CN117369406A - Real vehicle testing method and device based on rapid prototyping, electronic equipment and medium - Google Patents
Real vehicle testing method and device based on rapid prototyping, electronic equipment and medium Download PDFInfo
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Abstract
The application relates to the technical field of vehicle testing, in particular to a real vehicle testing method, device, electronic equipment and medium based on a rapid prototype, wherein the method comprises the following steps: the method comprises the steps of performing track processing on acquired vehicle positioning data to obtain expected track planning information of a vehicle, constructing a target control model by combining a preset track tracking algorithm, compiling the target control model to obtain calibration files and program files required by debugging, loading the files into preset test software, and performing real vehicle test by utilizing the algorithm to be tested to obtain a real vehicle test result of the target control model. Therefore, the problems of long test flow period, low reliability, high development cost and the like caused by imperfect upstream planning algorithm and random path input in the test process of the vehicle are solved, and the control model can be used for conducting verification in advance by introducing the track acquisition and tracking control ideas, and the tracking effect under multiple scenes can be achieved when the tracks of different scenes are tracked.
Description
Technical Field
The application relates to the technical field of vehicle testing, in particular to a real vehicle testing method, device, electronic equipment and medium based on a rapid prototype.
Background
With the rapid development of automatic driving vehicles, the automatic driving test technology becomes a guarantee of safe driving, wherein the control algorithm test for track tracking is more commonly required by users, the perfect control algorithm can more accurately track the preset path of the client, and the traffic accidents caused by human factors can be reduced to a certain extent, so that a great amount of tests and evaluations are very important for ensuring the safety and reliability of the vehicles in the running process.
In the related art, a rapid prototyping method based on a micro auto Box rapid prototyping device or a CAN (CAN Application Programming Environment ) rapid prototyping implementation method is mostly adopted to perform real vehicle testing, and in the initial stage of control system development, special hardware of the micro auto Box is used as a hardware running environment of a control algorithm and a control logic code, and according to a product development flow, the control algorithm CAN develop a control model to perform real vehicle testing when waiting for the input of an upstream planning algorithm to be perfect.
However, the following drawbacks may exist in the real vehicle testing process by the above method: (1) The strict follow of the development flow can lead to longer project development period and the control algorithm can not effectively carry out verification in the early development period, so that the reliability is not needed, and the project progress is affected; (2) The MicroAutoBox rapid prototyping equipment and corresponding software are high in price, and development cost is easy to be excessively high; (3) By adopting the micro auto Box or CANape rapid prototyping implementation method, the real vehicle test can be carried out when the upstream path planning algorithm is still required to be perfected, so that the control algorithm verification waiting period is longer, and the problem needs to be solved.
Disclosure of Invention
The application provides a real vehicle testing method, device, electronic equipment and medium based on a rapid prototype, which are used for solving the problems that a vehicle is imperfect in an upstream planning algorithm and is subjected to path input randomly in the testing process, so that the testing process is long in period, low in reliability, high in development cost and the like.
An embodiment of a first aspect of the present application provides a real vehicle testing method based on a rapid prototyping, including the following steps:
acquiring positioning data of a vehicle acquired in advance;
performing track processing on the positioning data to obtain expected track planning information of the vehicle;
Constructing a target control model based on the expected track planning information and a preset track tracking algorithm, and compiling the target control model to obtain a calibration file and a program file required by debugging; and
and loading the calibration file and the program file into preset test software, and performing real vehicle test by using a to-be-tested algorithm to obtain a real vehicle test result of the target control model.
According to an embodiment of the present application, the performing track processing on the positioning data to obtain the desired track planning information of the vehicle includes:
converting the positioning data into a UTM (Universal Transverse Mercator Grid System, universal cross ink card grid system)) coordinate system based on a preset coordinate conversion algorithm to obtain UTM positioning data;
and carrying out track smoothing on the UTM positioning data based on a preset track processing strategy, and calculating to obtain expected track planning information of the vehicle according to the processed UTM positioning data.
