Calibration method and device for vehicle autonomous lane-changing parameter, electronic equipment and medium

Abstract

The application relates to a vehicle autonomous lane-changing parameter calibration method and device, electronic equipment and a medium, comprising: acquiring yaw stability data of a to-be-tested vehicle in a pre-constructed real vehicle test scene, and using a preset longitudinal and lateral drive-by-wire system to jointly debug the to-be-tested vehicle, performing simulation testing on the to-be-tested vehicle by using initial autonomous lane-changing parameters to obtain yaw test data, comparing and analyzing the yaw stability data and the yaw test data to obtain a stability gradient parameter, then adjusting the initial autonomous lane-changing parameters to obtain new autonomous lane-changing parameters and perform simulation testing on the to-be-tested vehicle until a preset autonomous lane-changing condition is met, and the optimal autonomous lane-changing parameters are obtained. Therefore, the problems of a complex vehicle lane-changing process and the inability to analyze vehicle yaw stability are solved, the vehicle yaw stability in the lane-changing process is effectively analyzed by combining vehicle simulation and in-loop testing, thereby saving test calibration time cost and improving function development efficiency.

Description

Calibration methods, devices, electronic equipment, and media for autonomous lane-changing parameters of vehicles. Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, apparatus, electronic device and medium for calibrating autonomous lane-changing parameters of a vehicle. Background Technology

[0002] With the in-depth research on vehicle-assisted driving and autonomous driving technologies, it has become possible for vehicles to autonomously change lanes on highways to avoid safety hazards caused by traffic congestion.

[0003] In related technologies, automatic lane changing behavior that meets certain conditions has been realized, such as lane changing with a lever.

[0004] However, using a lane change lever requires manual input of the lane change command, which is then executed by the vehicle. The operation is relatively complex, and the driver needs to monitor the driving environment in real time and be prepared to take over the vehicle at any time. In addition, the vehicle's yaw stability cannot be analyzed during the lane change process, which reduces the driver's driving experience and urgently needs to be addressed. Summary of the Invention

[0005] This application provides a method, device, electronic device, and medium for calibrating vehicle autonomous lane-changing parameters to solve problems such as the complexity of the vehicle lane-changing process and the inability to analyze vehicle yaw stability.

[0006] The first aspect of this application provides a method for calibrating autonomous lane-changing parameters for vehicles, comprising the following steps:

[0007] Obtain yaw stability data of the vehicle under test in a pre-constructed real-vehicle test scenario;

[0008] The vehicle under test is integrated with a preset lateral and longitudinal drive-by-wire system, and simulated using initial autonomous lane-changing parameters based on a pre-constructed simulation test scenario to obtain yaw test data; and

[0009] By comparing and analyzing the yaw stability data and the yaw test data, stability gradient parameters are obtained. The initial autonomous lane-changing parameters are then adjusted based on the stability gradient parameters to obtain new autonomous lane-changing parameters. The new autonomous lane-changing parameters are then used to perform simulation tests on the vehicle under test until the preset autonomous lane-changing conditions are met, thus obtaining the optimal autonomous lane-changing parameters.

[0010] According to one embodiment of this application, the initial autonomous lane-changing parameters are adjusted based on the stability gradient parameters to obtain the new autonomous lane-changing parameters, and the vehicle under test is simulated again using the new autonomous lane-changing parameters until the preset autonomous lane-changing conditions are met, thereby obtaining the optimal autonomous lane-changing parameters, including:

[0011] Determine whether the new autonomous lane-changing parameters meet the preset autonomous lane-changing conditions;

[0012] When the new autonomous lane-changing parameters meet the preset autonomous lane-changing conditions, the optimal autonomous lane-changing parameters are obtained; otherwise, the simulation test is repeated on the vehicle under test until the preset autonomous lane-changing conditions are met.

[0013] According to one embodiment of this application, before obtaining the yaw stability data of the vehicle under test in the pre-built real-vehicle test scenario, the method further includes:

[0014] The simulation test scenario for the vehicle under test is constructed based on a modular approach and 3D scene.

