CN116107252A - Calibration method and device for vehicle autonomous lane change parameters, electronic equipment and medium - Google Patents
Calibration method and device for vehicle autonomous lane change parameters, electronic equipment and medium Download PDFInfo
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- CN116107252A CN116107252A CN202310111783.XA CN202310111783A CN116107252A CN 116107252 A CN116107252 A CN 116107252A CN 202310111783 A CN202310111783 A CN 202310111783A CN 116107252 A CN116107252 A CN 116107252A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/18—Propelling the vehicle
- B60W30/18009—Propelling the vehicle related to particular drive situations
- B60W30/18163—Lane change; Overtaking manoeuvres
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M17/00—Testing of vehicles
- G01M17/007—Wheeled or endless-tracked vehicles
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M17/00—Testing of vehicles
- G01M17/007—Wheeled or endless-tracked vehicles
- G01M17/06—Steering behaviour; Rolling behaviour
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0423—Input/output
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/25—Pc structure of the system
- G05B2219/25257—Microcontroller
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The application relates to a calibration method, a device, electronic equipment and a medium for an autonomous lane change parameter of a vehicle, comprising the following steps: the method comprises the steps of obtaining yaw stability data of a vehicle to be tested in a pre-built real vehicle test scene, performing joint debugging on the vehicle to be tested by utilizing a preset transverse and longitudinal drive-by-wire system, performing simulation test on the vehicle to be tested by adopting initial autonomous channel changing parameters to obtain yaw test data, comparing and analyzing the yaw stability data and the yaw test data to obtain stability gradient parameters, and then adjusting the initial autonomous channel changing parameters to obtain new autonomous channel changing parameters, and performing simulation test on the vehicle to be tested until preset autonomous channel changing conditions are met to obtain optimal autonomous channel changing parameters. Therefore, the problems that the vehicle lane changing process is complex, the yaw stability of the vehicle cannot be analyzed and the like are solved, and the yaw stability analysis of the vehicle in the lane changing process is effectively realized by combining the vehicle simulation and the in-loop test, so that the test calibration time cost is saved, and the function development efficiency is improved.
Description
Technical Field
The present disclosure relates to the field of vehicle technologies, and in particular, to a method, an apparatus, an electronic device, and a medium for calibrating an autonomous lane change parameter of a vehicle.
Background
With the intensive research of vehicle assisted driving and automatic driving technologies, in order to avoid potential safety hazards caused by vehicle congestion, it has become possible to realize autonomous lane changing of vehicles in expressway scenes.
In the related art, automatic track changing actions meeting certain conditions, such as track changing by a shift lever, etc., have been realized.
However, the track changing command needs to be manually input by utilizing the driving lever, the track changing action is executed by the vehicle, the operation process is complex, the driving environment needs to be monitored in real time by the driver in the process, the preparation of taking over the vehicle at any time is made, meanwhile, the yaw stability of the vehicle cannot be analyzed in the track changing process, and thus the driving experience of the driver can be reduced, and the problem needs to be solved.
Disclosure of Invention
The application provides a calibration method, device, electronic equipment and medium for an autonomous lane change parameter of a vehicle, so as to solve the problems that the lane change process of the vehicle is complex, the yaw stability of the vehicle cannot be analyzed, and the like.
An embodiment of a first aspect of the present application provides a method for calibrating an autonomous lane change parameter of a vehicle, including the following steps:
acquiring yaw stability data of a vehicle to be tested in a pre-constructed real vehicle test scene;
the vehicle to be tested is subjected to joint debugging by using a preset transverse and longitudinal drive-by-wire system, and is subjected to simulation test by adopting initial autonomous lane change parameters based on a pre-constructed simulation test scene, so that yaw test data are obtained; and
and comparing and analyzing the yaw stability data and the yaw test data to obtain stability gradient parameters, adjusting the initial autonomous channel change parameters according to the stability gradient parameters to obtain new autonomous channel change parameters, and carrying out simulation test on the vehicle to be tested by re-adopting the new autonomous channel change parameters until preset autonomous channel change conditions are met to obtain optimal autonomous channel change parameters.
