Trajectory tracking control parameter adjustment method and device, equipment, medium and vehicle

Abstract

The invention discloses a trajectory tracking control parameter adjusting method and device, equipment, a medium and a vehicle, and belongs to the technical field of vehicles. Constructing an LQR tracking model; according to a preset vehicle driving track and an LQR tracking model, simulation is carried out, and then a simulation result is determined; and determining an adjustment strategy of the trajectory tracking control parameters according to the simulation result, and then executing the adjustment strategy. According to the method, the trajectory tracking control parameters are adjusted through virtual simulation, so that the LQR controller of the vehicle can adapt to different road conditions, the performance, robustness and efficiency of path tracking of the automatic driving vehicle are improved, and the use requirements of users are effectively met.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a trajectory tracking control parameter adjustment method, device, equipment, medium and vehicle. Background Art

[0002] Currently, with the development of vehicle technology, vehicle autonomous driving is receiving more and more attention.

[0003] In the related art, the linear quadratic regulator (LQR) strategy is commonly used in the path tracking control of autonomous vehicles. However, in actual applications, it is found that the control parameters of LQR need to be manually adjusted according to specific road conditions, which not only has limited robustness, but also has a high adjustment time cost, consuming a lot of time and resources.

[0004] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0005] The embodiments of the present application provide a trajectory tracking control parameter adjustment method, device, equipment, medium and vehicle, which can enable the vehicle's LQR controller to adapt to different road conditions, improve the performance, robustness and efficiency of autonomous driving vehicle path tracking, and effectively meet the user's usage needs.

[0006] On the one hand, an embodiment of the present application provides a method for adjusting trajectory tracking control parameters, comprising the following steps:

[0007] Determine a preset driving trajectory of the vehicle;

[0008] Construct LQR tracking model;

[0009] Perform simulation according to the preset vehicle driving trajectory and the LQR tracking model, and then determine the simulation result;

[0010] According to the simulation results, an adjustment strategy for trajectory tracking control parameters is determined, and then the adjustment strategy is executed.

[0011] Optionally, the simulation method is a hardware-in-the-loop simulation method; the simulation is performed according to the preset vehicle driving trajectory and the LQR tracking model, and then the simulation result is determined, including:

[0012] Constructing a vehicle dynamic model; the vehicle dynamic model includes a kinematic model and a dynamic model;

[0013] Initializing the Q matrix and R matrix of the LQR tracking model;

[0014] According to the vehicle dynamic model and the LQR tracking model, the driving trajectory of the vehicle is simulated by the hardware-in-the-loop simulation method to obtain a simulated driving trajectory of the vehicle;

[0015] The preset driving trajectory of the vehicle is compared with the simulated driving trajectory of the vehicle to determine a simulation result.

[0016] Optionally, the simulation result includes a trajectory deviation; and determining an adjustment strategy for a trajectory tracking control parameter according to the simulation result, and then executing the adjustment strategy, comprises:

[0017] Determining a trajectory deviation between a preset driving trajectory of the vehicle and a simulated driving trajectory of the vehicle;

[0018] When the trajectory deviation is greater than or equal to a first threshold, determining that the adjustment strategy of the trajectory tracking control parameter is to optimize the Q matrix and the R matrix, and after optimizing the Q matrix and the R matrix, performing simulation again until the trajectory deviation is less than the first threshold;

[0019] When the trajectory deviation is less than the first threshold, the parameter settings of the Q matrix and the R matrix of the current LQR tracking model are determined, and an adjustment strategy for trajectory tracking control parameters is determined according to the parameter settings, and then the adjustment strategy is executed.

[0020] Optionally, the method further comprises:

[0021] In response to the triggered update instruction, the first threshold is updated.

[0022] Optionally, the update instruction includes a voice update instruction; and the updating of the first threshold in response to the triggered update instruction includes:

[0023] In response to the triggered voice update instruction, voice data is acquired, voice recognition is performed on the voice data, and the first threshold is updated according to the result of the voice recognition.