[0015] A real-vehicle test scenario for the vehicle under test is constructed based on preset standards.

[0016] According to one embodiment of this application, the vehicle under test is coordinated using a preset lateral and longitudinal drive-by-wire system, and based on a pre-constructed simulation test scenario, the vehicle under test is simulated using initial autonomous lane-changing parameters to obtain yaw test data, including:

[0017] The longitudinal acceleration and deceleration responses of the drive-by-wire system of the vehicle under test are debugged, and longitudinal debugging results are generated.

[0018] The lateral response speed and accuracy of the steering system of the vehicle under test are adjusted, and lateral adjustment results are generated.

[0019] Based on the longitudinal debugging results, the lateral debugging results, and the initial autonomous lane-changing parameters, the vehicle under test is controlled to conduct simulation tests to obtain yaw test data.

[0020] According to one embodiment of this application, the vehicle under test is controlled to perform simulation testing based on the longitudinal adjustment results, the lateral adjustment results, and the initial autonomous lane-changing parameters to obtain yaw test data, including:

[0021] Analyze the yaw test data of the vehicle under test in a pre-constructed real vehicle test scenario;

[0022] Fit the yaw test data and analyze the curvature of the lane-changing trajectory of the vehicle under test, and output the running results of the vehicle under test;

[0023] Based on the running results, the stability gradient parameters of the vehicle under test are output.

[0024] According to the vehicle autonomous lane-changing parameter calibration method of this application, yaw stability data of the vehicle under test in a pre-constructed real-vehicle test scenario is obtained. The vehicle under test is then integrated with a preset lateral and longitudinal drive-by-wire system. Simulation tests are performed on the vehicle under test using initial autonomous lane-changing parameters to obtain yaw test data. The stability gradient parameters are obtained by comparing and analyzing the yaw stability data and the yaw test data. The initial autonomous lane-changing parameters are then adjusted to obtain new autonomous lane-changing parameters, and simulation tests are performed on the vehicle under test until preset autonomous lane-changing conditions are met, thus obtaining the optimal autonomous lane-changing parameters. This solves the problems of complex vehicle lane-changing processes and the inability to analyze vehicle yaw stability. By combining vehicle simulation and in-loop testing, yaw stability analysis of the vehicle during lane-changing is effectively achieved, thereby saving test calibration time and improving functional development efficiency.

[0025] A second aspect of this application provides a calibration device for autonomous lane-changing parameters of a vehicle, comprising:

[0026] The acquisition module is used to acquire yaw stability data of the vehicle under test in a pre-built real vehicle test scenario;

[0027] The testing module is used to coordinate the test vehicle using a preset lateral and longitudinal drive-by-wire system, and to perform simulation tests on the test vehicle based on a pre-constructed simulation test scenario using initial autonomous lane-changing parameters to obtain yaw test data; and

[0028] The analysis module is used to compare and analyze the yaw stability data and the yaw test data to obtain stability gradient parameters, and adjust the initial autonomous lane-changing parameters according to the stability gradient parameters to obtain new autonomous lane-changing parameters. The new autonomous lane-changing parameters are then used to perform simulation tests on the vehicle under test until the preset autonomous lane-changing conditions are met, thus obtaining the optimal autonomous lane-changing parameters.

[0029] According to one embodiment of this application, the analysis module is specifically used for:

[0030] Determine whether the new autonomous lane-changing parameters meet the preset autonomous lane-changing conditions;

[0031] When the new autonomous lane-changing parameters meet the preset autonomous lane-changing conditions, the optimal autonomous lane-changing parameters are obtained; otherwise, the simulation test is repeated on the vehicle under test until the preset autonomous lane-changing conditions are met.

[0032] According to one embodiment of this application, before acquiring the yaw stability data of the vehicle under test in the pre-built real-vehicle test scenario, the acquisition module is further configured to:

[0033] The simulation test scenario for the vehicle under test is constructed based on a modular approach and 3D scene.

[0034] A real-vehicle test scenario for the vehicle under test is constructed based on preset standards.

