CN112558581A - Test method and test device for advanced driving assistance system function - Google Patents
Test method and test device for advanced driving assistance system function Download PDFInfo
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- CN112558581A CN112558581A CN201910919741.2A CN201910919741A CN112558581A CN 112558581 A CN112558581 A CN 112558581A CN 201910919741 A CN201910919741 A CN 201910919741A CN 112558581 A CN112558581 A CN 112558581A
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- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
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Abstract
The application discloses a method and a device for testing functions of an advanced driving assistance system, wherein road test data of a road test vehicle are obtained, and the road test data comprise CAN bus data and ADAS sensor data; and calculating the road test data according to a problem model, screening out the problem data in the road test data, and generating a test result of the advanced driving auxiliary system function of the road test vehicle according to the problem data. When the function of the advanced driving assistance system is tested, not only CAN bus data but also ADAS sensor data are utilized, so that the utilization rate of the ADAS sensor data is improved, and a test result is more accurate.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for testing functions of an advanced driving assistance system.
Background
An Intelligent internet Vehicle (ICV) (integrated Vehicle) is a new generation Vehicle organically combining an internet of vehicles and an Intelligent Vehicle, and a set of Advanced Driving Assistance System (ADAS) is mounted on the Vehicle, and various sensors mounted on the Vehicle are used for sensing surrounding environments at any time during the Driving process of the Vehicle, collecting data, combining with navigator map data, performing systematic operation and analysis, determining road conditions according to analysis results, and increasing the safety of Vehicle Driving. Therefore, before mass production of automobiles, the ADAS needs to be subjected to function testing, and the safety of the driving process is ensured.
In the prior art, most of test results are obtained according to the driving feeling judgment of a tester when the ADAS is subjected to the function test. In the test process, different testers have different subjective standards, different test results can be obtained in the same test process, the accuracy of the test results cannot be ensured, and the road test data in the road test process is wasted.
Disclosure of Invention
Based on the above problems, the present application provides a method and a device for testing functions of an advanced driving assistance system, so that the test result of the functions of the advanced driving assistance system is more accurate.
The embodiment of the application discloses the following technical scheme:
in a first aspect, the present application provides a method for testing advanced driving assistance system function, including:
acquiring road test data of a road test vehicle, wherein the road test data comprises CAN bus data and ADAS sensor data;
calculating the road test data according to a problem model, and screening out problem data in the road test data;
and generating a test result of the advanced driving assistance system function of the road test vehicle according to the problem data.
Optionally, the acquiring the road test data of the road test vehicle specifically includes:
and acquiring and fusing the CAN bus data and the ADAS sensor data to obtain the road test data of the road test vehicle.
Optionally, the performing the problem test on the road test data further includes, before determining the problem data in the road test data:
and preprocessing the road test data to remove non-numerical value dead pixels and/or micro bump data in the road test data.
Optionally, the operating the road test data according to the problem model, and screening out problem data in the road test data includes:
importing the road test data into the problem model, and traversing the logic branch of the problem model;
and if the logic branch test of the problem model is not passed, returning the problem data and exiting the logic branch test.
Optionally, the logical branch comprises at least one of: signal state logic branch, threshold setting logic branch.
Optionally, the problem model comprises at least one of: the road test vehicle comprises a sensor perception data verification model, a driving feeling and/or functional rationality decision verification model and a controller decision output verification model of the road test vehicle.
Optionally, the method further comprises: and analyzing and visualizing the test result of the road test vehicle through a chart area and/or a data pivot table.
In a second aspect, the present application provides a device for testing advanced driving assistance system functions, comprising:
the data acquisition module is used for acquiring road test data of a road test vehicle, wherein the road test data comprises CAN bus data and ADAS sensor data;
the data operation module is used for operating the road test data according to a problem model and screening out problem data in the road test data;
and the result generation module is used for generating a test result of the advanced driving assistance system function of the road test vehicle according to the problem data.
Optionally, the acquiring device is specifically configured to:
and acquiring and fusing the CAN bus data and the ADAS sensor data to obtain the road test data of the road test vehicle.
Optionally, the apparatus further includes a preprocessing module, configured to perform a preprocessing operation on the road test data, and remove non-numerical bad points and/or micro-bump data in the road test data.
Optionally, the data operation module is further configured to:
importing the road test data into the problem model, and traversing the logic branch of the problem model;
and if the logic branch test of the problem model is not passed, returning the problem data and exiting the logic branch test.
Optionally, the logical branch comprises at least one of: signal state logic branch, threshold setting logic branch.
Optionally, the problem model comprises at least one of: the road test vehicle comprises a sensor perception data verification model, a driving feeling and/or functional rationality decision verification model and a controller decision output verification model of the road test vehicle.
Optionally, the device further includes a visualization module, configured to analyze and visualize the test result of the road test vehicle through a chart area and/or a pivot table.