[0024] Optionally, the update instruction includes a text input update instruction; and updating the first threshold in response to the triggered update instruction includes:

[0025] In response to the triggered text input update instruction, text input data is acquired, text recognition is performed on the text input data, and the first threshold is updated according to the result of the text recognition.

[0026] On the other hand, an embodiment of the present application provides a trajectory tracking control parameter adjustment device, the device comprising:

[0027] A trajectory determination module, used to determine a preset driving trajectory of the vehicle;

[0028] Model building module, used to build LQR tracking model;

[0029] A simulation operation module, used to perform simulation according to the preset vehicle driving trajectory and the LQR tracking model, and then determine the simulation result;

[0030] The parameter adjustment module is used to determine the adjustment strategy of the trajectory tracking control parameters according to the simulation results, and then execute the adjustment strategy.

[0031] On the other hand, an embodiment of the present application provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned trajectory tracking control parameter adjustment method when executing the computer program.

[0032] On the other hand, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned trajectory tracking control parameter adjustment method is implemented.

[0033] On the other hand, an embodiment of the present application provides a vehicle, which includes the above-mentioned trajectory tracking control parameter adjustment device or the above-mentioned electronic device.

[0034] The embodiment of the present application adjusts the trajectory tracking control parameters through virtual simulation, so that the vehicle's LQR controller can adapt to different road conditions, improve the performance, robustness and efficiency of the autonomous driving vehicle's path tracking, and effectively meet the user's usage needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of an implementation environment of a trajectory tracking control parameter adjustment method provided in an embodiment of the present application;

[0036] Figure 2 It is a flow chart of a trajectory tracking control parameter adjustment method provided in an embodiment of the present application;

[0037] Figure 3 It is a schematic diagram of a process for determining simulation results provided by an embodiment of the present application;

[0038] Figure 4 It is a flow chart of a strategy for determining an adjustment of trajectory tracking control parameters provided in an embodiment of the present application;

[0039] Figure 5 It is a schematic diagram of a process of multiple simulation of trajectory tracking control parameters provided in an embodiment of the present application;

[0040] Figure 6 It is a structural schematic diagram of a trajectory tracking control parameter adjustment device provided in an embodiment of the present application;

[0041] Figure 7 It is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0043] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0044] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0046] Currently, with the development of vehicle technology, vehicle autonomous driving is receiving more and more attention.

[0047] In the related art, the linear quadratic regulator (LQR) strategy is commonly used in the path tracking control of autonomous vehicles. However, in actual applications, it is found that the control parameters of LQR need to be manually adjusted according to specific road conditions, which not only has limited robustness, but also has a high adjustment time cost, consuming a lot of time and resources.

[0048] In view of this, the embodiments of the present application provide a trajectory tracking control parameter adjustment method, device, equipment, medium and vehicle, which adjust the trajectory tracking control parameters through virtual simulation, so that the vehicle's LQR controller can adapt to different road conditions, improve the performance, robustness and efficiency of the path tracking of the autonomous driving vehicle, and effectively meet the user's usage needs.

[0049] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0050] The specific implementation of the embodiment of the present application is described in detail below in conjunction with the accompanying drawings. First, a trajectory tracking control parameter adjustment method provided in the embodiment of the present application is described in conjunction with the accompanying drawings.

[0051] Please refer to Figure 1 , Figure 1 The schematic diagram of the implementation environment of a trajectory tracking control parameter adjustment method provided by an embodiment of the present application is shown. In the implementation environment, the main hardware and software entities involved include a vehicle processor 110 and a server 120.

[0052] Specifically, the vehicle processor 110 may be installed with a control program for adjusting the trajectory tracking control parameters, and the server 120 is a background server for the control program. The vehicle processor 110 and the background server 120 are connected in communication. The trajectory tracking control parameter adjustment method provided in the embodiment of the present application may be executed on the vehicle processor 110 side.