[0035] According to one embodiment of this application, the testing module is specifically used for:

[0036] The longitudinal acceleration and deceleration responses of the drive-by-wire system of the vehicle under test are debugged, and longitudinal debugging results are generated.

[0037] The lateral response speed and accuracy of the steering system of the vehicle under test are adjusted, and lateral adjustment results are generated.

[0038] Based on the longitudinal debugging results, the lateral debugging results, and the initial autonomous lane-changing parameters, the vehicle under test is controlled to conduct simulation tests to obtain yaw test data.

[0039] According to one embodiment of this application, the testing module is specifically used for:

[0040] Analyze the yaw test data of the vehicle under test in a pre-constructed real vehicle test scenario;

[0041] Fit the yaw test data and analyze the curvature of the lane-changing trajectory of the vehicle under test, and output the running results of the vehicle under test;

[0042] Based on the running results, the stability gradient parameters of the vehicle under test are output.

[0043] According to the vehicle autonomous lane-changing parameter calibration device of this application embodiment, yaw stability data of the vehicle under test in a pre-constructed real vehicle test scenario is acquired. The vehicle under test is then integrated with a preset lateral and longitudinal drive-by-wire system. Simulation testing of the vehicle under test is performed using initial autonomous lane-changing parameters to obtain yaw test data. The stability gradient parameters are obtained by comparing and analyzing the yaw stability data and the yaw test data. The initial autonomous lane-changing parameters are then adjusted to obtain new autonomous lane-changing parameters, and simulation testing of the vehicle under test is performed until preset autonomous lane-changing conditions are met, thus obtaining the optimal autonomous lane-changing parameters. This solves the problems of complex vehicle lane-changing processes and the inability to analyze vehicle yaw stability. By combining vehicle simulation and in-loop testing, yaw stability analysis of the vehicle during lane-changing is effectively achieved, thereby saving test calibration time and improving functional development efficiency.

[0044] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle autonomous lane-changing parameter calibration method as described in the above embodiments.

[0045] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the vehicle autonomous lane-changing parameter calibration method as described in the above embodiments.

[0046] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0047] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0048] Figure 1 is a flowchart of a method for calibrating vehicle autonomous lane-changing parameters according to an embodiment of this application;

[0049] Figure 2 is a flowchart of a method for analyzing the yaw stability of vehicles automatically changing lanes on different road surfaces according to an embodiment of this application;

[0050] Figure 3 is a schematic diagram of a control model for vehicle testing according to an embodiment of this application;

[0051] Figure 4 is a schematic diagram of a vehicle trajectory according to an embodiment of this application;

[0052] Figure 5 is a schematic diagram of different road surface stability analyses according to an embodiment of this application;

[0053] Figure 6 is an example diagram of a calibration device for vehicle autonomous lane-changing parameters according to an embodiment of this application;

[0054] Figure 7 is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0055] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0056] The following describes a method, apparatus, electronic device, and medium for calibrating autonomous lane-changing parameters of a vehicle according to embodiments of this application, with reference to the accompanying drawings. Addressing the issues of complex lane-changing processes and the inability to analyze vehicle yaw stability mentioned in the background art, this application provides a method for calibrating autonomous lane-changing parameters. In this method, yaw stability data of the vehicle under test in a pre-constructed real-vehicle test scenario is acquired. The vehicle under test is then integrated with a preset lateral and longitudinal drive-by-wire system. Simulation tests are performed on the vehicle under test using initial autonomous lane-changing parameters to obtain yaw test data. The yaw stability data and yaw test data are compared and analyzed to obtain stability gradient parameters. The initial autonomous lane-changing parameters are then adjusted to obtain new autonomous lane-changing parameters, and simulation tests are performed on the vehicle under test until preset autonomous lane-changing conditions are met, resulting in optimal autonomous lane-changing parameters. This solves the problems of complex lane-changing processes and the inability to analyze vehicle yaw stability. By combining vehicle simulation and in-loop testing, yaw stability analysis of the vehicle during lane-changing is effectively achieved, thereby saving test calibration time and improving functional development efficiency.