According to an embodiment of the present application, the initial autonomous channel change parameter is adjusted according to the stability gradient parameter to obtain the new autonomous channel change parameter, and the new autonomous channel change parameter is re-adopted to perform a simulation test on the vehicle to be tested until a preset autonomous channel change condition is satisfied, so as to obtain an optimal autonomous channel change parameter, including:
judging whether the new autonomous channel change parameter meets the preset autonomous channel change condition;
and when the new autonomous channel change parameter meets the preset autonomous channel change condition, obtaining an optimal autonomous channel change parameter, otherwise, carrying out simulation test on the vehicle to be tested again until the preset autonomous channel change condition is met.
According to one embodiment of the present application, before acquiring yaw stability data of the vehicle to be tested in the pre-built real vehicle test scene, the method further includes:
constructing a simulation test scene of the vehicle to be tested based on a modularized mode and a 3D scene;
and constructing a real vehicle test scene of the vehicle to be tested based on a preset standard.
According to an embodiment of the application, the vehicle to be tested is subjected to joint debugging by using a preset transverse and longitudinal drive-by-wire system, and is subjected to simulation test by adopting initial autonomous lane change parameters based on a pre-constructed simulation test scene, so as to obtain yaw test data, and the method comprises the following steps:
debugging longitudinal acceleration response and deceleration response of the vehicle drive-by-wire system to be tested, and generating a longitudinal debugging result;
debugging the transverse response speed and precision of the steering system of the vehicle to be tested, and generating a transverse debugging result;
and controlling the vehicle to be tested to carry out simulation test based on the longitudinal debugging result, the transverse debugging result and the initial autonomous lane change parameter to obtain yaw test data.
According to an embodiment of the present application, based on the longitudinal debugging result, the transverse debugging result and the initial autonomous lane change parameter, the vehicle to be tested is controlled to perform a simulation test, so as to obtain yaw test data, including:
analyzing yaw test data of the vehicle to be tested in a pre-constructed real vehicle test scene;
fitting the yaw test data, analyzing the curvature of the lane change track of the vehicle to be tested, and outputting the running result of the vehicle to be tested;
and outputting stability gradient parameters of the vehicle to be tested based on the running result.
According to the calibration method of the vehicle autonomous channel change parameters, yaw stability data of a vehicle to be tested in a pre-built real vehicle test scene is obtained, the vehicle to be tested is subjected to joint debugging by using a pre-set transverse and longitudinal line control system, the initial autonomous channel change parameters are adopted to carry out simulation test on the vehicle to be tested, yaw test data are obtained, the yaw stability data and the yaw test data are compared and analyzed to obtain stability gradient parameters, then the initial autonomous channel change parameters are adjusted, and therefore new autonomous channel change parameters are obtained, simulation test is carried out on the vehicle to be tested until the pre-set autonomous channel change conditions are met, and the optimal autonomous channel change parameters are obtained. Therefore, the problems that the vehicle lane changing process is complex, the yaw stability of the vehicle cannot be analyzed and the like are solved, and the yaw stability analysis of the vehicle in the lane changing process is effectively realized by combining the vehicle simulation and the in-loop test, so that the test calibration time cost is saved, and the function development efficiency is improved.
An embodiment of a second aspect of the present application provides a calibration device for an autonomous lane change parameter of a vehicle, including:
the acquisition module is used for acquiring yaw stability data of the vehicle to be tested in a pre-constructed real vehicle test scene;
the test module is used for jointly debugging the vehicle to be tested by utilizing a preset transverse and longitudinal drive-by-wire system, and carrying out simulation test on the vehicle to be tested by adopting initial autonomous lane change parameters based on a pre-constructed simulation test scene to obtain yaw test data; and
the analysis module is used for comparing and analyzing the yaw stability data and the yaw test data to obtain stability gradient parameters, adjusting the initial autonomous channel change parameters according to the stability gradient parameters to obtain new autonomous channel change parameters, and re-adopting the new autonomous channel change parameters to perform simulation test on the vehicle to be tested until preset autonomous channel change conditions are met to obtain optimal autonomous channel change parameters.