Compared with the prior art, the method has the following beneficial effects:
according to the method and the device for testing the function of the advanced driving assistance system, the road test data of the road test vehicle is obtained, and the road test data comprises CAN bus data and ADAS sensor data; and calculating the road test data according to a problem model, screening out the problem data in the road test data, and generating a test result of the advanced driving auxiliary system function of the road test vehicle according to the problem data. When the function of the advanced driving assistance system is tested, not only CAN bus data but also ADAS sensor data are utilized, so that the utilization rate of the ADAS sensor data is improved, and a test result is more accurate.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a flowchart of a method for testing functions of an advanced driving assistance system according to an embodiment of the present application.
Fig. 2 is a flowchart of a method for testing functions of an advanced driving assistance system according to a second embodiment of the present application.
Fig. 3 is a flowchart of a method for testing functions of an advanced driving assistance system according to a third embodiment of the present application.
Fig. 4 is a flowchart of a method for testing functions of an advanced driving assistance system according to a fourth embodiment of the present application.
Fig. 5 is a flowchart of a method for testing functions of an advanced driving assistance system according to a fifth embodiment of the present application.
Fig. 6 is a block diagram of a test apparatus for testing functions of an advanced driving assistance system according to a sixth embodiment of the present application.
Fig. 7 is a block diagram of a device for testing functions of an advanced driving assistance system according to a seventh embodiment of the present application.
Fig. 8 is a block diagram of a test apparatus for testing functions of an advanced driving assistance system according to an eighth embodiment of the present application.
Fig. 9 is a block diagram of a device for testing functions of an advanced driving assistance system according to a ninth embodiment of the present application.
Fig. 10 is a block diagram of a test apparatus for testing functions of an advanced driving assistance system according to a tenth embodiment of the present application.
Detailed Description
As described above, most of the test results are judged according to the driving feeling of the tester when the ADAS is subjected to the function test. In the test process, different testers have different subjective standards, different test results can be obtained in the same test process, the accuracy of the test results cannot be ensured, and the road test data in the road test process is wasted.
Based on the above problems, the inventors have studied and provided a method and an apparatus for testing the functions of an advanced driving assistance system. The method is specifically performed by a test device. The method comprises the steps that road test data of a road test vehicle are obtained, wherein the road test data comprise CAN bus data and ADAS sensor data; and calculating the road test data according to a problem model, screening out the problem data in the road test data, and generating a test result of the advanced driving auxiliary system function of the road test vehicle according to the problem data. When the function of the advanced driving assistance system is tested, not only CAN bus data but also ADAS sensor data are utilized, so that the utilization rate of the ADAS sensor data is improved, and a test result is more accurate.
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
First embodiment
Referring to fig. 1, the figure is a flowchart of a method for testing functions of an advanced driving assistance system according to an embodiment of the present application.
As shown in fig. 1, the method for testing the advanced driving assistance system function provided in this embodiment includes:
and S102, obtaining road test data of the road test vehicle, wherein the road test data comprises CAN bus data and ADAS sensor data.
In this embodiment, the CAN bus data includes Controller Area Network (CAN) bus data of the road test vehicle. The ADAS sensor data comprises sensor data of advanced driving assistance systems of the road test vehicle, such as lidar data, camera data, etc.
In a specific embodiment, a CAN card and a data fusion acquisition industrial personal computer may be used to respectively acquire the CAN bus data and the ADAS sensor data, and certainly, in other embodiments, the road test data may also be acquired in other manners, which is not limited herein.
In this embodiment, in view of the problem that the advanced driving assistance system function is determined by the driving feeling of a tester mostly when the advanced driving assistance system function is tested in the prior art, the ADAS sensor data utilization rate is low. Therefore, the road test data obtained by the testing method of the application not only comprises the CAN bus data, but also comprises the ADAS sensor data, so that the utilization rate and the value of the ADAS sensor data are greatly improved, and the defect of low data utilization rate of the ADAS sensor in the prior art is overcome.
And S104, calculating the road test data according to a problem model, and screening out problem data in the road test data.
In this embodiment, when the advanced driving assistance system is tested, various tests may be performed, for example, a driving smoothness test, a following distance stability test, and the like. When the advanced driving assistance system is subjected to multiple tests, the road test data can be calculated by using one problem model, so that the problem data corresponding to each test can be screened out, and the road test data can also be calculated by using one problem model for each test, so that the problem data corresponding to each test can be screened out.
In this embodiment, it is automatic right through the problem model way examination data operate, obtain the problem data no longer need the tester to judge whether way examination data is the problem data, reduced the human cost in the test process, improved efficiency of software testing, also avoided the tester to judge in addition whether way examination data is the problem data, omit the problem data, guaranteed the accuracy of test result.
And S106, generating a test result of the advanced driving assistance system function of the road test vehicle according to the problem data.
In this embodiment, the test result includes the precise time point when the problem data occurs, the name and the number of the problem data, the signal value of the problem data, and at least one of the controllers that generate the problem data, so that the precise positioning of the problem data and the automatic statistics of the problem severity are realized, and the test efficiency and the precision of the test result are improved.