[0057] Specifically, before introducing the embodiments of this application, we will first introduce the relevant application modules and test conditions required for the test vehicle of this application embodiment, including the drive-by-wire chassis, front intelligent forward-looking module, four-corner millimeter-wave radar, front millimeter-wave radar and other perception and target recognition systems; the intelligent driving system and the drive-by-wire system have completed joint debugging and meet the response requirements; the simulation system needs to have data acquisition and analysis capabilities, and the input and output of each module must be normal. Among them, data acquisition and analysis requires CAN (Controller Area Network) analysis tools, and the software must have the ability to process information from each system.

[0058] Specifically, Figure 1 is a flowchart illustrating a method for calibrating autonomous lane-changing parameters of a vehicle according to an embodiment of this application.

[0059] As shown in Figure 1, the calibration method for the vehicle's autonomous lane-changing parameters includes the following steps:

[0060] In step S101, the yaw stability data of the vehicle under test in a pre-constructed real vehicle test scenario is obtained.

[0061] Furthermore, in some embodiments, before obtaining the yaw stability data of the vehicle under test in a pre-constructed real vehicle test scenario, the method further includes: constructing a simulation test scenario of the vehicle under test based on a modular approach and a 3D scene; and constructing a real vehicle test scenario of the vehicle under test based on preset standards.

[0062] Specifically, as shown in Figure 2, before conducting real-vehicle testing on the vehicle under test, this embodiment of the application first needs to build a real-vehicle test scenario based on highway standards, including a 3-lane road model and double lane change points marked with cones; secondly, a simulation environment for the double lane change scenario of the vehicle under test is built, mainly based on a modular approach and 3D scene construction to create a simulation test scenario for the vehicle under test; finally, based on the built simulation test scenario and real-vehicle test scenario, the yaw stability data of the vehicle under test is obtained.

[0063] As shown in Figure 3, the construction of the dual lane change simulation scenario for the vehicle under test mainly includes building a model of the vehicle under test, such as a simulation vehicle model and a real vehicle interface model, a driver model, an autonomous steering planning model, a vehicle lateral and longitudinal control model, a data acquisition module, and a yaw stability calculation module.

[0064] In step S102, the vehicle under test is tested by using the preset lateral and longitudinal drive control system, and the vehicle under test is simulated based on the pre-constructed simulation test scenario and the initial autonomous lane-changing parameters are used to obtain yaw test data.

[0065] Furthermore, in some embodiments, the vehicle under test is integrated with a preset longitudinal and lateral drive-by-wire system, and based on a pre-constructed simulation test scenario, the vehicle under test is simulated using initial autonomous lane-changing parameters to obtain yaw test data. This includes: adjusting the longitudinal acceleration and deceleration responses of the vehicle under test's drive-by-wire system and generating longitudinal adjustment results; adjusting the lateral response speed and accuracy of the vehicle under test's steering system and generating lateral adjustment results; and controlling the vehicle under test to perform simulation tests based on the longitudinal adjustment results, lateral adjustment results, and initial autonomous lane-changing parameters to obtain yaw test data.

[0066] Furthermore, in some embodiments, the vehicle under test is controlled to perform simulation tests based on the longitudinal debugging results, the lateral debugging results, and the initial autonomous lane-changing parameters to obtain yaw test data, including: analyzing the yaw test data of the vehicle under test in a pre-constructed real vehicle test scenario; fitting the yaw test data and analyzing the curvature of the lane-changing trajectory of the vehicle under test, and outputting the running results of the vehicle under test; and outputting the stability gradient parameters of the vehicle under test based on the running results.

[0067] The preset horizontal and vertical wire control system can be a test system set by those skilled in the art or a test system obtained through computer simulation, and no specific limitation is made here.

[0068] Specifically, in this embodiment of the application, after setting up a simulation test scenario for the vehicle under test, it is necessary to debug the longitudinal acceleration and deceleration response of the vehicle under test through the longitudinal drive-by-wire system, and adjust the control interface threshold of the response time by adjusting the algorithm model to make it have a certain stability, and finally generate the longitudinal debugging result; at the same time, it is also necessary to debug the lateral response speed and accuracy of the vehicle under test through the lateral drive-by-wire system, and finally generate the lateral debugging result.

