAN)) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a TMThe communication interface 606 may facilitate communication within a variety of network and protocol types, including wired networks (e.g., LAN, cable, etc.) and wireless networks (e.g., WLAN, cellular, satellite, etc.), the Internet, etc. The communication interface 606 may also provide for communication with external storage devices (not shown) such as in a storage array, network attached storage, a storage area network, etc.

[0099] In some examples, a display device 608 such as a monitor may be included for displaying information and images to the user. Other I / O devices 610 may be devices that receive various inputs from the user and provide various outputs to the user, and may include a touch input device, a gesture input device, a camera, a keyboard, a remote control, a mouse, a printer, an audio input / output device, and the like.

[0100] The technology described herein can be supported by these various configurations of the computer device 600, and is not limited to the specific examples of the technology described herein. For example, the functionality can also be implemented in whole or in part on the "cloud" by using a distributed system. The cloud includes and / or represents a platform for resources. The platform abstracts the underlying functionality of the hardware (e.g., server) and software resources of the cloud. Resources can include applications and / or data that can be used when performing computing processing on a server away from the computer device 600. Resources can also include services provided over the Internet and / or through a subscriber network such as a cellular or Wi-Fi network. The platform can abstract resources and functions to connect the computer device 600 to other computer devices. Therefore, the implementation of the functions described herein can be distributed throughout the cloud. For example, the functions can be implemented partially on the computer device 600 and partially through a platform that abstracts the functions of the cloud.

[0101] Although the present disclosure has been illustrated and described in detail in the drawings and the foregoing description, such illustration and description should be considered illustrative and schematic, not restrictive; the present disclosure is not limited to the disclosed embodiments. By studying the drawings, the disclosure and the appended claims, those skilled in the art will be able to understand and implement variations to the disclosed embodiments when practicing the claimed subject matter. In the claims, the word "comprising" does not exclude other elements or steps that are not listed, the indefinite article "a" or "an" does not exclude a plurality, the term "plurality" means two or more, and the term "based on" should be interpreted as "based at least in part on". The mere fact that certain measures are recorded in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

Claims

1. A vehicle control method for controlling a steering wheel angle of a vehicle in real time during automatic parking of the vehicle, the method comprising: Acquire a self-correcting lateral dynamics model of the vehicle and a motion state of the vehicle at a current moment, wherein the self-correcting lateral dynamics model establishes a mapping between a steering wheel angle of the vehicle and a turning curvature of the vehicle, wherein the turning curvature is a function of a self-correcting coefficient and the steering wheel angle; determining an actual turning curvature of the vehicle based on the motion state quantity; determining a theoretical turning curvature of the vehicle based on the self-correcting lateral dynamics model and the motion state quantity; Determining a value of the self-correction coefficient at the current moment based on the actual turning curvature and the theoretical turning curvature; and Based on the value of the self-correction coefficient at the current moment and the self-correction lateral dynamics model, the steering wheel angle of the vehicle is controlled, The motion state quantity includes an actual steering wheel angle of the vehicle, and determining a theoretical turning curvature of the vehicle based on the self-correcting lateral dynamics model and the motion state quantity includes: Obtaining a value of the self-correction coefficient at a previous time, where the previous time is a past time immediately before the current time; and The theoretical turning curvature of the vehicle is obtained by substituting the value of the self-correction coefficient at the previous moment and the actual steering angle of the vehicle into the self-correction lateral dynamics model, Wherein, based on the actual turning curvature and the theoretical turning curvature, determining the value of the self-correction coefficient at the current moment includes: In response to determining that the actual turning curvature of the vehicle is greater than or equal to a preset turning curvature, calculating the value of the self-correction coefficient at the current moment as the sum of the value of the self-correction coefficient at the previous moment and a self-correction coefficient adjustment amount, wherein the self-correction coefficient adjustment amount is proportional to a difference obtained by subtracting the actual turning curvature from the theoretical turning curvature; In response to determining that the actual turning curvature of the vehicle is less than a preset turning curvature, the value of the self-correction coefficient at the current moment is set to the value of the self-correction coefficient at the previous moment.