In this embodiment, the test result is obtained by calculating the road test data, and the result obtained by the tester according to the driving experience can be judged, so that divergence caused by different driving experiences when different testers test the same problem is avoided, and the consistency of the test result is ensured. In addition, the road test data acquisition, the screening of the road test data to obtain the problem point data and the generation of the test result are all automatically completed, and the data processing efficiency of the whole test process is obviously improved.
According to the method for testing the function of the advanced driving assistance system, the road test data of a road test vehicle are obtained, wherein the road test data comprise CAN bus data and ADAS sensor data; and calculating the road test data according to a problem model, screening out the problem data in the road test data, and generating a test result of the advanced driving auxiliary system function of the road test vehicle according to the problem data. When the function of the advanced driving assistance system is tested, not only CAN bus data but also ADAS sensor data are utilized, so that the utilization rate of the ADAS sensor data is improved, and a test result is more accurate.
On the basis of the foregoing embodiments, the present application further provides another method for testing the functions of the advanced driving assistance system. Specific implementations of the method are described and illustrated below with reference to the examples and the figures.
Second embodiment
Referring to fig. 2, it is a flowchart of a method for testing functions of an advanced driving assistance system according to a second embodiment of the present application. The method for testing the advanced driving assistance system function provided in this embodiment includes the aforementioned steps S102 to S106.
As shown in fig. 2, in this embodiment, the step S102 specifically includes: and acquiring and fusing the CAN bus data and the ADAS sensor data to obtain the road test data of the road test vehicle.
In this embodiment, because the sources of the CAN bus data and the ADAS sensor data are different, the data formats of the two data and the timestamps of the collected data may also be different, and the data cannot be processed when the road test data is calculated. Therefore, the CAN bus data and the ADAS sensor data are subjected to fusion processing, the formats and the time stamps of the two data are unified, the road test data are obtained, and the operation on the road test data is convenient.
It should be noted that, the CAN bus data may be used as a standard, and certainly, the ADAS sensor data may also be used as a standard, or a standard is determined by itself to unify the format and the timestamp of the two data, which is not limited herein. For example, the CSV format is widely used for exchanging data table information between applications with different architectures, can solve the problem of interworking of incompatible data formats, and can unify the formats of two kinds of data into the CSV format.
In this embodiment, the road test data is obtained by fusing the CAN bus data and the ADAS sensor data, so that the utilization rate and value of the ADAS sensor data are greatly improved, and the defect of low data utilization rate of the ADAS sensor in the prior art is overcome.
On the basis of the foregoing embodiments, the present application further provides another method for testing the functions of the advanced driving assistance system. Specific implementations of the method are described and illustrated below with reference to the examples and the figures.
Third embodiment
Referring to fig. 3, it is a flowchart of a method for testing functions of an advanced driving assistance system according to a third embodiment of the present application. The method for testing the advanced driving assistance system function provided in this embodiment includes the aforementioned steps S102 to S106.
Step S102 may adopt the implementation manner described in embodiment two, or adopt other implementation manners.
As shown in fig. 3, in this embodiment, before the step S104, the method further includes:
step S103, preprocessing the road test data, and removing non-numerical value dead pixels and/or micro bump data in the road test data.
In this embodiment, some blank spots may appear when data collection is performed, and letters or symbols, rather than numbers, may be displayed in the road test data. Therefore, by carrying out preprocessing operation on the road test data, non-numerical value bad points in the road test data are removed, the influence of the non-numerical value bad points on the test result is avoided, and the accuracy of the test result is ensured.
In a specific embodiment, the data cleaning script may be used to perform numerical detection on the road test data, and remove non-numerical bad points detected. Of course, other ways may also be used to remove the non-numerical bad point in the road test data, which is not limited herein.
In this embodiment, when data collection is performed, there may be some micro-bump data in the road test data for some reasons. For example, when the road test vehicle is in a stationary state, the acceleration sensor of the road test vehicle may output a little jump data due to the vibration of the road test vehicle, which may vary from 0 to 0.03. Therefore, the micro bumping data in the road test data are removed by preprocessing the road test data, so that the influence of the micro bumping data on the test result is avoided, and the accuracy of the test result is ensured.
In a specific embodiment, a frequency domain analysis may be performed on the road test data by using a spectrum analysis method to determine a filter pass band and a rejection band of the road test data, and then a low pass filter may be used to filter the micro-bump data in the road test data according to the filter pass band and the rejection band of the road test data. Of course, the micro-bump data in the road test data may be removed in other ways, which is not limited herein.
It should be noted that, only non-numerical bad points in the road test data may be removed, or only micro-bump data in the road test data may be removed, and preferably, in order to ensure the accuracy of the test result, the non-numerical bad points and the micro-bump data in the road test data are removed at the same time.
On the basis of the foregoing embodiments, the present application further provides another method for testing the functions of the advanced driving assistance system. Specific implementations of the method are described and illustrated below with reference to the examples and the figures.
Fourth embodiment
Referring to fig. 4, it is a flowchart of a method for testing functions of an advanced driving assistance system according to a fourth embodiment of the present application. The method for testing the advanced driving assistance system function provided in this embodiment includes the aforementioned steps S102 to S106. It may or may not include step S103.