According to one embodiment of the present application, the constructing a target control model based on the desired trajectory planning information and the preset trajectory tracking algorithm includes:
Based on the expected track planning information of the vehicle, utilizing the preset track tracking algorithm to respectively carry out longitudinal acceleration following control and transverse steering wheel corner following control on the vehicle to obtain acceleration following information and steering wheel corner following information of the vehicle;
and perfecting an input module of the target control model according to expected track planning information, acceleration following information and steering wheel rotation angle following information of the vehicle, and perfecting an output module of the target control model according to the preset perfecting strategy to obtain the target control model.
According to one embodiment of the present application, after obtaining the acceleration following information and the steering wheel angle following information of the vehicle, the method further includes:
verifying a longitudinal control algorithm of the vehicle based on the acceleration following information of the vehicle to obtain a longitudinal control algorithm effect of the vehicle;
and verifying a transverse control algorithm of the vehicle based on the steering wheel rotation angle following information of the vehicle to obtain the transverse control algorithm effect of the vehicle.
According to one embodiment of the present application, the input module for perfecting the target control model according to the desired trajectory planning information, the acceleration following information, and the steering wheel rotation angle following information of the vehicle, and the output module for perfecting the target control model according to the preset perfecting strategy, includes:
Acquiring target input information of the vehicle;
calculating target acceleration information of the vehicle according to the longitudinal control algorithm based on the target input information, and calculating steering wheel angle information of the vehicle according to the transverse control algorithm;
calculating a control frame of the steering wheel angle according to the steering wheel angle information, and calculating a control frame of the target acceleration according to the target acceleration information to respectively obtain the steering wheel angle control frame and the target acceleration control frame;
converting the steering wheel angle control frame and the target acceleration control frame into formats required by a control model to be output according to a preset perfect output strategy;
the steering wheel angle command of the vehicle is received by using an EPS (Electric Power Steering), and the target acceleration command of the vehicle is received by using an ESP (Electronic Stability Program, vehicle body electronic stability system) to control the vehicle to perform a real vehicle test according to the steering wheel angle control frame and the target acceleration control frame.
According to one embodiment of the present application, after collecting the positioning data of the vehicle, further comprising:
Acquiring chassis data and gear information of the vehicle;
and controlling the vehicle to debug based on the positioning data, chassis data and gear information of the vehicle.
According to the rapid prototyping-based real vehicle testing method, through track processing is conducted on acquired vehicle positioning data, expected track planning information to be tracked by a vehicle is obtained, a target control model is built and compiled based on the expected track planning information and a preset track tracking algorithm, calibration files and program files required by debugging are obtained, meanwhile, the files are loaded to preset testing software, real vehicle testing is conducted through the to-be-tested algorithm, and a real vehicle testing result of the target control model is obtained. Therefore, the problems of long test flow period, low reliability, high development cost and the like caused by imperfect upstream planning algorithm and random path input in the test process of the vehicle are solved, and the control model can be used for conducting verification in advance by introducing the track acquisition and tracking control ideas, and the tracking effect under multiple scenes can be achieved when the tracks of different scenes are tracked.
An embodiment of a second aspect of the present application provides a real vehicle testing device based on a rapid prototyping, including:
The acquisition module is used for acquiring the positioning data of the vehicle acquired in advance;
the preprocessing module is used for carrying out track processing on the positioning data to obtain expected track planning information of the vehicle;
the construction module is used for constructing a target control model based on the expected track planning information and a preset track tracking algorithm, compiling the target control model and obtaining a calibration file and a program file required by debugging; and
and the test module is used for loading the calibration file and the program file into preset test software, and carrying out real vehicle test by utilizing a to-be-tested algorithm to obtain a real vehicle test result of the target control model.
According to one embodiment of the present application, the preprocessing module is specifically configured to:
converting the positioning data into a UTM coordinate system based on a preset coordinate conversion algorithm to obtain UTM positioning data;
and carrying out track smoothing on the UTM positioning data based on a preset track processing strategy, and calculating to obtain expected track planning information of the vehicle according to the processed UTM positioning data.