[0069] Specifically, in this embodiment, the vehicle under test is controlled to perform simulation tests based on the longitudinal debugging results, the lateral debugging results, and the initial autonomous lane-changing parameters, so that the acceleration, deceleration, and steering requirements of the intelligent driving system meet the functional specifications, and finally the yaw test data is obtained.

[0070] Specifically, this application embodiment requires yaw stability analysis of the test vehicle under both real-vehicle test scenarios and simulation test scenarios. First, the basic values ​​of relevant control parameters are initially determined through simulation testing; second, the vehicle is controlled by the driver to conduct a double lane change test, and then the yaw stability parameters with better comfort are recorded as a reference based on the driver's subjective evaluation; finally, the vehicle is controlled by the control algorithm to conduct the test, and the yaw test data is recorded.

[0071] Further, as shown in Figure 4, after obtaining the yaw test data, this embodiment of the application analyzes the yaw test data of the vehicle under test in both the simulation test scenario and the real vehicle test scenario. Then, it fits the yaw test data and analyzes the curvature of the lane-changing trajectory of the vehicle under test, analyzes the driving trajectory of the vehicle under test, and outputs the running results of the vehicle under test. Based on the running results, it analyzes the yaw stability of the vehicle under test, thereby outputting the stability gradient parameters of the vehicle under test.

[0072] In step S103, the stability gradient parameters are obtained by comparing and analyzing the yaw stability data and the yaw test data. The initial autonomous lane-changing parameters are then adjusted according to the stability gradient parameters to obtain new autonomous lane-changing parameters. The new autonomous lane-changing parameters are then used to perform simulation tests on the vehicle under test until the preset autonomous lane-changing conditions are met, thus obtaining the optimal autonomous lane-changing parameters.

[0073] Furthermore, in some embodiments, the initial autonomous lane-changing parameters are adjusted according to the stability gradient parameters to obtain new autonomous lane-changing parameters, and the vehicle under test is simulated again using the new autonomous lane-changing parameters until the preset autonomous lane-changing conditions are met to obtain the optimal autonomous lane-changing parameters. This includes: determining whether the new autonomous lane-changing parameters meet the preset autonomous lane-changing conditions; if the new autonomous lane-changing parameters meet the preset autonomous lane-changing conditions, the optimal autonomous lane-changing parameters are obtained; otherwise, the vehicle under test is simulated again until the preset autonomous lane-changing conditions are met.

[0074] The preset autonomous lane-changing conditions can be lane-changing conditions set by those skilled in the art or lane-changing conditions obtained through computer simulation, and no specific limitation is made here.

[0075] Specifically, in the embodiments of this application, based on the stability gradient parameters obtained from the analysis of yaw stability data and yaw test data, the initial autonomous lane-changing parameters are adjusted according to the stability gradient parameters to obtain new autonomous lane-changing parameters, and then the optimal path and speed planning is generated according to the new autonomous lane-changing parameters.

[0076] Specifically, as shown in Figure 5, after obtaining new autonomous lane-changing parameters, this embodiment of the application needs to determine whether the new autonomous lane-changing parameters meet the preset autonomous lane-changing conditions, that is, to determine the stability of the new autonomous lane-changing parameters under different road conditions. If the new autonomous lane-changing parameters meet the preset autonomous lane-changing conditions, the optimal autonomous lane-changing parameters are obtained, and the vehicle under test performs a lane-changing action according to the optimal autonomous lane-changing parameters. If the new autonomous lane-changing parameters do not meet the preset autonomous lane-changing conditions, the vehicle under test is re-simulated until autonomous lane-changing parameters that meet the preset autonomous lane-changing conditions are obtained, and the lane-changing action is finally completed. This improves the effect of yaw stability analysis of the vehicle during autonomous lane-changing and increases the efficiency of test calibration.