According to one embodiment of the application, the analysis module is specifically configured to:
judging whether the new autonomous channel change parameter meets the preset autonomous channel change condition;
and when the new autonomous channel change parameter meets the preset autonomous channel change condition, obtaining an optimal autonomous channel change parameter, otherwise, carrying out simulation test on the vehicle to be tested again until the preset autonomous channel change condition is met.
According to one embodiment of the application, before acquiring yaw stability data of the vehicle to be tested in the pre-built real vehicle test scenario, the acquiring module is further configured to:
constructing a simulation test scene of the vehicle to be tested based on a modularized mode and a 3D scene;
and constructing a real vehicle test scene of the vehicle to be tested based on a preset standard.
According to one embodiment of the application, the test module is specifically configured to:
debugging longitudinal acceleration response and deceleration response of the vehicle drive-by-wire system to be tested, and generating a longitudinal debugging result;
debugging the transverse response speed and precision of the steering system of the vehicle to be tested, and generating a transverse debugging result;
and controlling the vehicle to be tested to carry out simulation test based on the longitudinal debugging result, the transverse debugging result and the initial autonomous lane change parameter to obtain yaw test data.
According to one embodiment of the application, the test module is specifically configured to:
analyzing yaw test data of the vehicle to be tested in a pre-constructed real vehicle test scene;
fitting the yaw test data, analyzing the curvature of the lane change track of the vehicle to be tested, and outputting the running result of the vehicle to be tested;
and outputting stability gradient parameters of the vehicle to be tested based on the running result.
According to the calibration device for the vehicle autonomous channel change parameters, yaw stability data of a vehicle to be tested in a pre-built real vehicle test scene is obtained, the vehicle to be tested is subjected to joint debugging by using a pre-set transverse and longitudinal line control system, the initial autonomous channel change parameters are adopted to carry out simulation test on the vehicle to be tested, yaw test data are obtained, the yaw stability data and the yaw test data are compared and analyzed to obtain stability gradient parameters, then the initial autonomous channel change parameters are adjusted, and accordingly new autonomous channel change parameters are obtained, simulation test is carried out on the vehicle to be tested until the pre-set autonomous channel change conditions are met, and the optimal autonomous channel change parameters are obtained. Therefore, the problems that the vehicle lane changing process is complex, the yaw stability of the vehicle cannot be analyzed and the like are solved, and the yaw stability analysis of the vehicle in the lane changing process is effectively realized by combining the vehicle simulation and the in-loop test, so that the test calibration time cost is saved, and the function development efficiency is improved.
An embodiment of a third aspect of the present application provides an electronic device, including: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the calibration method of the vehicle autonomous lane change parameters according to the embodiment.
An embodiment of a fourth aspect of the present application provides a computer readable storage medium having stored thereon a computer program that is executed by a processor for implementing the method for calibrating a vehicle autonomous lane change parameter according to the above embodiment.