According to one embodiment of the present application, the building block is specifically configured to:
Based on expected track planning information of the vehicle, longitudinal acceleration following control and transverse steering wheel corner following control are respectively carried out on the vehicle by utilizing a preset track tracking algorithm, so that acceleration following information and steering wheel corner following information of the vehicle are obtained;
and perfecting an input module of the target control model according to expected track planning information, acceleration following information and steering wheel rotation angle following information of the vehicle, and perfecting an output module of the target control model according to the preset perfecting strategy to obtain the target control model.
According to one embodiment of the application, after obtaining the acceleration following information and the steering wheel angle following information of the vehicle, the building module is further configured to:
verifying a longitudinal control algorithm of the vehicle based on the acceleration following information of the vehicle to obtain a longitudinal control algorithm effect of the vehicle;
and verifying a transverse control algorithm of the vehicle based on the steering wheel rotation angle following information of the vehicle to obtain the transverse control algorithm effect of the vehicle.
According to one embodiment of the present application, the building block is specifically configured to:
Acquiring target input information of the vehicle;
calculating target acceleration information of the vehicle according to the longitudinal control algorithm based on the target input information, and calculating steering wheel angle information of the vehicle according to the transverse control algorithm;
calculating a control frame of the steering wheel angle according to the steering wheel angle information, and calculating a control frame of the target acceleration according to the target acceleration information to respectively obtain the steering wheel angle control frame and the target acceleration control frame;
converting the steering wheel angle control frame and the target acceleration control frame into formats required by a target control model to be output according to a preset perfect output strategy;
and receiving a steering wheel angle instruction of the vehicle by using an EPS (expandable polystyrene), and receiving a target acceleration instruction of the vehicle by using an ESP (electronic stability program) so as to control the vehicle to perform a real vehicle test according to the steering wheel angle control frame and the target acceleration control frame.
According to one embodiment of the application, after acquiring the pre-acquired positioning data of the vehicle, the acquisition module is further configured to:
acquiring chassis data and gear information of the vehicle;
And controlling the vehicle to debug based on the positioning data, chassis data and gear information of the vehicle.
According to the rapid prototyping-based real vehicle testing device, through track processing on acquired vehicle positioning data, expected track planning information to be tracked by a vehicle is obtained, a target control model is constructed and compiled based on the expected track planning information and a preset track tracking algorithm, calibration files and program files required by debugging are obtained, meanwhile, the files are loaded to preset testing software, and real vehicle testing is carried out by utilizing the algorithm to be tested, so that a real vehicle testing result of the target control model is obtained. Therefore, the problems of long test flow period, low reliability, high development cost and the like caused by imperfect upstream planning algorithm and random path input in the test process of the vehicle are solved, and the control model can be used for conducting verification in advance by introducing the track acquisition and tracking control ideas, and the tracking effect under multiple scenes can be achieved when the tracks of different scenes are tracked.
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 rapid prototyping-based real vehicle testing method as described in the embodiment.
An embodiment of a fourth aspect of the present application provides a computer readable storage medium having stored thereon a computer program for execution by a processor for implementing a rapid prototyping-based real-vehicle testing method as described in the above embodiments.
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.
Drawings
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 flow chart of a real vehicle test method based on a rapid prototyping method according to one embodiment of the present application;
FIG. 2 is a schematic diagram of a trajectory acquisition system according to one embodiment of the present application;
FIG. 3 is a schematic diagram of a control model real vehicle test system according to one embodiment of the present application;
FIG. 4 is a schematic diagram of a rapid prototyping real vehicle test implementation in accordance with one embodiment of the present application;
FIG. 5 is a schematic diagram of a control model profile and input and output perfection ideas according to one embodiment of the present application;
FIG. 6 is an example diagram of a rapid prototyping-based real-vehicle testing apparatus in accordance with 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 rapid prototyping-based real vehicle testing method, a rapid prototyping-based real vehicle testing device, an electronic device and a storage medium according to embodiments of the present application with reference to the accompanying drawings. Aiming at the problems that in the prior art, the upstream planning algorithm of a vehicle is imperfect and a path is input randomly in the testing process, so that the testing process has long period, low reliability and high development cost, the application provides a real vehicle testing method based on a rapid prototype. Therefore, the problems of long test flow period, low reliability, high development cost and the like caused by imperfect upstream planning algorithm and random path input in the test process of the vehicle are solved, and the control model can be used for conducting verification in advance by introducing the track acquisition and tracking control ideas, and the tracking effect under multiple scenes can be achieved when the tracks of different scenes are tracked.