[0077] According to the vehicle autonomous lane-changing parameter calibration method of this application, yaw stability data of the vehicle under test in a pre-constructed real-vehicle test scenario is obtained. The vehicle under test is then integrated with a preset lateral and longitudinal drive-by-wire system. Simulation tests are performed on the vehicle under test using initial autonomous lane-changing parameters to obtain yaw test data. The stability gradient parameters are obtained by comparing and analyzing the yaw stability data and the yaw test data. The initial autonomous lane-changing parameters are then adjusted to obtain new autonomous lane-changing parameters, and simulation tests are performed on the vehicle under test until preset autonomous lane-changing conditions are met, thus obtaining the optimal autonomous lane-changing parameters. This solves the problems of complex vehicle lane-changing processes and the inability to analyze vehicle yaw stability. By combining vehicle simulation and in-loop testing, yaw stability analysis of the vehicle during lane-changing is effectively achieved, thereby saving test calibration time and improving functional development efficiency.

[0078] Next, referring to the accompanying drawings, a calibration device for vehicle autonomous lane-changing parameters proposed according to an embodiment of this application is described.

[0079] Figure 6 is a block diagram of a vehicle autonomous lane-changing parameter calibration device according to an embodiment of this application.

[0080] As shown in Figure 6, the calibration device 10 for the autonomous lane-changing parameters of the vehicle includes: an acquisition module 100, a testing module 200, and an analysis module 300.

[0081] Among them, the acquisition module 100 is used to acquire the yaw stability data of the vehicle under test in a pre-constructed real vehicle test scenario;

[0082] Test module 200 is used to coordinate the test vehicle using a preset lateral and longitudinal drive-by-wire system, and to perform simulation tests on the test vehicle using initial autonomous lane-changing parameters based on a pre-constructed simulation test scenario, thereby obtaining yaw test data; and

[0083] The analysis module 300 is used to compare and analyze the yaw stability data and yaw test data to obtain the stability gradient parameters, and adjust the initial autonomous lane-changing parameters according to the stability gradient parameters to obtain new autonomous lane-changing parameters. The new autonomous lane-changing parameters are then used to conduct simulation tests on the vehicle under test until the preset autonomous lane-changing conditions are met, thus obtaining the optimal autonomous lane-changing parameters.

[0084] Furthermore, in some embodiments, the analysis module 300 is specifically used for:

[0085] Determine whether the new autonomous lane-changing parameters meet the preset autonomous lane-changing conditions;

[0086] When the new autonomous lane-changing parameters meet the preset autonomous lane-changing conditions, the optimal autonomous lane-changing parameters are obtained; otherwise, the simulation test on the vehicle under test is repeated until the preset autonomous lane-changing conditions are met.

[0087] Furthermore, in some embodiments, before acquiring the yaw stability data of the vehicle under test in a pre-constructed real-vehicle test scenario, the acquisition module 100 is also used to:

[0088] The simulation test scenario for the vehicle under test is constructed based on a modular approach and 3D scene construction.

[0089] A real-vehicle test scenario is constructed based on preset standards for the vehicle under test.

[0090] Furthermore, in some embodiments, the test module 200 is specifically used for:

[0091] Debug the longitudinal acceleration and deceleration response of the drive-by-wire system of the vehicle under test, and generate longitudinal debugging results;

[0092] The lateral response speed and accuracy of the steering system of the vehicle under test are adjusted, and lateral adjustment results are generated.

[0093] Based on the longitudinal and lateral debugging results and the initial autonomous lane-changing parameters, the vehicle under test was controlled to conduct simulation tests and obtain yaw test data.

[0094] Furthermore, in some embodiments, the test module 200 is specifically used for:

[0095] Analyze the yaw test data of the vehicle under test in a pre-constructed real vehicle test scenario;

[0096] Fit the yaw test data and analyze the curvature of the lane-changing trajectory of the vehicle under test, and output the running results of the vehicle under test;

[0097] The stability gradient parameters of the vehicle under test are output based on the running results.