[0053] Server 120 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0054] In addition, the server 120 may also be a node server in the blockchain network.

[0055] The vehicle processor 110 and the server 120 may establish a communication connection via a wireless network. The wireless network uses standard communication technology and / or protocols, and the network may be set to the Internet, or any other network, such as but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, or any combination of a wireless network, a private network, or a virtual private network. Furthermore, the above-mentioned software and hardware entities may use the same communication connection mode or different communication connection modes, and this application does not impose any specific restrictions on this.

[0056] Of course, it is understandable that Figure 1 The implementation environment in the embodiment of the present application is only some optional application scenarios of the trajectory tracking control parameter adjustment method provided in the embodiment of the present application, and the actual application is not fixed. Figure 1 The software and hardware environment shown is not specifically limited in this application.

[0057] Please refer to Figure 2 , Figure 2 It is a flow chart of a trajectory tracking control parameter adjustment method provided in an embodiment of the present application, which specifically includes but is not limited to steps S100 to S400.

[0058] Step S100: determining a preset driving trajectory of the vehicle.

[0059] In the embodiment of the present application, a driving reference trajectory can be preset according to the application scenario of the vehicle, and this trajectory represents the path that the vehicle should accurately track. It is understandable that the preset driving trajectory of the vehicle can be in various forms, such as a straight line, an arc, or a more complex path. For example, it can be a long straight high-speed driving trajectory, or a slow-speed following cruise path in a congested traffic environment in the city, etc. Different types of vehicle preset driving trajectories.

[0060] In practical applications, several different preset vehicle driving trajectories can be determined in advance based on the commonly used vehicle traffic scenarios obtained in the early stage, and simulation evaluation can be performed for each preset vehicle driving trajectory to determine the optimal trajectory tracking control parameters for the corresponding scenario and store them inside the vehicle processor. This can better improve the efficiency of trajectory tracking control parameter adjustment and enhance the user experience.

[0061] Step S200: construct an LQR tracking model.

[0062] In the embodiment of the present application, the LQR (Linear Quadratic Regulator) tracking model obtains the optimal control strategy by optimizing a quadratic objective function. In vehicle trajectory tracking, LQR usually hopes to design the optimal control input so that the system state can follow the given reference preset driving trajectory as much as possible.

[0063] Step S300: Perform simulation according to the preset vehicle driving trajectory and the LQR tracking model, and then determine the simulation result.

[0064] In the embodiment of the present application, after the LQR tracking model is constructed, simulation can be further performed. For example, the simulation can be based on discrete time steps, apply the LQR control law at each moment to calculate, and provide the trajectory and control input of the vehicle in the time domain.

[0065] Specifically, in an optional implementation, please refer to Figure 3 , Figure 3 : is a flow chart of determining a simulation result provided by an embodiment of the present application, wherein the simulation method is a hardware-in-the-loop simulation method; the simulation is performed according to the preset vehicle driving trajectory and the LQR tracking model, and then the simulation result is determined, including:

[0066] Step S310: constructing a vehicle dynamic model; the vehicle dynamic model includes a kinematic model and a dynamic model;

[0067] Step S320, initializing the Q matrix and R matrix of the LQR tracking model;

[0068] Step S330: According to the vehicle dynamic model and the LQR tracking model, the driving trajectory of the vehicle is simulated by the hardware-in-the-loop simulation method to obtain a simulated driving trajectory of the vehicle;

[0069] Step S340: Compare the preset driving trajectory of the vehicle with the simulated driving trajectory of the vehicle to determine a simulation result.

[0070] In the embodiment of the present application, a hardware in the loop (HIL) simulation method can be selected. HIL refers to testing the performance of actual hardware in a virtual environment, simulating the real-world environment, physical system and vehicle dynamics through a simulator, and inputting them into hardware (such as a controller) to obtain a response from the hardware.