Additional aspects and advantages of the 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 the application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flowchart of a method for calibrating an autonomous lane change parameter of a vehicle according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for analyzing yaw stability of an automatic lane change of a vehicle on different roadways in accordance with one embodiment of the application;
FIG. 3 is a control model schematic of a vehicle test according to one embodiment of the present application;
FIG. 4 is a schematic illustration of a vehicle trajectory according to one embodiment of the present application;
FIG. 5 is a schematic diagram of various pavement stability analysis according to one embodiment of the present application;
FIG. 6 is an example diagram of a calibration device for vehicle autonomous lane change parameters according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
The following describes a calibration method, a device, electronic equipment and a medium for an autonomous lane change parameter of a vehicle according to an embodiment of the application with reference to the accompanying drawings. Aiming at the problems that the vehicle track changing process is complex and the yaw stability of the vehicle cannot be analyzed in the background technology, the application provides a calibration method of the vehicle autonomous track changing parameter, in the method, yaw stability data of a vehicle to be tested in a pre-constructed real vehicle test scene is obtained, the vehicle to be tested is subjected to joint adjustment by a preset transverse and longitudinal drive-by-wire system, an initial autonomous track changing parameter is adopted to carry out simulation test on the vehicle to be tested, yaw test data is obtained, the yaw stability data and the yaw test data are compared and analyzed to obtain a stability gradient parameter, then the initial autonomous track changing parameter is adjusted, so that a new autonomous track changing parameter is obtained, simulation test is carried out on the vehicle to be tested until a preset autonomous track changing condition is met, and an optimal autonomous track changing parameter is obtained. Therefore, the problems that the vehicle lane changing process is complex, the yaw stability of the vehicle cannot be analyzed and the like are solved, and the yaw stability analysis of the vehicle in the lane changing process is effectively realized by combining the vehicle simulation and the in-loop test, so that the test calibration time cost is saved, and the function development efficiency is improved.
Specifically, before introducing the embodiments of the present application, first, a description is given of related application modules and test conditions to be provided for a test vehicle according to the embodiments of the present application, including sensing and target recognition systems such as a drive-by-wire chassis, a front intelligent front view module, a four-corner millimeter wave radar, a front millimeter wave radar, and the like; the intelligent driving system and the drive-by-wire system complete joint debugging and meet response requirements; the simulation system needs to have data acquisition and analysis capability, and the input and output of each module are normal, wherein the data acquisition and analysis requires CAN (Controller Area Network ) analysis tools, and the software has the capability of processing information of each system.
Specifically, fig. 1 is a flow chart of a calibration method for an autonomous lane change parameter of a vehicle according to an embodiment of the present application.
As shown in fig. 1, the calibration method of the vehicle autonomous lane change parameter comprises the following steps:
in step S101, yaw stability data of a vehicle to be tested in a pre-built real vehicle test scene is acquired.
Further, in some embodiments, before acquiring yaw stability data of the vehicle under test in the pre-built real vehicle test scenario, the method further includes: constructing a simulation test scene of the vehicle to be tested based on the modularized mode and the 3D scene; and constructing a real vehicle test scene of the vehicle to be tested based on a preset standard.
Specifically, as shown in fig. 2, before a real vehicle test is performed on a vehicle to be tested, a real vehicle test scene including a 3-lane road model and a double lane change site marked with a cone barrel is first set up based on expressway standards; secondly, constructing a simulation environment of a double lane change scene of the vehicle to be tested, and constructing a simulation test scene of the vehicle to be tested mainly based on a modularized mode and a 3D scene; and finally, acquiring yaw stability data of the vehicle to be tested based on the constructed simulation test scene and the real vehicle test scene.
The building of the double-lane-change simulation scene of the vehicle to be tested mainly comprises building a vehicle model to be tested, such as a simulation vehicle model, a real vehicle interface model, a driver model, an autonomous steering planning model, a vehicle transverse and longitudinal control model, a data acquisition module, a yaw stability calculation module and the like, as shown in fig. 3.
In step S102, the vehicle to be tested is co-tuned by using a preset transverse and longitudinal drive-by-wire system, and based on a pre-constructed simulation test scene, an initial autonomous lane change parameter is adopted to perform a simulation test on the vehicle to be tested, so as to obtain yaw test data.
Further, in some embodiments, the vehicle to be tested is co-tuned by using a preset transverse and longitudinal drive-by-wire system, and based on a pre-constructed simulation test scene, an initial autonomous lane change parameter is adopted to perform a simulation test on the vehicle to be tested, so as to obtain yaw test data, including: debugging longitudinal acceleration response and deceleration response of a vehicle drive-by-wire system to be tested, and generating a longitudinal debugging result; debugging the transverse response speed and precision of a steering system of the vehicle to be tested, and generating a transverse debugging result; and controlling the vehicle to be tested to perform simulation test based on the longitudinal debugging result, the transverse debugging result and the initial autonomous lane change parameter to obtain yaw test data.