2. The method according to claim 1, wherein: The motion state quantity includes the vehicle speed and the yaw rate of the vehicle, Wherein, determining the actual turning curvature of the vehicle based on the motion state quantity includes: calculating the actual turning curvature of the vehicle as a ratio of the yaw angular velocity of the vehicle to the vehicle speed of the vehicle.

3. The method according to claim 1, further comprising: Obtaining the planned parking trajectory and preview time of the vehicle; Determining a preview trajectory point corresponding to the preview time on the planned parking trajectory; as well as determining the curvature at the preview trajectory point, Wherein, based on the value of the self-correction coefficient at the current moment and the self-correction lateral dynamics model, controlling the steering wheel angle of the vehicle includes: By substituting the value of the self-correction coefficient at the current moment and the curvature at the preview trajectory point into the self-correction lateral dynamics model, the steering wheel angle of the vehicle at the preview time is obtained.

4. The method according to any one of claims 1 to 3, wherein: The motion state quantity includes a vehicle speed of the vehicle, and the method is performed in response to determining that the vehicle speed of the vehicle is greater than a preset vehicle speed.

5. A vehicle control device for controlling the steering wheel angle of a vehicle in real time during automatic parking of the vehicle, the device comprising: A first module is used to obtain a self-correcting lateral dynamics model of the vehicle and a motion state of the vehicle at a current moment, wherein the self-correcting lateral dynamics model establishes a mapping between a steering wheel angle of the vehicle and a turning curvature of the vehicle, wherein the turning curvature is a function of a self-correcting coefficient and the steering wheel angle; A second module is used to determine the actual turning curvature of the vehicle based on the motion state quantity; A third module is used to determine a theoretical turning curvature of the vehicle based on the self-correcting lateral dynamics model and the motion state quantity; A fourth module is used to determine the value of the self-correction coefficient at the current moment based on the actual turning curvature and the theoretical turning curvature; and a fifth module, for controlling the steering wheel angle of the vehicle based on the value of the self-correction coefficient at the current moment and the self-correction lateral dynamics model, The motion state quantity includes an actual steering wheel angle of the vehicle, and determining a theoretical turning curvature of the vehicle based on the self-correcting lateral dynamics model and the motion state quantity includes: Obtaining a value of the self-correction coefficient at a previous time, where the previous time is a past time immediately before the current time; and The theoretical turning curvature of the vehicle is obtained by substituting the value of the self-correction coefficient at the previous moment and the actual steering angle of the vehicle into the self-correction lateral dynamics model, Wherein, based on the actual turning curvature and the theoretical turning curvature, determining the value of the self-correction coefficient at the current moment includes: In response to determining that the actual turning curvature of the vehicle is greater than or equal to a preset turning curvature, calculating the value of the self-correction coefficient at the current moment as the sum of the value of the self-correction coefficient at the previous moment and a self-correction coefficient adjustment amount, wherein the self-correction coefficient adjustment amount is proportional to a difference obtained by subtracting the actual turning curvature from the theoretical turning curvature; In response to determining that the actual turning curvature of the vehicle is less than a preset turning curvature, the value of the self-correction coefficient at the current moment is set to the value of the self-correction coefficient at the previous moment.

6. A computer device, comprising: at least one processor; as well as at least one memory having a computer program stored thereon, Wherein, when the computer program is executed by the at least one processor, the at least one processor executes the method according to any one of claims 1 to 4.

7. A vehicle comprising the vehicle control device according to claim 5 or the computer device according to claim 6.

8. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor is caused to execute the method according to any one of claims 1 to 4.

9. A computer program product, comprising a computer program, which, when executed by a processor, causes the processor to execute the method according to any one of claims 1 to 4.

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