[0071] Furthermore, a vehicle dynamic model can be constructed to improve the simulation effect, wherein the vehicle dynamic model includes a kinematic model and a dynamic model. It is understandable that the vehicle kinematic model usually considers the geometric motion relationship of the vehicle, which is suitable for low-speed or simpler control scenarios; the vehicle dynamic model usually considers the physical characteristics and external forces of the vehicle, which is suitable for high-speed or complex control scenarios.

[0072] For example, when constructing a vehicle's kinematic model, a bicycle model can be selected to describe the vehicle's motion relationship, including the vehicle's position, heading angle, speed, and steering angle; when constructing a vehicle's dynamic model, the Newton-Euler equation can be used to take into account the vehicle's dynamic characteristics such as steering and acceleration.

[0073] Furthermore, the initial Q matrix and R matrix can be set based on experience or preliminary guesses; the Q matrix is ​​used to measure the weight of the state error, and a larger Q value will increase the penalty for deviation, allowing the vehicle to track the trajectory more strictly; the R matrix is ​​used to punish the control input, and a larger R value limits the control strength to prevent excessive control.

[0074] Furthermore, according to the constructed vehicle dynamic model and the initialized LQR tracking model, the driving trajectory of the vehicle is simulated by using the HIL simulation method to obtain the simulated driving trajectory of the vehicle.

[0075] It is understandable that, after the simulated driving trajectory of the vehicle is determined, it can be compared with the pre-set preset driving trajectory of the vehicle to determine the simulation result.

[0076] Step S400: determining an adjustment strategy for trajectory tracking control parameters according to the simulation results, and then executing the adjustment strategy.

[0077] In an embodiment of the present application, when the simulation results are determined, the trajectory tracking performance of the vehicle can be determined according to the indicators in the simulation results. If the tracking performance is poor, the optimization adjustment direction of the Q matrix and R matrix of the LQR tracking model can be further determined according to the performance evaluation results, and the corresponding adjustment strategy can be executed; if the tracking performance is good, it can be determined that the current control parameters are adapted to the pre-set vehicle preset driving trajectory, and the corresponding parameters can be implemented in actual applications.

[0078] Specifically, in an optional implementation, please refer to Figure 4 , Figure 4 : is a flow chart of determining an adjustment strategy for trajectory tracking control parameters provided by an embodiment of the present application, wherein the simulation result includes a trajectory deviation; determining the adjustment strategy for trajectory tracking control parameters according to the simulation result, and then executing the adjustment strategy, including:

[0079] Step S410, determining a trajectory deviation between a preset driving trajectory of the vehicle and a simulated driving trajectory of the vehicle;

[0080] Step S420: when the trajectory deviation is greater than or equal to a first threshold, determining that the adjustment strategy of the trajectory tracking control parameter is to optimize the Q matrix and the R matrix, and after optimizing the Q matrix and the R matrix, performing simulation again until the trajectory deviation is less than the first threshold;

[0081] Step S430: When the trajectory deviation is less than the first threshold, determine the parameter settings of the Q matrix and the R matrix of the current LQR tracking model, and determine the adjustment strategy of the trajectory tracking control parameters according to the parameter settings, and then execute the adjustment strategy.

[0082] In an embodiment of the present application, when determining the simulation result, the trajectory deviation of the vehicle can be obtained, wherein the trajectory deviation refers to the trajectory deviation between the preset driving trajectory of the vehicle and the simulated driving trajectory of the vehicle.

[0083] Furthermore, by comparing the trajectory deviation with the first threshold, it is determined whether the trajectory tracking performance of the current vehicle meets the use requirements. Exemplarily, when the trajectory deviation is greater than or equal to the first threshold, it indicates that the trajectory tracking performance of the current vehicle is poor and cannot follow the set reference driving trajectory well. Therefore, the adjustment strategy of the current trajectory tracking control parameters can be determined to optimize the Q matrix and the R matrix, and after executing the corresponding adjustment strategy, that is, the Q matrix and the R matrix, the simulation is performed again until the trajectory deviation is less than the first threshold. For example, if the vehicle's response to the trajectory is too slow, the weight of the state error in the Q matrix can be increased; if the vehicle's trajectory tracking control is too intense, the weight of the control input in the R matrix can be increased.