Specifically, fig. 1 is a schematic flow chart of a real vehicle testing method based on a rapid prototyping provided in an embodiment of the present application.
As shown in fig. 1, the real vehicle testing method based on the rapid prototyping comprises the following steps:
in step S101, positioning data of a vehicle acquired in advance is acquired.
According to one embodiment of the present application, after collecting the positioning data of the vehicle, further comprising: collecting chassis data and gear information of a vehicle; and controlling the vehicle to debug based on the positioning data, the chassis data and the gear information of the vehicle.
Specifically, as shown in fig. 2 and 3, the embodiment of the present application is equipped with a combined navigation system to implement real-time positioning of a vehicle, and collects positioning data of the vehicle according to real-time positioning information of the vehicle, and based on a CANape rapid prototyping test concept and a track collection concept, a PC (Personal Computer ) machine running the CANape is adopted.
Specifically, as shown in fig. 4, when the vehicle is running, firstly, the running track of the vehicle is measured by a GPS (Global Positioning System ) and IMU (Inertial Measurement Unit, inertial measurement unit) integrated navigation system assembled by the vehicle, and the measured running track is propagated through a CAN (Controller Area Network ) network, and then the positioning data of the vehicle is collected by the CANape software; and secondly, acquiring chassis data and gear information of the vehicle, and controlling the vehicle to debug based on the positioning data, the chassis data and the gear information of the vehicle.
In step S102, track processing is performed on the positioning data to obtain desired track planning information of the vehicle.
Further, in some embodiments, track processing is performed on the positioning data to obtain track planning and planning information of the vehicle, including: converting the positioning data into a UTM coordinate system based on a preset coordinate conversion algorithm to obtain UTM positioning data; and carrying out track smoothing on the UTM positioning data based on a preset track processing strategy, and calculating to obtain expected track planning information of the vehicle according to the processed UTM positioning data.
The preset coordinate conversion algorithm may be a coordinate conversion algorithm adopted by a person skilled in the art according to actual test requirements, or may be a coordinate conversion algorithm obtained by multiple simulation of a computer, which is not limited herein.
Specifically, the embodiment of the application converts the collected positioning data of the vehicle into UTM positioning data from longitude and latitude coordinates through a preset coordinate conversion algorithm, and performs track smoothing on the UTM positioning data based on a preset track processing strategy, and calculates corresponding course angle, slope, displacement, speed, acceleration, curvature and the like to obtain expected track planning information of the vehicle to be tracked, so that a section of tracking track of the vehicle is obtained. Wherein, the course angle formula on the track can be expressed as:
θ＝arctan(y′)
The displacement formula can be expressed as:
the velocity formula can be expressed as:
v＝s/t
the acceleration formula can be expressed as:
a＝v/t
the curvature formula can be expressed as:
wherein,
in step S103, a target control model is constructed based on the desired trajectory planning information and a preset trajectory tracking algorithm, and the target control model is compiled to obtain calibration files and program files required for debugging.
Further, in some embodiments, constructing the target control model based on the desired trajectory planning information and a preset trajectory tracking algorithm includes: based on expected track planning information of the vehicle, longitudinal acceleration following control and transverse steering wheel corner following control are respectively carried out on the vehicle by utilizing a preset track tracking algorithm, so that acceleration following information and steering wheel corner following information of the vehicle are obtained; and improving an input module of a target control model according to the expected track planning information, the acceleration following information and the steering wheel rotation angle following information of the vehicle, and improving an output module of the target control model according to a preset improvement strategy to obtain the target control model.