[0098] According to the vehicle autonomous lane-changing parameter calibration device of this application embodiment, yaw stability data of the vehicle under test in a pre-constructed real vehicle test scenario is acquired. The vehicle under test is then integrated with a preset lateral and longitudinal drive-by-wire system. Simulation testing of the vehicle under test is performed using initial autonomous lane-changing parameters to obtain yaw test data. The stability gradient parameters are obtained by comparing and analyzing the yaw stability data and the yaw test data. The initial autonomous lane-changing parameters are then adjusted to obtain new autonomous lane-changing parameters, and simulation testing of the vehicle under test is performed until preset autonomous lane-changing conditions are met, thus obtaining the optimal autonomous lane-changing parameters. This solves the problems of complex vehicle lane-changing processes and the inability to analyze vehicle yaw stability. By combining vehicle simulation and in-loop testing, yaw stability analysis of the vehicle during lane-changing is effectively achieved, thereby saving test calibration time and improving functional development efficiency.

[0099] Figure 7 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0100] The memory 701, the processor 702, and the computer program stored on the memory 701 and executable on the processor 702.

[0101] When the processor 702 executes the program, it implements the calibration method for autonomous lane-changing parameters of the vehicle provided in the above embodiments.

[0102] Furthermore, electronic devices also include:

[0103] Communication interface 703 is used for communication between memory 701 and processor 702.

[0104] The memory 701 is used to store computer programs that can run on the processor 702.

[0105] The memory 701 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0106] If the memory 701, processor 702, and communication interface 703 are implemented independently, they can be interconnected via a bus to communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one thick line is used in Figure 7, but this does not indicate that there is only one bus or one type of bus.

[0107] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0108] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0109] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for calibrating autonomous lane-changing parameters for vehicles.

[0110] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0111] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0112] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0113] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0114] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0115] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.

[0116] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0117] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for calibrating autonomous lane-changing parameters for vehicles, characterized in that, Includes the following steps: Obtain yaw stability data of the vehicle under test in a pre-constructed real-vehicle test scenario; The vehicle under test is tested by using a pre-set lateral and longitudinal drive control system, and by using initial autonomous lane-changing parameters based on a pre-built simulation test scenario to obtain yaw test data. The process involves comparing and analyzing the yaw stability data and the yaw test data to obtain stability gradient parameters. Based on these parameters, the initial autonomous lane-changing parameters are adjusted to obtain new autonomous lane-changing parameters. These new parameters are then used to re-simulate the vehicle under test until preset autonomous lane-changing conditions are met, resulting in optimal autonomous lane-changing parameters. Specifically, the vehicle under test is integrated with preset longitudinal and lateral steerable systems. Based on a pre-constructed simulation test scenario, the initial autonomous lane-changing parameters are used to simulate the vehicle under test, obtaining yaw test data. This includes: adjusting the longitudinal acceleration and deceleration responses of the vehicle under test's steerable system and generating longitudinal adjustment results; adjusting the lateral response speed and accuracy of the vehicle under test's steering system and generating lateral adjustment results; controlling the vehicle under test for simulation based on the longitudinal adjustment results, the lateral adjustment results, and the initial autonomous lane-changing parameters to obtain yaw test data; and controlling the vehicle under test for simulation based on the longitudinal adjustment results, the lateral adjustment results, and the initial autonomous lane-changing parameters. The test process involves obtaining yaw stability data for the vehicle under test, including: analyzing yaw stability data of the vehicle under test in a pre-constructed real-vehicle test scenario; fitting the yaw stability data in the real-vehicle test scenario and the yaw stability data in the simulation test scenario to obtain the curvature of the lane-changing trajectory of the vehicle under test in the real-vehicle test scenario and the curvature of the lane-changing trajectory of the vehicle under test in the simulation test scenario, and outputting the running results of the vehicle under test based on the curvature of the lane-changing trajectory of the vehicle under test in the real-vehicle test scenario and the curvature of the lane-changing trajectory of the vehicle under test in the simulation test scenario; outputting the stability gradient parameters of the vehicle under test based on the running results; constructing a real-vehicle test scenario based on highway standards, including a 3-lane road model and double lane-change points marked with cones; obtaining yaw stability data of the vehicle under test based on the constructed real-vehicle test scenario; before obtaining the yaw stability data of the vehicle under test in the pre-constructed real-vehicle test scenario, the process also includes: constructing a simulation test scenario of the vehicle under test based on a modular approach and a 3D scene; and constructing a real-vehicle test scenario of the vehicle under test based on preset standards.