[0084] Furthermore, when the trajectory deviation is less than the first threshold, it indicates that the trajectory tracking performance of the current vehicle is good, and then the parameter settings of the Q matrix and the R matrix of the current LQR tracking model are determined, and the adjustment strategy of the trajectory tracking control parameters is determined to be no need for adjustment based on this set of parameter settings, and this set of parameter settings and the corresponding vehicle preset driving trajectory and vehicle application scenarios can be further recorded in the vehicle processor for quick call during actual use.

[0085] It is understandable that the present application does not limit the specific value of the first threshold, and it can be flexibly selected according to actual needs.

[0086] In practical applications, simulation results can also include indicators such as control smoothness and control input amplitude. Control input smoothness refers to analyzing the rate of change of steering angle and acceleration. Too drastic control will affect the stability and comfort of the vehicle. Control input amplitude refers to whether the size of the control input exceeds a reasonable range. If the control input is too large, it may put pressure on the vehicle hardware.

[0087] Specifically, in a possible implementation manner, the trajectory tracking control parameter adjustment method provided in the embodiment of the present application further includes:

[0088] In response to the triggered update instruction, the first threshold is updated.

[0089] It is understandable that the first threshold is not unchangeable after being preset, but the first threshold can be updated in real time by responding to the triggered update instruction, thereby better meeting the user's usage needs.

[0090] It should be noted that the update instruction can be actively triggered by the user or sent by the server 120 to the vehicle processor 110, and this application does not make any specific limitation on this.

[0091] In an embodiment of the present application, update instructions may include different update instructions such as voice update instructions, text input update instructions entered through an input box, touch update instructions triggered by touch screen operation, selection update instructions triggered by drop-down menu selection, and update instructions selected by checking a check box, but are not limited to the above content.

[0092] Specifically, in a possible implementation, the update instruction is a voice update instruction; in response to the triggered update instruction, updating the first threshold includes:

[0093] In response to the triggered voice update instruction, voice data is acquired, voice recognition is performed on the voice data, and the first threshold is updated according to the result of the voice recognition.

[0094] It is understandable that the voice data can be acquired through a microphone mounted on the vehicle or through a microphone of a user's smartphone terminal and sent to the vehicle processor 110. Furthermore, when performing voice recognition on the voice data, it can be recognized by a pre-trained voice recognition model. The voice recognition model can be obtained by training with voice data samples with voice recognition result label information.

[0095] That is to say, after the speech recognition result is determined in advance, a combination of a speech data sample and the corresponding speech recognition result label can be used as a training sample, thereby obtaining multiple training samples.

[0096] After obtaining multiple training samples, the multiple training samples are input into the speech recognition model in sequence, that is, the speech data sample and the speech recognition result label in each training sample are input into the speech recognition model at the same time, and the model parameters in the speech recognition model are adjusted according to each output result of the speech recognition model, and finally the pre-training process of the speech recognition model is completed.

[0097] The pre-training process of the speech recognition model may be considered completed after reaching a preset number of pre-training times; or the pre-training process of the speech recognition model may be considered completed when the training output results of the speech recognition model converge.

[0098] Finally, the first threshold is updated accordingly according to the result of the speech recognition.

[0099] Specifically, in a possible implementation, the update instruction is a text input update instruction; in response to the triggered update instruction, updating the first threshold includes:

[0100] In response to the triggered text input update instruction, text input data is acquired, text recognition is performed on the text input data, and the first threshold is updated according to the result of the text recognition.

[0101] It is understandable that the text input data can be obtained through the input box on the vehicle display screen, or through the input box of the application on the smartphone terminal used by the user, or can be automatically obtained through flexible settings based on the actual usage environment requirements.