Step S102 may adopt the implementation manner described in embodiment two, or adopt other implementation manners.
As shown in fig. 4, in the present embodiment, the step S104 includes the following sub-steps:
step S1041, importing the road test data into the problem model, and traversing the logic branch of the problem model.
And step S1042, if the logic branch test of the problem model is not passed, returning the problem data and exiting the logic branch test.
In this embodiment, the logical branch includes at least one of: signal state logic branch, threshold setting logic branch.
In a specific embodiment, the path test data may be operated through the signal state logic branch, and the signal data in the path test data is compared with the standard signal data in the signal state logic branch. And if the signal data in the road test data is inconsistent with the standard signal data, indicating that the signal data in the road test data does not pass the logic branch test of the problem model, returning the signal data in the road test data as problem data, and exiting the signal state logic branch. And if the signal data in the road test data is consistent with the standard signal data, the signal data in the road test data passes the logic branch test of the problem model, and then a null value is returned and the signal state logic branch is exited. Through the signal state logic branch is used for calculating the road test data, the problem data in the road test data can be accurately positioned, the occurrence time and the occurrence logic reason of the problem data are obtained, the accurate positioning of the problems with the advanced driving assistance function is realized, and the accuracy of the test result is improved.
In a specific embodiment, the road test data may be calculated through the threshold setting logic branch, and the road test data is compared with a corresponding threshold in the threshold setting logic branch. And if the road test data does not exceed the threshold, the road test data passes the test of the threshold setting logic branch of the problem model, a null value is returned, and the threshold setting logic branch is exited. And if the road test data exceeds the threshold, indicating that the road test data does not pass the test of the threshold setting logic branch of the problem model, returning the road test data as problem data, and exiting the threshold setting logic branch. In this embodiment, the threshold is obtained by digitizing the driving experience of the tester, so that the standard is more objective when the problem data is screened, and the objectivity and consistency of the test result are ensured.
In this embodiment, the problem model includes at least one of the following: the road test vehicle comprises a sensor perception data verification model, a driving feeling and/or functional rationality decision verification model and a controller decision output verification model of the road test vehicle.
In this embodiment, the problem that the sensor perception data verification model of the road test vehicle aims at includes the problem that the ADAS controller of the road test vehicle produces the perception of external road conditions environment, for example, the problem whether perception and front vehicle distance are sensitive or not. The problems aimed by the driving feeling and/or function rationality decision checking model comprise the problems of control decisions made by the ADAS controller of the road test vehicle on the sensed external road condition environment, such as the problems of whether the control speed is appropriate when the distance between the adaS controller and the front vehicle is too small, and the like. The problems for the controller decision output verification model include the problems that the instructions transmitted by the ADAS controller of the road test vehicle are taken and executed after reaching other controllers and hardware, for example, the problems that the ADAS controller of the road test vehicle transmits a deceleration instruction, and whether the execution of the brake controller and the accelerator controller is in time are solved.
Preferably, the road test data are respectively imported into a sensor perception data verification model, a driving feeling and/or function rationality decision verification model and a controller decision output verification model of the road test vehicle, so that the comprehensive test of the ADAS problem is realized, and the test result is more accurate.
In a specific embodiment, the standard signal data corresponding to the sensor sensing data verification model of the road test vehicle may be standard sensor data collected from the road test vehicle. For example, a tester may additionally install the same type of sensor with higher accuracy than that of the advanced driving assistance system on the road test vehicle, and then obtain the standard signal data by performing data acquisition on the same type of sensor with higher accuracy. Of course, the setting method of the standard signal data corresponding to the sensor sensing data verification model may adopt other methods, which is not limited herein.
In a specific embodiment, the standard signal data corresponding to the driving experience and/or functional rationality decision-making verification model may be obtained as the standard signal data according to the road test data of the road test vehicle. For example, a tester obtains standard decision output data of the road test vehicle as a control accelerator according to vehicle distance data in vehicle road test data, and the standard decision output data is collated with actual decision output data in the road test data to judge whether the actual decision output data in the road test data is accurate. Of course, the setting method of the standard signal data corresponding to the ride quality and/or functional rationality decision-making verification model may adopt other methods, which are not limited herein.
In a specific embodiment, the standard signal data corresponding to the controller decision output verification model may be total control output data of the road test vehicle. For example, the general control output data of the road test vehicle and the output data of the controller in the road test data are verified, and whether the output data of the controller in the road test data is timely and stable is judged. Of course, the setting method of the standard signal data corresponding to the decision output verification model of the controller may adopt other methods, which is not limited herein.
In a specific embodiment, the threshold corresponding to the sensor-aware data verification model of the road test vehicle may be determined according to at least one of the ADAS development design parameters, ADAS certification standards and specifications at home and abroad, and processing of historical road test data according to a numerical model. The specific steps of processing the historical road test data according to the numerical model are as follows: screening out historical road test data corresponding to a sensor perception data verification model of the road test vehicle from the historical road test data, importing the historical road test data corresponding to the sensor perception data verification model of the road test vehicle into the numerical model, counting the frequency of each signal value in the historical road test data corresponding to the sensor perception data verification model of the road test vehicle, forming a frequency distribution table, determining the distribution condition of the signal values in the problem data in the historical road test data corresponding to the sensor perception data verification model of the road test vehicle in the frequency distribution table, and determining a threshold value capable of covering 65% to 99% of the signal values of the problem data according to the distribution condition.
