CN112925221A - Auxiliary driving closed loop test method based on data reinjection - Google Patents
Auxiliary driving closed loop test method based on data reinjection Download PDFInfo
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Abstract
The invention discloses an auxiliary driving closed loop test method based on data reinjection, which comprises the following steps: step 1, analyzing and splitting the collected real vehicle data, and extracting scene data; step 2, sending the scene data to an ADAS algorithm module, processing, arbitrating and deciding by the ADAS algorithm module according to the received data, then sending a control instruction to a vehicle dynamics model, finishing a series of actions of the vehicle by the vehicle dynamics model according to the received control instruction, and then sending the vehicle information to a bus system module; the ADAS algorithm module acquires corresponding signals from the bus system module according to the required vehicle information, matches and connects the signals with the bus system module, and inputs the vehicle information and the scene data into the ADAS algorithm module together to form closed-loop simulation. The method and the device improve the use efficiency and the simulation value of the data by using the scene data for multiple times and using the same data to carry out multiple rounds of iterative tests on the ADAS algorithm.
Description
Technical Field
The invention belongs to the technical field of automobile testing, and particularly relates to a driving assistance closed loop testing method based on data reinjection.
Background
With the increasing popularity of Advanced Driving Assistance Systems (ADAS), the importance of the systems is increasing. However, as the user's requirements for the system become higher and higher, the ADAS is integrated with more and more functions, so that the real vehicle testing becomes more and more difficult, and even dangerous.
When each whole vehicle factory (component manufacturer) performs real vehicle testing, a large amount of data is recorded, and a lot of data is not effectively utilized, so that great resource waste is caused. In order to avoid the problem of real-time test risk and effectively utilize the existing data, many whole car factories try to utilize the acquired data to perform open-loop tests, and the existing mainstream open-loop test method has the following disadvantages:
(1) the test is completely open loop with no closed loop response similar to a real vehicle. The sensing information, the vehicle information and the like are data which are actually collected by the vehicle and cannot be changed, and the significance of open-loop testing is not great.
(2) All data are actually existed and cannot be modified, and data resources are wasted due to repeated tests.
(3) Some open-loop tests need to collect data separately, and the original data is not utilized for testing, so that the part of the work is more resource-consuming.
(4) In the loop test, the simulation precision is low and the simulation effect is poor.
Therefore, it is necessary to develop a new driving assistance closed loop test method based on data reinjection.
Disclosure of Invention
The invention aims to provide a driving assistance closed loop test method based on data reinjection, which improves the use efficiency and the simulation value of data by using scene data for multiple times and performing multiple rounds of iterative tests on an ADAS algorithm by using the same data.
The invention relates to a driving assistance closed loop test method based on data reinjection, which comprises an ADAS algorithm module, a vehicle dynamics model and a bus system module, wherein the vehicle dynamics model is respectively connected with the ADAS algorithm module and the bus system module, and the method comprises the following steps:
step 1, analyzing and splitting the collected real vehicle data, and extracting scene data to form a scene database;
step 2, after the scene data are ready, sending the scene data to an ADAS algorithm module, processing, arbitrating and deciding the scene data by the ADAS algorithm module according to the received data, then sending a control command to a vehicle dynamics model, finishing a series of actions of the vehicle by the vehicle dynamics model according to the received control command, and then sending the vehicle information to a bus system module; the ADAS algorithm module acquires corresponding signals from the bus system module according to the required vehicle information, matches and connects the signals with the bus system module, and inputs the vehicle information and the scene data into the ADAS algorithm module together to form closed-loop simulation.
Further, other data including cloud node data and V2X communication data are also included, and are input to the ADAS algorithm module together with vehicle information and scene data.
Further, the accuracy of the vehicle dynamics model is above 80%.
Further, when scene data is input, a sampling frequency is set for each signal, and the sampling frequency is consistent with the real vehicle acquisition frequency.
The invention has the following advantages: the method can repeatedly utilize the scene data, form the scene data file into a scene library file, and perform multiple rounds of iterative tests on the ADAS algorithm. Meanwhile, the larger the sample size of the real vehicle test is, the more and more perfect the accumulated scene library is. By repeatedly using the scene database file, the use efficiency of the data is improved, and the simulation value is improved.
Drawings
Fig. 1 is a closed loop simulation block diagram in the present embodiment.
Detailed Description
The invention will be further explained with reference to the drawings.
As shown in fig. 1, a driving assistance closed loop test method based on data reinjection includes an ADAS algorithm module, a vehicle dynamics model and a bus system module, where the vehicle dynamics model is connected to the ADAS algorithm module and the bus system module, respectively, and the method includes the following steps:
step 1, analyzing and splitting the collected real vehicle data, and extracting scene data to form a scene database; the method specifically comprises the following steps:
and analyzing the real vehicle data according to the collected equipment types and protocols. After data analysis, the data are divided into categories such as scene data, vehicle information, fault diagnosis information and the like according to the protocol content. And extracting and splitting scene data contents from real vehicle data. In order to improve the universality and the application range of the simulation method, the related scene related information must be covered. Each signal in the scene data has a respective sampling period, so that the number of each signal may be different for data recorded in the same time period. This requires that a sampling frequency be set for each signal when the scene data is input, which sampling frequency is consistent with the real vehicle acquisition frequency to ensure that the data can be correctly matched when the scene data is fed into the ADAS algorithm module.