[0102] Furthermore, after receiving the text input data from the user, when performing text recognition on the text input data, the recognition can be performed using a pre-trained neural network model, and the first threshold can be updated according to the result of the text recognition.

[0103] Below, in combination with the specific application implementation process, the trajectory tracking control parameter adjustment method provided in this application is described in detail and explained:

[0104] As an optional implementation, please refer to Figure 5 , Figure 5 It is a flow chart of multiple simulations of trajectory tracking control parameters provided in an embodiment of the present application.

[0105] First, the preset driving trajectory of the vehicle is determined as a reference trajectory, which is the path that the vehicle should track as accurately as possible, and then the LQR tracking model and the vehicle dynamic model are further constructed. The vehicle dynamic model includes the vehicle's kinematic model and dynamic model.

[0106] Furthermore, the Q matrix and the R matrix of the LQR tracking model are initialized, and according to the vehicle dynamic model and the LQR tracking model, the driving trajectory of the vehicle is simulated and tracked through a hardware-in-the-loop simulation method to obtain a simulated driving trajectory of the vehicle.

[0107] Furthermore, by comparing the preset driving trajectory of the vehicle with the simulated driving trajectory of the vehicle, indicators such as the vehicle's trajectory deviation, control smoothness, and control input amplitude are determined as simulation results and used to evaluate the vehicle's trajectory tracking performance.

[0108] It can be understood that by comparing the pre-set threshold with the performance index, it can be determined whether the performance evaluation of the vehicle meets the usage requirements. If it does not meet the conditions, the Q matrix and R matrix can be optimized and adjusted according to the abnormal direction of the performance index, and simulation and performance evaluation can be performed again until the vehicle's trajectory tracking performance meets the usage requirements, and the vehicle trajectory tracking control parameters are adjusted.

[0109] See also Figure 6 , Figure 6 : is a structural diagram of a trajectory tracking control parameter adjustment device provided in an embodiment of the present application, which can implement the above-mentioned trajectory tracking control parameter adjustment method. The trajectory tracking control parameter adjustment device 600 includes:

[0110] The trajectory determination module 610 is used to determine a preset driving trajectory of the vehicle;

[0111] A model building module 620, for building an LQR tracking model;

[0112] A simulation operation module 630, configured to perform simulation according to the preset vehicle driving trajectory and the LQR tracking model, and then determine a simulation result;

[0113] The parameter adjustment module 640 is used to determine an adjustment strategy for trajectory tracking control parameters according to the simulation results, and then execute the adjustment strategy.

[0114] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0115] Please refer to Figure 7 , Figure 7 The hardware structure of the electronic device provided in the embodiment of the present application is illustrated, and the electronic device includes:

[0116] The processor 701 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0117] The memory 702 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 can store an operating system and other applications. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 702, and the processor 701 calls and executes the trajectory tracking control parameter adjustment method of the embodiment of this application;

[0118] Input / output interface 703, used to implement information input and output;

[0119] Communication interface 704, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0120] A bus 705 that transmits information between the various components of the device (e.g., the processor 701, the memory 702, the input / output interface 703, and the communication interface 704);

[0121] The processor 701 , the memory 702 , the input / output interface 703 and the communication interface 704 are connected to each other in communication within the device via a bus 705 .

[0122] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned trajectory tracking control parameter adjustment method is implemented.

[0123] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0124] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0125] Specifically, in a possible implementation, an embodiment of the present application further provides a vehicle, which includes the above-mentioned trajectory tracking control parameter adjustment device or the above-mentioned electronic device.

[0126] Specifically, the vehicle may be a private car, such as a sedan, a multi-purpose vehicle, a sports utility vehicle, or a pickup truck, etc. The vehicle may also be an operating vehicle, such as a van, a bus, a small truck, or a large trailer, etc. The vehicle may be a gasoline vehicle or a new energy vehicle. When the vehicle is a new energy vehicle, it may be a hybrid vehicle or a pure electric vehicle.