Further, in some embodiments, after obtaining the acceleration following information and the steering wheel angle following information of the vehicle, the method further includes: verifying a longitudinal control algorithm of the vehicle based on the acceleration following information of the vehicle to obtain a longitudinal control algorithm effect of the vehicle; and verifying a transverse control algorithm of the vehicle based on steering wheel rotation angle following information of the vehicle to obtain the transverse control algorithm effect of the vehicle.
The preset track tracking algorithm may be a track tracking algorithm adopted by a person skilled in the art according to actual test requirements, or may be a track tracking algorithm obtained by multiple simulation of a computer, and the preset perfection strategy may be a strategy adopted by a person skilled in the art according to actual test requirements, which is not particularly limited herein.
Specifically, according to the acquired expected track planning information, the longitudinal acceleration following control and the transverse steering wheel corner following control are respectively carried out on the vehicle by using a preset track tracking algorithm to obtain the acceleration following information and the steering wheel corner following information of the vehicle, so that an input module of a target control model is perfected according to the expected track planning information, the acceleration following information and the steering wheel corner following information of the vehicle, an output module is perfected according to a preset perfecting strategy, such as a rapid prototyping real vehicle testing method of testing software, and the target control model is built by using Matlab/Simulink software.
Further, after the target control model is built, the collected expected track planning information is input into the target control model as an expected track, and the tracking effect of the target control model is verified by matching with a preset track tracking algorithm, namely, the longitudinal control algorithm of the vehicle is verified based on the acceleration following information of the vehicle, so that the longitudinal control algorithm effect of the vehicle is obtained; and verifying a transverse control algorithm of the vehicle based on steering wheel rotation angle following information of the vehicle to obtain a transverse control algorithm effect of the vehicle, thereby obtaining a final tracking effect of the target control model. The longitudinal control algorithm of the embodiment of the application adopts a double-loop PID (Proportion Integral Differential, PID algorithm), such as displacement-speed PID and speed-acceleration PID control algorithm, and the transverse control algorithm adopts a linear quadratic form-based optimal control algorithm, such as LQR (Linear Quadratic Regulator, linear quadratic form regulator) control algorithm.
Further, in some embodiments, an input module for refining a target control model based on desired trajectory planning information, acceleration following information, and steering wheel angle following information for a vehicle includes: acquiring target input information of a vehicle; calculating target acceleration information of the vehicle according to a longitudinal control algorithm based on the target input information, and calculating steering wheel angle information of the vehicle according to a transverse control algorithm; calculating a control frame of the steering wheel angle according to the steering wheel angle information, and calculating a control frame of the target acceleration according to the target acceleration information, so as to respectively obtain the steering wheel angle control frame and the target acceleration control frame; converting the steering wheel angle control frame and the target acceleration control frame into formats required by a target control model for outputting according to a preset perfect output strategy; and receiving a steering wheel angle instruction of the vehicle by using the EPS, and receiving a target acceleration instruction of the vehicle by using the ESP so as to control the vehicle to perform real vehicle test according to the steering wheel angle control frame and the target acceleration control frame.
Specifically, in the embodiment of the present application, an input module of a target control model needs to be perfected according to desired track planning information, acceleration following information and steering wheel rotation angle following information of a vehicle, and an output module of the target control model is perfected through a preset perfecting strategy, as shown in fig. 5, in the perfecting process of the input module and the output module of the target control model, in the embodiment of the present application, current position information of the vehicle, vehicle chassis information on a CAN bus and current gear information are acquired according to a CANape input module in Simulink software, and a fixed desired path is acquired; secondly, calculating feedback front wheel steering angle information of the vehicle according to an LQR control algorithm, and calculating feedforward front wheel steering angle information of the vehicle according to a steady-state error elimination formula, so that the feedback front wheel steering angle information and the feedforward front wheel steering angle information are added to obtain the front wheel steering angle information of the vehicle, and the front wheel steering angle information is converted into steering wheel steering angle information of the vehicle; calculating feedback acceleration information according to a displacement-speed PID and a speed-acceleration PID control algorithm, and obtaining feedforward acceleration information from expected acceleration of expected track planning information of the vehicle, so as to add the feedback acceleration information and the feedforward acceleration information to obtain target acceleration information of the vehicle; thirdly, according to a CANunpack module and a CANpack module in Simulink software, calculating a checksum value sent to an EPS and an ESP control frame in real time, calculating an Alive counter value in real time according to a cyclic thought, and sending a control frame whole frame signal to a CAN bus according to a CANape output module to obtain a steering wheel angle control frame of a vehicle and a target acceleration control frame of the vehicle, and simultaneously, converting the steering wheel angle control frame and the target acceleration control frame into formats required by a target control model to be output according to a preset perfect output strategy, so that output conditions are provided for real vehicle testing; and finally, receiving a steering wheel angle instruction of the vehicle by the EPS, and receiving a target acceleration instruction of the vehicle by the ESP, so as to control the vehicle to perform a real vehicle test according to the steering wheel angle control frame and the target acceleration control frame after format conversion.