Step 2, after the scene data are ready, sending the scene data to an ADAS algorithm module, processing, arbitrating and deciding the scene data by the ADAS algorithm module according to the received data, then sending a control command to a vehicle dynamics model, finishing a series of actions of the vehicle by the vehicle dynamics model according to the received control command, and then sending the vehicle information to a bus system module; the ADAS algorithm module acquires corresponding signals from the bus system module according to the required vehicle information, matches and connects the signals with the bus system module, and inputs the vehicle information and the scene data into the ADAS algorithm module together to form closed-loop simulation.
As the ADAS functions are more and more, the signals are more and more complex, and the sources of the signals are diversified. In addition to the information collected by the vehicle, in order to be suitable for simulation of future ADAS algorithm, other data sources outside the vehicle, such as cloud node data, V2X communication data and the like, can be added to assist the test to perform certain special functions. This portion of data is optional and can be ignored if there is no particular need. And if other data are needed, inputting the other data, the vehicle information and the scene data into the ADAS algorithm module.
In this embodiment, after the scene data, other data, and vehicle information are ready, all signals are sent to the external interface of the ADAS algorithm module according to a certain structural hierarchy. The interface signals are uniformly subjected to rate synchronization according to a transmission rule, and the received data volume of the ADAS algorithm module in unit time is equal to the actually required data volume. When the method is used for loading the ADAS algorithm, the frequency of the ADAS algorithm is required to be the same as that of the algorithm loaded by an actual vehicle, so that the correctness of the operations such as integration, differentiation and the like in the ADAS algorithm can be ensured. The ADAS algorithm module then outputs the correct control commands to the vehicle dynamics model. This part is the main subject of closed loop simulation testing.
In the embodiment, the vehicle dynamics model is the key of simulation precision, the vehicle dynamics model is built in the prior art, and different components are selected according to the types of vehicles (such as fuel vehicles, electric vehicles, hybrid vehicles and the like) loaded by the ADAS algorithm. The built vehicle dynamic model needs to meet certain precision according to requirements, and generally reaches more than 80%. The vehicle dynamics model greatly helps performance simulation of the ADAS algorithm, and data of vehicle response has guiding significance on revision of the ADAS algorithm in the simulation process of the ADAS algorithm. The ADAS algorithm outputs torque, brake and steering signals, and the vehicle dynamics model needs to output tens or hundreds of signals, such as: vehicle speed, lateral longitudinal acceleration, steering wheel steering angle, steering wheel angular rate, steering wheel torque, engine speed, engine power, master cylinder pressure, wheel cylinder pressure, output torque, wheel speed, gear, YawRate, and course angle, among others.
In this embodiment, the vehicle information is obtained from the bus system module, and the vehicle information is obtained by sorting according to the information structure of the real vehicle controller. The vehicle information is a signal set with a logic hierarchy, the vehicle information comprises a plurality of electronic controller nodes, each electronic controller node comprises a plurality of signals, and each signal is generated by a corresponding module of a vehicle dynamic model. And the vehicle dynamics model can continuously call the internal solver to calculate according to the received control command. Modules in the vehicle dynamics model can work cooperatively to obtain required vehicle information, and the vehicle information is constantly in the updating process.
In this embodiment, the bus system module configures signals output by the vehicle dynamics model into different virtual electronic controllers according to the CAN or Ethernet protocol. All the related virtual electronic controllers are mounted on the bus system module through the bus, and the virtual electronic controllers can communicate with each other. The bus system module interacts with the vehicle information, the vehicle information at the moment is transmitted to the ADAS algorithm module, and data form a closed loop.
By using the method, the existing acquired data can be effectively utilized to test the function and performance of the ADAS algorithm.
Claims (4)
1. A driving assistance closed loop test method based on data reinjection is characterized by comprising an ADAS algorithm module, a vehicle dynamics model and a bus system module, wherein the vehicle dynamics model is respectively connected with the ADAS algorithm module and the bus system module, and the method comprises the following steps:
step 1, analyzing and splitting the collected real vehicle data, and extracting scene data to form a scene database;
step 2, after the scene data are ready, sending the scene data to an ADAS algorithm module, processing, arbitrating and deciding the scene data by the ADAS algorithm module according to the received data, then sending a control command to a vehicle dynamics model, finishing a series of actions of the vehicle by the vehicle dynamics model according to the received control command, and then sending the vehicle information to a bus system module; the ADAS algorithm module acquires corresponding signals from the bus system module according to the required vehicle information, matches and connects the signals with the bus system module, and inputs the vehicle information and the scene data into the ADAS algorithm module together to form closed-loop simulation.
2. The data reinjection-based aided driving closed-loop test method according to claim 1, characterized in that: other data including cloud node data and V2X communication data are also included, which are input to the ADAS algorithm module along with vehicle information, scene data.
3. The data-reinjection-based aided driving closed-loop test method according to claim 1 or 2, characterized in that: the accuracy of the vehicle dynamics model is above 80%.
4. The data reinjection-based aided driving closed-loop test method according to claim 3, characterized in that: when scene data is input, a sampling frequency is set for each signal, and the sampling frequency is consistent with the real vehicle acquisition frequency.
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Cited By (3)
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| CN113467429A (en) * | 2021-07-23 | 2021-10-01 | 重庆长安汽车股份有限公司 | Real vehicle scene reinjection system and method based on PCAN-USB and ADAS controller |
| CN114326443A (en) * | 2022-01-14 | 2022-04-12 | 重庆长安汽车股份有限公司 | MIL simulation test method and system for ADAS and readable storage medium |
| CN114488854A (en) * | 2022-01-26 | 2022-05-13 | 上海和夏新能源科技有限公司 | Intelligent driving and ADAS analog simulation method and system based on test data |
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