[0127] The embodiments of the present application provide a trajectory tracking control parameter adjustment method, device, equipment, medium and vehicle, which adjust the trajectory tracking control parameters through virtual simulation, so that the LQR controller of the vehicle can adapt to different road conditions, improve the performance, robustness and efficiency of the path tracking of the autonomous driving vehicle, and effectively meet the user's usage needs.

[0128] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0129] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0130] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0131] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0132] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0133] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0134] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0135] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0137] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0138] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A trajectory tracking control parameter adjustment method, characterized in that: The method comprises the following steps: Determine a preset driving trajectory of the vehicle; Construct LQR tracking model; Perform simulation according to the preset vehicle driving trajectory and the LQR tracking model, and then determine the simulation result; According to the simulation results, an adjustment strategy for trajectory tracking control parameters is determined, and then the adjustment strategy is executed.

2. The trajectory tracking control parameter adjustment method according to claim 1, characterized in that: The simulation method is a hardware-in-the-loop simulation method; the simulation is performed according to the preset vehicle driving trajectory and the LQR tracking model, and then the simulation result is determined, including: Constructing a vehicle dynamic model; the vehicle dynamic model includes a kinematic model and a dynamic model; Initializing the Q matrix and R matrix of the LQR tracking model; According to the vehicle dynamic model and the LQR tracking model, the driving trajectory of the vehicle is simulated by the hardware-in-the-loop simulation method to obtain a simulated driving trajectory of the vehicle; The preset driving trajectory of the vehicle is compared with the simulated driving trajectory of the vehicle to determine a simulation result.

3. The trajectory tracking control parameter adjustment method according to claim 1, characterized in that: The simulation result includes a trajectory deviation; determining an adjustment strategy for a trajectory tracking control parameter according to the simulation result, and then executing the adjustment strategy, including: Determining a trajectory deviation between a preset driving trajectory of the vehicle and a simulated driving trajectory of the vehicle; When the trajectory deviation is greater than or equal to a first threshold, determining that the adjustment strategy of the trajectory tracking control parameter is to optimize the Q matrix and the R matrix, and after optimizing the Q matrix and the R matrix, performing simulation again until the trajectory deviation is less than the first threshold; When the trajectory deviation is less than the first threshold, the parameter settings of the Q matrix and the R matrix of the current LQR tracking model are determined, and an adjustment strategy for trajectory tracking control parameters is determined according to the parameter settings, and then the adjustment strategy is executed.

4. The trajectory tracking control parameter adjustment method according to claim 3, characterized in that: The method further comprises: In response to the triggered update instruction, the first threshold is updated.

5. The trajectory tracking control parameter adjustment method according to claim 4, characterized in that: The updating instruction includes a voice updating instruction; the updating of the first threshold in response to the triggered updating instruction includes: In response to the triggered voice update instruction, voice data is acquired, voice recognition is performed on the voice data, and the first threshold is updated according to the result of the voice recognition.

6. The trajectory tracking control parameter adjustment method according to claim 4, characterized in that: The update instruction includes a text input update instruction; the updating of the first threshold in response to the triggered update instruction includes: In response to the triggered text input update instruction, text input data is acquired, text recognition is performed on the text input data, and the first threshold is updated according to the result of the text recognition.

7. A trajectory tracking control parameter adjustment device, characterized in that: The device comprises: A trajectory determination module, used to determine a preset driving trajectory of the vehicle; Model building module, used to build LQR tracking model; A simulation operation module, used to perform simulation according to the preset vehicle driving trajectory and the LQR tracking model, and then determine the simulation result; The parameter adjustment module is used to determine the adjustment strategy of the trajectory tracking control parameters according to the simulation results, and then execute the adjustment strategy.

8. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the trajectory tracking control parameter adjustment method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the trajectory tracking control parameter adjustment method according to any one of claims 1 to 6 is implemented.

10. A vehicle, characterized in that: The vehicle includes the trajectory tracking control parameter adjustment device as claimed in claim 7 or the electronic device as claimed in claim 8.

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