In this embodiment, the threshold of the problem model is obtained according to the historical road test data and the numerical model, and the driving feeling of the tester is digitized, so that the test result is more objective and accurate.
It should be noted that the threshold values corresponding to the driving experience and/or functional rationality decision-making verification model and the controller decision-making output verification model are similar to the threshold value setting method of the sensor perception data verification model of the road test vehicle, and are not repeated here.
In this embodiment, in the prior art, the problem that the function of the advanced driving assistance system is determined according to the driving feeling of the tester, most of the problems can only be sensed in the decision output aspect of the controller, and the problem of the function of the advanced driving assistance system in the sensor perception aspect and the problem in the driving feeling and/or function rationality decision aspect cannot be tested. Therefore, the logic branch test is carried out on the road test data through the sensor perception data verification model, the driving feeling and/or function rationality decision-making verification model and the controller decision-making output verification model of the road test vehicle, the comprehensive problem test of the functions of the advanced driving auxiliary system is realized, and the obtained test result is more accurate.
Optionally, before the road test data is imported into the problem model, the road test data may be further segmented to obtain road test data corresponding to the problem model, and then the road test data corresponding to the problem model is imported into the problem model. By dividing the road test data, the data dimension is reduced, so that when the problem model carries out logic branch test on the road test data, the data operation amount of the logic branch is simplified, and the test efficiency is improved.
In this embodiment, when the problem data is screened out from the road test data, the problem data is no longer determined according to the driving feeling of a tester, but is screened out from the road test data according to the logic branch test of the problem model, so that the objectivity and consistency of the test result are ensured, and the test result is more accurate.
On the basis of the foregoing embodiments, the present application further provides another method for testing the functions of the advanced driving assistance system. Specific implementations of the method are described and illustrated below with reference to the examples and the figures.
Fifth embodiment
Referring to fig. 5, it is a flowchart of a method for testing functions of an advanced driving assistance system according to a fifth embodiment of the present application. The method for testing the advanced driving assistance system function provided in this embodiment includes the aforementioned steps S102 to S106. It may or may not include step S103.
Step S102 may adopt the implementation manner described in embodiment two, or adopt other implementation manners. Step S104 may adopt the implementation described in the fourth embodiment, or adopt other implementations.
As shown in fig. 5, in this embodiment, after the step S106, the method further includes:
and S107, analyzing and visualizing the test result of the road test vehicle through a chart area and/or a data pivot table.
In this embodiment, the visualization processing may specifically be that at least one of the precise time point of occurrence of the problem data included in the test result, the name and the number of the problem data, the signal value of the problem data, and the controller generating the problem data is displayed to a tester through a chart area and/or a data table, so that the tester can conveniently view the test result and analyze the test result, thereby improving the test efficiency.
Based on the test method for the advanced driving assistance system function provided by the foregoing embodiment, correspondingly, the present application further provides a test device for the advanced driving assistance system function. The following describes a specific implementation of the apparatus with reference to the drawings and examples.
Sixth embodiment
Referring to fig. 6, it is a block diagram of a test apparatus for testing functions of an advanced driving assistance system according to a sixth embodiment of the present application.
As shown in fig. 6, the test apparatus for advanced driving assistance system function according to the present embodiment includes:
the data acquisition module 602 is configured to acquire road test data of a road test vehicle, where the road test data includes CAN bus data and ADAS sensor data.
In this embodiment, the CAN bus data includes Controller Area Network (CAN) bus data of the road test vehicle. The ADAS sensor data comprises sensor data of advanced driving assistance systems of the road test vehicle, such as lidar data, camera data, etc.
In a specific embodiment, a CAN card and a data fusion acquisition industrial personal computer may be used to acquire the CAN bus data and the ADAS sensor data, and of course, in other embodiments, the road test data may be acquired in other manners, which is not limited herein.
In this embodiment, in view of the problem that the advanced driving assistance system function is determined by the driving feeling of a tester mostly when the advanced driving assistance system function is tested in the prior art, the ADAS sensor data utilization rate is low. Therefore, the road test data obtained by the testing method of the application comprises the ADAS sensor data, so that the utilization rate and the value of the ADAS sensor data are greatly improved, and the defect of low data utilization rate of the ADAS sensor in the prior art is overcome.
And a data operation module 604, configured to perform operation on the road test data according to a problem model, and screen out problem data in the road test data.
In this embodiment, when the advanced driving assistance system is tested, various problems may occur, such as a driving smoothness problem and a following distance stability problem. Therefore, the problem model may calculate the road test data for all problems, or may calculate the road test data for a certain problem. When the problem model calculates the road test data for a certain problem, in order to test the advanced driving assistance system for all problems, it is preferable that the number of the problem models is equal to the number of problems.