Further, after the target control model is built, the target control model is compiled according to the CANape rapid prototype implementation method, and the target control model is compiled into the A2L calibration file and the dll program file required by debugging.
In step S104, the calibration file and the program file are loaded into a preset test software, and the real vehicle test is performed by using the algorithm to be tested, so as to obtain the real vehicle test result of the target control model.
The preset test software may be test software adopted by a person skilled in the art according to actual test requirements, and is not specifically limited herein.
Specifically, the embodiment of the application loads the obtained A2L calibration file and dll program file to preset test software, such as CANape software, sends a control instruction in a PC (personal computer) provided with the CANape software, and simultaneously monitors and calibrates the vehicle in real time by utilizing a to-be-tested algorithm so as to realize the test of a transverse control algorithm and a longitudinal control algorithm in the real vehicle, and finally obtains the real vehicle test result of the target control model.
In summary, the embodiments of the present application can produce the following effects by introducing the concept of rapid prototyping test, trajectory acquisition and tracking control based on casape:
(1) The real vehicle collects positioning data and processes the positioning data to obtain a planned track, namely a fixed expected track of the vehicle, so that the problem that an upstream planning algorithm is imperfect and cannot be input is solved, and the control model can be unfolded and verified.
(2) The tracking thought is introduced, the acquired fixed expected track is input into the target control model under the condition that the upstream planning algorithm is imperfect and the real-time updated track cannot be acquired, the target control model can develop effect debugging in advance by tracking the section of track, and the tracking effect of the control model under multiple scenes can be verified by tracking the expected track of different scenes.
According to the rapid prototyping-based real vehicle testing method, through track processing is conducted on acquired vehicle positioning data, expected track planning information to be tracked by a vehicle is obtained, a target control model is built and compiled based on the expected track planning information and a preset track tracking algorithm, calibration files and program files required by debugging are obtained, meanwhile, the files are loaded to preset testing software, real vehicle testing is conducted through the to-be-tested algorithm, and a real vehicle testing result of the target control model is obtained. Therefore, the problems of long test flow period, low reliability, high development cost and the like caused by imperfect upstream planning algorithm and random path input in the test process of the vehicle are solved, and the control model can be used for conducting verification in advance by introducing the track acquisition and tracking control ideas, and the tracking effect under multiple scenes can be achieved when the tracks of different scenes are tracked.
Next, a rapid prototyping-based real vehicle testing apparatus according to an embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 6 is a block schematic diagram of a rapid prototyping-based real vehicle testing apparatus in accordance with an embodiment of the present application.
As shown in fig. 6, the rapid prototyping-based real vehicle testing apparatus 10 includes: an acquisition module 100, a preprocessing module 200, a construction module 300 and a test module 400.
The acquiring module 100 is configured to acquire positioning data of a vehicle acquired in advance;
the preprocessing module 200 is used for performing track processing on the positioning data to obtain expected track planning information of the vehicle;
the construction module 300 is configured to construct a target control model based on the expected trajectory planning information and a preset trajectory tracking algorithm, and compile the target control model to obtain a calibration file and a program file required for debugging; and
the test module 400 is configured to load the calibration file and the program file into preset test software, and perform real vehicle test by using the algorithm to be tested, so as to obtain a real vehicle test result of the target control model.