Further, in some embodiments, the vehicle to be tested is controlled to perform a simulation test based on the longitudinal debugging result, the transverse debugging result and the initial autonomous lane change parameter to obtain yaw test data, including: analyzing yaw test data of a vehicle to be tested in a pre-constructed real vehicle test scene; fitting yaw test data, analyzing the curvature of a lane change track of the vehicle to be tested, and outputting the running result of the vehicle to be tested; and outputting stability gradient parameters of the vehicle to be tested based on the operation result.
The preset horizontal and vertical drive-by-wire system may be a test system set by a person skilled in the art, or may be a test system obtained through computer simulation, which is not limited herein.
Specifically, in the embodiment of the application, after a simulation test scene of a vehicle to be tested is built, a longitudinal acceleration response and a longitudinal deceleration response of the vehicle to be tested are required to be debugged through a longitudinal drive-by-wire system, and a control interface threshold value of response time is adjusted through an adjustment algorithm model, so that the vehicle has certain stability, and a longitudinal debugging result is finally generated; meanwhile, the transverse response speed and the precision of the vehicle to be tested are required to be debugged through a transverse drive-by-wire system, and finally a transverse debugging result is generated.
Specifically, in the embodiment of the application, the vehicle to be tested is controlled to perform simulation test based on the longitudinal debugging result, the transverse debugging result and the initial autonomous lane change parameter, so that the acceleration, deceleration and steering requirements of the intelligent driving system are required to meet the function specification requirements, and yaw test data is finally obtained.
Specifically, the embodiment of the application needs to analyze the yaw stability of the vehicle to be tested in a real vehicle test scene and a simulation test scene. Firstly, preliminarily determining relevant control parameter basic values through simulation tests; secondly, controlling the vehicle to perform a double-lane change test by a driver, and recording yaw stability parameters with good comfort as references by subjective evaluation of the driver; and finally, controlling the vehicle to test by a control algorithm, and recording yaw test data.
Further, as shown in fig. 4, after yaw test data are obtained, the embodiment of the application respectively analyzes yaw test data of the vehicle to be tested in a simulation test scene and a real vehicle test scene, then fits the yaw test data and analyzes the curvature of the lane change track of the vehicle to be tested, analyzes the driving track of the vehicle to be tested, outputs the running result of the vehicle to be tested, and analyzes the yaw stability of the vehicle to be tested based on the running result, so as to output the stability gradient parameter of the vehicle to be tested.
In step S103, the yaw stability data and the yaw test data are compared and analyzed to obtain stability gradient parameters, and the initial autonomous channel change parameters are adjusted according to the stability gradient parameters to obtain new autonomous channel change parameters, and the new autonomous channel change parameters are adopted again to perform simulation test on the vehicle to be tested until preset autonomous channel change conditions are met, so that the optimal autonomous channel change parameters are obtained.
Further, in some embodiments, the initial autonomous channel change parameter is adjusted according to the stability gradient parameter to obtain a new autonomous channel change parameter, and the new autonomous channel change parameter is re-adopted to perform a simulation test on the vehicle to be tested until a preset autonomous channel change condition is satisfied, so as to obtain an optimal autonomous channel change parameter, including: judging whether the new autonomous channel change parameter meets the preset autonomous channel change condition; when the new autonomous channel change parameter meets the preset autonomous channel change condition, obtaining the optimal autonomous channel change parameter, otherwise, carrying out simulation test on the vehicle to be tested again until the preset autonomous channel change condition is met.
The preset autonomous channel changing condition may be a channel changing condition set by a person skilled in the art, or may be a channel changing condition obtained through computer simulation, which is not limited herein.
Specifically, in the embodiment of the application, based on the stability gradient parameter obtained by analyzing the yaw stability data and the yaw test data, the initial autonomous channel change parameter is adjusted according to the stability gradient parameter to obtain a new autonomous channel change parameter, and then an optimal path and a speed plan are generated according to the new autonomous channel change parameter.