In this embodiment, it is automatic right through the problem model way examination data operate, obtain the problem data no longer need the tester to judge whether way examination data is the problem data, reduced the human cost in the test process, improved efficiency of software testing, also avoided the tester to judge in addition whether way examination data is the problem data, omit the problem data, guaranteed the accuracy of test result.
And a result generating module 606, configured to generate a test result of the advanced driving assistance system function of the road test vehicle according to the problem data.
In this embodiment, the test result includes the precise time point when the problem data occurs, the name and the number of the problem data, the signal value of the problem data, and at least one of the controllers that generate the problem data, so that the precise positioning of the problem data and the automatic statistics of the problem severity are realized, and the test efficiency and the precision of the test result are improved.
In this embodiment, the test result is obtained by calculating the road test data, and the result obtained by the tester according to the driving experience can be judged, so that divergence caused by different driving experiences when different testers test the same problem is avoided, and the consistency of the test result is ensured. In addition, the road test data acquisition, the screening of the road test data to obtain the problem point data and the generation of the test result are all automatically completed, and the data processing efficiency of the whole test process is obviously improved.
According to the test device for the function of the advanced driving assistance system, the road test data of the road test vehicle is obtained, and the road test data comprises CAN bus data and ADAS sensor data; and calculating the road test data according to a problem model, screening out the problem data in the road test data, and generating a test result of the advanced driving auxiliary system function of the road test vehicle according to the problem data. When the function of the advanced driving assistance system is tested, not only CAN bus data but also ADAS sensor data are utilized, so that the utilization rate of the ADAS sensor data is improved, and a test result is more accurate.
On the basis of the foregoing embodiments, the present application further provides another test apparatus for advanced driving assistance system functions. A specific implementation of the device is described and illustrated below with reference to the embodiments and the accompanying drawings.
Seventh embodiment
Referring to fig. 7, it is a block diagram of a device for testing functions of an advanced driving assistance system according to a seventh embodiment of the present application. The testing apparatus for advanced driving assistance system function provided in this embodiment includes the aforementioned data acquiring module 602, data calculating module 604, and result generating module 606.
As shown in fig. 7, in this embodiment, the data obtaining module 602 is specifically configured to perform collection and fusion processing on the CAN bus data and the ADAS sensor data to obtain road test data of the road test vehicle.
In this embodiment, because the sources of the CAN bus data and the ADAS sensor data are different, the data formats of the two data and the timestamps of the collected data may also be different, and the data cannot be processed when the road test data is calculated. Therefore, the CAN bus data and the ADAS sensor data are subjected to fusion processing, the formats and the time stamps of the two data are unified, the road test data are obtained, and the operation on the road test data is convenient.
It should be noted that, the CAN bus data may be used as a standard, and certainly, the ADAS sensor data may also be used as a standard, or a standard is determined by itself to unify the format and the timestamp of the two data, which is not limited herein. For example, the CSV format is widely used for exchanging data table information between applications with different architectures, can solve the problem of interworking of incompatible data formats, and can unify the formats of two kinds of data into the CSV format.
In this embodiment, the road test data is obtained by fusing the CAN bus data and the ADAS sensor data, so that the utilization rate and value of the ADAS sensor data are greatly improved, and the defect of low data utilization rate of the ADAS sensor in the prior art is overcome.
On the basis of the foregoing embodiments, the present application further provides another test apparatus for advanced driving assistance system functions. A specific implementation of the device is described and illustrated below with reference to the embodiments and the accompanying drawings.
Eighth embodiment
Referring to fig. 8, this figure is a block diagram of a test apparatus for testing functions of an advanced driving assistance system according to an eighth embodiment of the present application. The testing apparatus for advanced driving assistance system function provided in this embodiment includes the aforementioned data acquiring module 602, data calculating module 604, and result generating module 606. The data obtaining module 602 may adopt the specific implementation manner in the seventh embodiment, or adopt other specific implementation manners.
As shown in fig. 8, in this embodiment, the testing apparatus further includes a preprocessing module 603, configured to perform a preprocessing operation on the road test data to remove non-numerical bad points and/or micro-bump data in the road test data.
In this embodiment, some blank spots may appear when data collection is performed, and letters or symbols, rather than numbers, may be displayed in the road test data. Therefore, by carrying out preprocessing operation on the road test data, non-numerical value bad points in the road test data are removed, the influence of the non-numerical value bad points on the test result is avoided, and the accuracy of the test result is ensured.
In a specific embodiment, the data cleaning script may be used to perform numerical detection on the road test data, and remove non-numerical bad points detected. Of course, other ways may also be used to remove the non-numerical bad point in the road test data, which is not limited herein.
In this embodiment, when data collection is performed, there may be some micro-bump data in the road test data for some reasons. For example, when the road test vehicle is in a stationary state, the acceleration sensor of the road test vehicle may output a little jump data due to the vibration of the road test vehicle, which may vary from 0 to 0.03. Therefore, the micro bumping data in the road test data are removed by preprocessing the road test data, so that the influence of the micro bumping data on the test result is avoided, and the accuracy of the test result is ensured.