2. The method according to claim 1, characterized in that, The initial autonomous lane-changing parameters are adjusted according to the stability gradient parameters to obtain the new autonomous lane-changing parameters. The vehicle under test is then simulated again using the new autonomous lane-changing parameters until the preset autonomous lane-changing conditions are met, thus obtaining the optimal autonomous lane-changing parameters. This includes: determining whether the new autonomous lane-changing parameters meet the preset autonomous lane-changing conditions; if the new autonomous lane-changing parameters meet the preset autonomous lane-changing conditions, the optimal autonomous lane-changing parameters are obtained; otherwise, the vehicle under test is simulated again until the preset autonomous lane-changing conditions are met.

3. A calibration device for autonomous lane-changing parameters of a vehicle, characterized in that, include: The acquisition module is used to acquire yaw stability data of the vehicle under test in a pre-built real vehicle test scenario; The testing module is used to coordinate the vehicle under test using a preset lateral and longitudinal drive control system, and to conduct simulation tests on the vehicle under test based on a pre-built simulation test scenario using initial autonomous lane-changing parameters to obtain yaw test data. The system includes an analysis module for comparing and analyzing the yaw stability data and the yaw test data to obtain stability gradient parameters, adjusting the initial autonomous lane-changing parameters based on the stability gradient parameters to obtain new autonomous lane-changing parameters, and re-simulating the vehicle under test using the new autonomous lane-changing parameters until the preset autonomous lane-changing conditions are met to obtain the optimal autonomous lane-changing parameters. Specifically, the testing module is used to: debug the longitudinal acceleration and deceleration responses of the vehicle's drive-by-wire system and generate longitudinal debugging results; debug the lateral response speed and accuracy of the vehicle's steering system and generate lateral debugging results; control the vehicle under test for simulation testing based on the longitudinal debugging results, the lateral debugging results, and the initial autonomous lane-changing parameters to obtain yaw test data; and specifically, the testing module is used to: analyze the yaw stability data of the vehicle under test in a pre-constructed real-vehicle test scenario; and fit the real-vehicle test data to the yaw test data. The yaw stability data in the test scenario and the yaw test data in the simulation test scenario are used to obtain the curvature of the lane-changing trajectory of the vehicle under test in the real vehicle test scenario and the curvature of the lane-changing trajectory of the vehicle under test in the simulation test scenario. Based on the curvature of the lane-changing trajectory of the vehicle under test in the real vehicle test scenario and the curvature of the lane-changing trajectory of the vehicle under test in the simulation test scenario, the running result of the vehicle under test is output. Based on the running result, the stability gradient parameters of the vehicle under test are output. A real vehicle test scenario is built based on highway standards, including a 3-lane road model and double lane change points marked with cones. The yaw stability data of the vehicle under test is obtained based on the built real vehicle test scenario. Before obtaining the yaw stability data of the vehicle under test in the pre-built real vehicle test scenario, the acquisition module is also used to: build a simulation test scenario of the vehicle under test based on a modular approach and a 3D scene; and build a real vehicle test scenario of the vehicle under test based on preset standards.

4. The apparatus according to claim 3, characterized in that, The analysis module is specifically used to: determine whether the new autonomous lane-changing parameters meet the preset autonomous lane-changing conditions; when the new autonomous lane-changing parameters meet the preset autonomous lane-changing conditions, obtain the optimal autonomous lane-changing parameters; otherwise, re-perform simulation testing on the vehicle under test until the preset autonomous lane-changing conditions are met.

5. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the vehicle autonomous lane-changing parameter calibration method as described in any one of claims 1-2.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the calibration method for autonomous lane-changing parameters of the vehicle as described in any one of claims 1-2.

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