Further, in some embodiments, the preprocessing module 200 is specifically configured to:
converting the positioning data into a UTM coordinate system based on a preset coordinate conversion algorithm to obtain UTM positioning data;
And carrying out track smoothing on the UTM positioning data based on a preset track processing strategy, and calculating to obtain expected track planning information of the vehicle according to the processed UTM positioning data.
Further, in some embodiments, the building block 300 is specifically configured to:
based on expected track planning information of the vehicle, longitudinal acceleration following control and transverse steering wheel corner following control are respectively carried out on the vehicle by utilizing a preset track tracking algorithm, so that acceleration following information and steering wheel corner following information of the vehicle are obtained;
and improving an input module of a target control model according to the expected track planning information, the acceleration following information and the steering wheel rotation angle following information of the vehicle, and improving an output module of the target control model according to a preset improvement strategy to obtain the target control model.
Further, in some embodiments, after obtaining the acceleration following information and the steering wheel angle following information of the vehicle, the construction module 300 is further configured to:
verifying a longitudinal control algorithm of the vehicle based on the acceleration following information of the vehicle to obtain a longitudinal control algorithm effect of the vehicle;
and verifying a transverse control algorithm of the vehicle based on steering wheel rotation angle following information of the vehicle to obtain the transverse control algorithm effect of the vehicle.
Further, in some embodiments, the building block 300 is specifically configured to:
acquiring target input information of a vehicle;
calculating target acceleration information of the vehicle according to a longitudinal control algorithm based on the target input information, and calculating steering wheel angle information of the vehicle according to a transverse control algorithm;
calculating a control frame of the steering wheel angle according to the steering wheel angle information, and calculating a control frame of the target acceleration according to the target acceleration information, so as to respectively obtain the steering wheel angle control frame and the target acceleration control frame;
converting the steering wheel angle control frame and the target acceleration control frame into formats required by a target control model for outputting according to a preset perfect output strategy;
and receiving a steering wheel angle instruction of the vehicle by using the EPS, and receiving a target acceleration instruction of the vehicle by using the ESP so as to control the vehicle to perform real vehicle test according to the steering wheel angle control frame and the target acceleration control frame.
Further, in some embodiments, after acquiring the pre-acquired positioning data of the vehicle, the acquisition module 100 is further configured to:
collecting chassis data and gear information of a vehicle;
and controlling the vehicle to debug based on the positioning data, the chassis data and the gear information of the vehicle.
According to the rapid prototyping-based real vehicle testing device, through track processing on acquired vehicle positioning data, expected track planning information to be tracked by a vehicle is obtained, a target control model is constructed and compiled based on the expected track planning information and a preset track tracking algorithm, calibration files and program files required by debugging are obtained, meanwhile, the files are loaded to preset testing software, and real vehicle testing is carried out by utilizing the algorithm to be tested, so that a real vehicle testing result of the target control model is obtained. Therefore, the problems of long test flow period, low reliability, high development cost and the like caused by imperfect upstream planning algorithm and random path input in the test process of the vehicle are solved, and the control model can be used for conducting verification in advance by introducing the track acquisition and tracking control ideas, and the tracking effect under multiple scenes can be achieved when the tracks of different scenes are tracked.
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:
memory 701, processor 702, and computer programs stored on memory 701 and executable on processor 702.
The processor 702 implements the rapid prototyping-based real vehicle testing method provided in the above embodiments when executing a program.
Further, the electronic device further includes:
a communication interface 703 for communication between the memory 701 and the processor 702.
Memory 701 for storing a computer program executable on 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 executed by a processor, implements the rapid prototyping-based real vehicle testing method as above.
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 executable instructions for implementing specific logical functions or steps of the process, and additional 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 from that shown or discussed, 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 real vehicle testing method based on the rapid prototyping is characterized by comprising the following steps of:
acquiring positioning data of a vehicle acquired in advance;
performing track processing on the positioning data to obtain expected track planning information of the vehicle;
constructing a target control model based on the expected track planning information and a preset track tracking algorithm, and compiling the target control model to obtain a calibration file and a program file required by debugging; and
and loading the calibration file and the program file into preset test software, and performing real vehicle test by using a to-be-tested algorithm to obtain a real vehicle test result of the target control model.