In a specific embodiment, a frequency domain analysis may be performed on the road test data by using a spectrum analysis method to determine a filter pass band and a rejection band of the road test data, and then a low pass filter may be used to filter the micro-bump data in the road test data according to the filter pass band and the rejection band of the road test data. Of course, the micro-bump data in the road test data may be removed in other ways, which is not limited herein.
It should be noted that, only non-numerical bad points in the road test data may be removed, or only micro-bump data in the road test data may be removed, and preferably, in order to ensure the accuracy of the test result, the non-numerical bad points and the micro-bump data in the road test data are removed at the same time.
On the basis of the foregoing embodiments, the present application further provides another test apparatus for advanced driving assistance system functions. A specific implementation of the device is described and illustrated below with reference to the embodiments and the accompanying drawings.
Ninth embodiment
Referring to fig. 9, it is a block diagram of a device for testing functions of an advanced driving assistance system according to a ninth embodiment of the present application. The testing device for advanced driving assistance system function provided by this embodiment includes the aforementioned data acquisition module 602, data operation module 604, and result generation module 606. It may or may not include the pre-processing module 603.
The data obtaining module 602 may adopt the specific implementation manner in the seventh embodiment, or adopt other specific implementation manners.
As shown in fig. 9, in this embodiment, the data operation module 604 is further configured to:
importing the road test data into the problem model, and traversing the logic branch of the problem model;
and if the logic branch test of the problem model is not passed, returning the problem data and exiting the logic branch test.
In this embodiment, the logical branch includes at least one of: signal state logic branch, threshold setting logic branch.
In a specific embodiment, the path test data may be operated through the signal state logic branch, and the signal data in the path test data is compared with the standard signal data in the signal state logic branch. And if the signal data in the road test data is inconsistent with the standard signal data, indicating that the signal data in the road test data does not pass the logic branch test of the problem model, returning the signal data in the road test data as problem data, and exiting the signal state logic branch. And if the signal data in the road test data is consistent with the standard signal data, the signal data in the road test data passes the logic branch test of the problem model, and then a null value is returned and the signal state logic branch is exited. Through the signal state logic branch is used for calculating the road test data, the problem data in the road test data can be accurately positioned, the occurrence time and the occurrence logic reason of the problem data are obtained, the accurate positioning of the problems with the advanced driving assistance function is realized, and the accuracy of the test result is improved.
In a specific embodiment, the road test data may be calculated through the threshold setting logic branch, and the road test data is compared with a corresponding threshold in the threshold setting logic branch. And if the road test data does not exceed the threshold, the road test data passes the test of the threshold setting logic branch of the problem model, a null value is returned, and the threshold setting logic branch is exited. And if the road test data exceeds the threshold, indicating that the road test data does not pass the test of the threshold setting logic branch of the problem model, returning the road test data as problem data, and exiting the threshold setting logic branch. In this embodiment, the threshold is obtained by digitizing the driving experience of the tester, so that the standard is more objective when the problem data is screened, and the objectivity and consistency of the test result are ensured.
In this embodiment, the problem model includes at least one of the following: the road test vehicle comprises a sensor perception data verification model, a driving feeling and/or functional rationality decision verification model and a controller decision output verification model of the road test vehicle.
In this embodiment, the problem that the sensor perception data verification model of the road test vehicle aims at includes a problem that the ADAS controller of the road test vehicle perceives the external road condition environment, for example, a problem that the perception and the distance between vehicles ahead appear. The driving feeling and/or function rationality decision-making verification model aims at the problems including the problem that an ADAS controller of the road test vehicle makes control decisions on the sensed external road condition environment, for example, the problem of controlling the speed of the vehicle when the distance between the vehicle and the front vehicle is too small. The problem that the decision output verification model of the controller aims at includes the problem that the instructions transmitted by the ADAS controller of the road test vehicle are taken and executed after reaching other controllers and hardware, for example, the instructions transmitted by the ADAS controller of the road test vehicle are decelerated, and the problems occur in the execution of a brake controller and an accelerator controller.
Preferably, the road test data are respectively imported into a sensor perception data verification model, a driving feeling and/or function rationality decision verification model and a controller decision output verification model of the road test vehicle, so that the comprehensive test of the ADAS problem is realized, and the test result is more accurate.
In a specific embodiment, the standard signal data corresponding to the sensor sensing data verification model of the road test vehicle may be standard sensor data collected from the road test vehicle. For example, a tester may additionally install the same type of sensor with higher accuracy than that of the advanced driving assistance system on the road test vehicle, and then obtain the standard signal data by performing data acquisition on the same type of sensor with higher accuracy. Of course, the setting method of the standard signal data corresponding to the sensor sensing data verification model may adopt other methods, which is not limited herein.
In a specific embodiment, the standard signal data corresponding to the driving experience and/or functional rationality decision-making verification model may be obtained as the standard signal data according to the road test data of the road test vehicle. For example, a tester obtains standard decision output data of the road test vehicle as a control accelerator according to vehicle distance data in vehicle road test data, and the standard decision output data is collated with actual decision output data in the road test data to judge whether the actual decision output data in the road test data is accurate. Of course, the setting method of the standard signal data corresponding to the ride quality and/or functional rationality decision-making verification model may adopt other methods, which are not limited herein.