Specifically, as shown in fig. 5, after obtaining a new independent lane change parameter, the embodiment of the application needs to determine whether the new independent lane change parameter meets a preset independent lane change condition, that is, determine the stability of the new independent lane change parameter under different road conditions, if the new independent lane change parameter meets the preset independent lane change condition, obtain an optimal independent lane change parameter, and the vehicle to be tested performs a lane change action according to the optimal independent lane change parameter, if the new independent lane change parameter does not meet the preset independent lane change condition, perform a simulation test on the vehicle to be tested again until the independent lane change parameter meeting the preset independent lane change condition is obtained, and finally complete the lane change action, so as to improve the effect of yaw stability analysis of the vehicle in the independent lane change process and improve the test calibration efficiency.
According to the calibration method of the vehicle autonomous channel change parameters, yaw stability data of a vehicle to be tested in a pre-built real vehicle test scene is obtained, the vehicle to be tested is subjected to joint debugging by using a pre-set transverse and longitudinal line control system, the initial autonomous channel change parameters are adopted to carry out simulation test on the vehicle to be tested, yaw test data are obtained, the yaw stability data and the yaw test data are compared and analyzed to obtain stability gradient parameters, then the initial autonomous channel change parameters are adjusted, and therefore new autonomous channel change parameters are obtained, simulation test is carried out on the vehicle to be tested until the pre-set autonomous channel change conditions are met, and the optimal autonomous channel change parameters are obtained. Therefore, the problems that the vehicle lane changing process is complex, the yaw stability of the vehicle cannot be analyzed and the like are solved, and the yaw stability analysis of the vehicle in the lane changing process is effectively realized by combining the vehicle simulation and the in-loop test, so that the test calibration time cost is saved, and the function development efficiency is improved.
Next, a calibration device for the vehicle autonomous lane change parameter according to the embodiment of the application will be described with reference to the accompanying drawings.
FIG. 6 is a block schematic diagram of a calibration device for vehicle autonomous lane change parameters according to an embodiment of the present application.
As shown in fig. 6, the calibration device 10 for the vehicle autonomous lane change parameter includes: an acquisition module 100, a test module 200 and an analysis module 300.
The acquiring module 100 is configured to acquire yaw stability data of a vehicle to be tested in a pre-constructed real vehicle test scene;
the test module 200 is used for jointly debugging the vehicle to be tested by utilizing a preset transverse and longitudinal drive-by-wire system, and carrying out simulation test on the vehicle to be tested by adopting initial autonomous lane change parameters based on a pre-constructed simulation test scene to obtain yaw test data; and
the analysis module 300 is configured to compare and analyze the yaw stability data and the yaw test data to obtain a stability gradient parameter, adjust an initial autonomous channel change parameter according to the stability gradient parameter to obtain a new autonomous channel change parameter, and re-use the new autonomous channel change parameter to perform a simulation test on the vehicle to be tested until a preset autonomous channel change condition is met, so as to obtain an optimal autonomous channel change parameter.
Further, in some embodiments, the analysis module 300 is specifically configured to:
judging whether the new autonomous channel change parameter meets the preset autonomous channel change condition;
when the new autonomous channel change parameter meets the preset autonomous channel change condition, obtaining the optimal autonomous channel change parameter, otherwise, carrying out simulation test on the vehicle to be tested again until the preset autonomous channel change condition is met.
Further, in some embodiments, before acquiring yaw stability data of the vehicle under test in the pre-built real vehicle test scenario, the acquiring module 100 is further configured to:
constructing a simulation test scene of the vehicle to be tested based on the modularized mode and the 3D scene;
and constructing a real vehicle test scene of the vehicle to be tested based on a preset standard.
Further, in some embodiments, the test module 200 is specifically configured to:
debugging longitudinal acceleration response and deceleration response of a vehicle drive-by-wire system to be tested, and generating a longitudinal debugging result;
debugging the transverse response speed and precision of a steering system of the vehicle to be tested, and generating a transverse debugging result;
and controlling the vehicle to be tested to perform simulation test based on the longitudinal debugging result, the transverse debugging result and the initial autonomous lane change parameter to obtain yaw test data.