2. The method of claim 1, wherein performing trajectory processing on the positioning data to obtain desired trajectory planning information for the vehicle comprises:
Converting the positioning data into a universal UTM coordinate system of a cross ink card grid system based on a preset coordinate conversion algorithm to obtain UTM positioning data;
and carrying out track smoothing on the UTM positioning data based on a preset track processing strategy, and calculating to obtain expected track planning information of the vehicle according to the processed UTM positioning data.
3. The method of claim 2, wherein the constructing a target control model based on the desired trajectory planning information and the preset trajectory tracking algorithm comprises:
based on the expected track planning information of the vehicle, utilizing the preset track tracking algorithm to respectively carry out longitudinal acceleration following control and transverse steering wheel corner following control on the vehicle to obtain acceleration following information and steering wheel corner following information of the vehicle;
and perfecting an input module of the target control model according to expected track planning information, acceleration following information and steering wheel rotation angle following information of the vehicle, and perfecting an output module of the target control model according to the preset perfecting strategy to obtain the target control model.
4. A method according to claim 3, further comprising, after obtaining the acceleration following information and the steering wheel angle following information of the vehicle:
verifying a longitudinal control algorithm of the vehicle based on the acceleration following information of the vehicle to obtain a longitudinal control algorithm effect of the vehicle;
and verifying a transverse control algorithm of the vehicle based on the steering wheel rotation angle following information of the vehicle to obtain the transverse control algorithm effect of the vehicle.
5. A method according to claim 3, wherein said perfecting an input module of said target control model based on desired trajectory planning information, said acceleration following information and said steering wheel angle following information of said vehicle, and perfecting an output module of said target control model based on said preset perfecting strategy, comprises:
acquiring target input information of the vehicle;
calculating target acceleration information of the vehicle according to the longitudinal control algorithm based on the target input information, and calculating steering wheel angle information of the vehicle according to the transverse control algorithm;
calculating a control frame of the steering wheel angle according to the steering wheel angle information, and calculating a control frame of the target acceleration according to the target acceleration information to respectively obtain the steering wheel angle control frame and the target acceleration control frame;
Converting the steering wheel angle control frame and the target acceleration control frame into formats required by a target control model to be output according to a preset perfect output strategy;
and receiving a steering wheel angle instruction of the vehicle by using an Electric Power Steering (EPS) system, and receiving a target acceleration instruction of the vehicle by using a vehicle body electronic stability system (ESP) system so as to control the vehicle to perform a real vehicle test according to the steering wheel angle control frame and the target acceleration control frame.
6. The method of claim 1, further comprising, after collecting the positioning data of the vehicle:
acquiring chassis data and gear information of the vehicle;
and controlling the vehicle to debug based on the positioning data, chassis data and gear information of the vehicle.
7. A rapid prototyping-based real vehicle testing apparatus, comprising:
the acquisition module is used for acquiring the positioning data of the vehicle acquired in advance;
the preprocessing module is used for carrying out track processing on the positioning data to obtain expected track planning information of the vehicle;
the construction module is used for constructing a target control model based on the expected track planning information and a preset track tracking algorithm, compiling the target control model and obtaining a calibration file and a program file required by debugging; and
And the test module is used for loading the calibration file and the program file into preset test software, and carrying out real vehicle test by utilizing a to-be-tested algorithm to obtain a real vehicle test result of the target control model.
8. The apparatus according to claim 7, wherein the preprocessing module is specifically configured to:
converting the positioning data into a UTM coordinate system based on a preset coordinate conversion algorithm to obtain UTM positioning data;
and carrying out track smoothing on the UTM positioning data based on a preset track processing strategy, and calculating to obtain expected track planning information of the vehicle according to the processed UTM positioning data.
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 rapid prototyping-based real vehicle testing method of any one of claims 1-6.
10. A computer readable storage medium having stored thereon a computer program, the program being executable by a processor for implementing a rapid prototyping-based real-vehicle testing method as claimed in any one of claims 1-6.
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