Further, in some embodiments, the test module 200 is specifically configured to:
analyzing yaw test data of a vehicle to be tested in a pre-constructed real vehicle test scene;
fitting yaw test data, analyzing the curvature of a lane change track of the vehicle to be tested, and outputting the running result of the vehicle to be tested;
and outputting stability gradient parameters of the vehicle to be tested based on the operation result.
According to the calibration device for the vehicle autonomous channel change parameters, yaw stability data of a vehicle to be tested in a pre-built real vehicle test scene is obtained, the vehicle to be tested is subjected to joint debugging by using a pre-set transverse and longitudinal line control system, the initial autonomous channel change parameters are adopted to carry out simulation test on the vehicle to be tested, yaw test data are obtained, the yaw stability data and the yaw test data are compared and analyzed to obtain stability gradient parameters, then the initial autonomous channel change parameters are adjusted, and accordingly new autonomous channel change parameters are obtained, simulation test is carried out on the vehicle to be tested until the pre-set autonomous channel change conditions are met, and the optimal autonomous channel change parameters are obtained. Therefore, the problems that the vehicle lane changing process is complex, the yaw stability of the vehicle cannot be analyzed and the like are solved, and the yaw stability analysis of the vehicle in the lane changing process is effectively realized by combining the vehicle simulation and the in-loop test, so that the test calibration time cost is saved, and the function development efficiency is improved.
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:
The processor 702 executes the program to implement the calibration method for the vehicle autonomous lane change parameter provided in the above embodiment.
Further, the electronic device further includes:
a communication interface 703 for communication between the memory 701 and the processor 702.
The memory 701 may include a high-speed RAM memory or may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.
If the memory 701, the processor 702, and the communication interface 703 are implemented independently, the communication interface 703, the memory 701, and the processor 702 may be connected to each other through a bus and perform communication with each other. The bus may be an industry standard architecture (Industry Standard Architecture, abbreviated ISA) bus, an external device interconnect (Peripheral Component, abbreviated PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 7, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 701, the processor 702, and the communication interface 703 are integrated on a chip, the memory 701, the processor 702, and the communication interface 703 may communicate with each other through internal interfaces.
The processor 702 may be a central processing unit (Central Processing Unit, abbreviated as CPU) or an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC) or one or more integrated circuits configured to implement embodiments of the present application.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, realizes the calibration method of the vehicle autonomous lane change parameter.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or part of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, and the program may be stored in a computer readable storage medium, where the program when executed includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented as software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. The method for calibrating the automatic lane change parameters of the vehicle is characterized by comprising the following steps of:
acquiring yaw stability data of a vehicle to be tested in a pre-constructed real vehicle test scene;
the vehicle to be tested is subjected to joint debugging by using a preset transverse and longitudinal drive-by-wire system, and is subjected to simulation test by adopting initial autonomous lane change parameters based on a pre-constructed simulation test scene, so that yaw test data are obtained; and
and comparing and analyzing the yaw stability data and the yaw test data to obtain stability gradient parameters, adjusting the initial autonomous channel change parameters according to the stability gradient parameters to obtain new autonomous channel change parameters, and carrying out simulation test on the vehicle to be tested by re-adopting the new autonomous channel change parameters until preset autonomous channel change conditions are met to obtain optimal autonomous channel change parameters.
2. The method of claim 1, wherein adjusting the initial autonomous lane change parameter according to the stability gradient parameter to obtain the new autonomous lane change parameter, and re-employing the new autonomous lane change parameter to perform a simulation test on the vehicle under test until a preset autonomous lane change condition is satisfied, to obtain an optimal autonomous lane change parameter, comprises:
judging whether the new autonomous channel change parameter meets the preset autonomous channel change condition;
and when the new autonomous channel change parameter meets the preset autonomous channel change condition, obtaining an optimal autonomous channel change parameter, otherwise, carrying out simulation test on the vehicle to be tested again until the preset autonomous channel change condition is met.
3. The method of claim 1, further comprising, prior to acquiring yaw stability data of the vehicle under test in the pre-constructed real vehicle test scenario:
constructing a simulation test scene of the vehicle to be tested based on a modularized mode and a 3D scene;
and constructing a real vehicle test scene of the vehicle to be tested based on a preset standard.
4. The method of claim 1, wherein the vehicle to be tested is intermodal with a pre-set horizontal and vertical drive-by-wire system, and based on a pre-built simulation test scenario, the vehicle to be tested is subjected to a simulation test with initial autonomous lane change parameters to obtain yaw test data, comprising:
debugging longitudinal acceleration response and deceleration response of the vehicle drive-by-wire system to be tested, and generating a longitudinal debugging result;
debugging the transverse response speed and precision of the steering system of the vehicle to be tested, and generating a transverse debugging result;
and controlling the vehicle to be tested to carry out simulation test based on the longitudinal debugging result, the transverse debugging result and the initial autonomous lane change parameter to obtain yaw test data.
5. The method of claim 1, wherein controlling the vehicle under test to perform a simulation test based on the longitudinal debug result, the lateral debug result, and the initial autonomous lane change parameter to obtain yaw test data comprises:
analyzing yaw test data of the vehicle to be tested in a pre-constructed real vehicle test scene;
fitting the yaw test data, analyzing the curvature of the lane change track of the vehicle to be tested, and outputting the running result of the vehicle to be tested;
and outputting stability gradient parameters of the vehicle to be tested based on the running result.
6. The utility model provides a calibration device of vehicle autonomous lane change parameter which characterized in that includes:
the acquisition module is used for acquiring yaw stability data of the vehicle to be tested in a pre-constructed real vehicle test scene;
the test module is used for jointly debugging the vehicle to be tested by utilizing a preset transverse and longitudinal drive-by-wire system, and carrying out simulation test on the vehicle to be tested by adopting initial autonomous lane change parameters based on a pre-constructed simulation test scene to obtain yaw test data; and
the analysis module is used for comparing and analyzing the yaw stability data and the yaw test data to obtain stability gradient parameters, adjusting the initial autonomous channel change parameters according to the stability gradient parameters to obtain new autonomous channel change parameters, and re-adopting the new autonomous channel change parameters to perform simulation test on the vehicle to be tested until preset autonomous channel change conditions are met to obtain optimal autonomous channel change parameters.
7. The apparatus according to claim 6, wherein the analysis module is specifically configured to:
judging whether the new autonomous channel change parameter meets the preset autonomous channel change condition;
and when the new autonomous channel change parameter meets the preset autonomous channel change condition, obtaining an optimal autonomous channel change parameter, otherwise, carrying out simulation test on the vehicle to be tested again until the preset autonomous channel change condition is met.
8. The apparatus of claim 6, wherein prior to acquiring yaw stability data of a vehicle under test in the pre-constructed real vehicle test scenario, the acquisition module is further configured to:
constructing a simulation test scene of the vehicle to be tested based on a modularized mode and a 3D scene;
and constructing a real vehicle test scene of the vehicle to be tested based on a preset standard.
9. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of calibrating a vehicle autonomous lane change parameter as claimed in any one of claims 1 to 5.
10. A computer readable storage medium having stored thereon a computer program, characterized in that the program is executed by a processor for implementing a method of calibrating a vehicle autonomous lane change parameter according to any of claims 1-5.
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| CN116661428A (en) * | 2023-07-28 | 2023-08-29 | 江西五十铃汽车有限公司 | Method and system for testing performance parameters of whole vehicle |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116661428A (en) * | 2023-07-28 | 2023-08-29 | 江西五十铃汽车有限公司 | Method and system for testing performance parameters of whole vehicle |
| CN116661428B (en) * | 2023-07-28 | 2023-11-03 | 江西五十铃汽车有限公司 | Method and system for testing performance parameters of whole vehicle |
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