In a specific embodiment, the standard signal data corresponding to the controller decision output verification model may be total control output data of the road test vehicle. For example, the general control output data of the road test vehicle and the output data of the controller in the road test data are verified, and whether the output data of the controller in the road test data is timely and stable is judged. Of course, the setting method of the standard signal data corresponding to the decision output verification model of the controller may adopt other methods, which is not limited herein.
In a specific embodiment, the threshold corresponding to the sensor-aware data verification model of the road test vehicle may be determined according to at least one of the ADAS development design parameters, ADAS certification standards and specifications at home and abroad, and processing of historical road test data according to a numerical model. The specific steps of processing the historical road test data according to the numerical model are as follows: screening out historical road test data corresponding to a sensor perception data verification model of the road test vehicle from the historical road test data, importing the historical road test data corresponding to the sensor perception data verification model of the road test vehicle into the numerical model, counting the frequency of each signal value in the historical road test data corresponding to the sensor perception data verification model of the road test vehicle, forming a frequency distribution table, determining the distribution condition of the signal values in the problem data in the historical road test data corresponding to the sensor perception data verification model of the road test vehicle in the frequency distribution table, and determining a threshold value capable of covering 65% to 99% of the signal values of the problem data according to the distribution condition.
In this embodiment, the threshold of the problem model is obtained according to the historical road test data and the numerical model, and the driving feeling of the tester is digitized, so that the test result is more objective and accurate.
It should be noted that the threshold values corresponding to the driving experience and/or functional rationality decision-making verification model and the controller decision-making output verification model are similar to the threshold value setting method of the sensor perception data verification model of the road test vehicle, and are not repeated here.
In this embodiment, in the prior art, the problem that the function of the advanced driving assistance system is determined according to the driving feeling of the tester, most of the problems can only be sensed in the decision output aspect of the controller, and the problem of the function of the advanced driving assistance system in the sensor perception aspect and the problem in the driving feeling and/or function rationality decision aspect cannot be tested. Therefore, the logic branch test is carried out on the road test data through the sensor perception data verification model, the driving feeling and/or function rationality decision-making verification model and the controller decision-making output verification model of the road test vehicle, the comprehensive problem test of the functions of the advanced driving auxiliary system is realized, and the obtained test result is more accurate.
Optionally, before the road test data is imported into the problem model, the road test data may be further segmented to obtain road test data corresponding to the problem model, and then the road test data corresponding to the problem model is imported into the problem model. By dividing the road test data, the data dimension is reduced, so that when the problem model carries out logic branch test on the road test data, the data operation amount of the logic branch is simplified, and the test efficiency is improved.
In this embodiment, when the problem data is screened out from the road test data, the problem data is no longer determined according to the driving feeling of a tester, but is screened out from the road test data according to the logic branch test of the problem model, so that the objectivity and consistency of the test result are ensured, and the test result is more accurate.
On the basis of the foregoing embodiments, the present application further provides another test apparatus for advanced driving assistance system functions. A specific implementation of the device is described and illustrated below with reference to the embodiments and the accompanying drawings.
Tenth embodiment
Referring to fig. 10, the figure is a block diagram of a test apparatus for advanced driving assistance system functions according to a tenth embodiment of the present application. The testing apparatus for advanced driving assistance system function provided in this embodiment includes the aforementioned data acquiring module 602, data calculating module 604, and result generating module 606. It may or may not include the pre-processing module 603.
The data obtaining module 602 may adopt the specific implementation manner in the seventh embodiment, or adopt other specific implementation manners. The data operation module 604 may adopt the specific implementation manner in the ninth embodiment, or adopt other specific implementation manners.
As shown in fig. 10, in this embodiment, the testing apparatus further includes a visualization module 607 for analyzing and visualizing the test result of the road test vehicle through a chart area and/or a pivot table.
In this embodiment, the visualization processing may specifically be that at least one of the precise time point of occurrence of the problem data included in the test result, the name and the number of the problem data, the signal value of the problem data, and the controller generating the problem data is displayed to a tester through a chart area and/or a data table, so that the tester can conveniently view the test result and analyze the test result, thereby improving the test efficiency.
It should be noted that, in the present specification, all the embodiments are described in a progressive manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus and system embodiments, since they are substantially similar to the method embodiments, they are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts suggested as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only one specific embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (14)
1. A method for testing the function of an advanced driving assistance system is characterized by comprising the following steps:
acquiring road test data of a road test vehicle, wherein the road test data comprises CAN bus data and ADAS sensor data;
calculating the road test data according to a problem model, and screening out problem data in the road test data;
and generating a test result of the advanced driving assistance system function of the road test vehicle according to the problem data.
2. The method according to claim 1, wherein the acquiring of the road test data of the road test vehicle is specifically:
and acquiring and fusing the CAN bus data and the ADAS sensor data to obtain the road test data of the road test vehicle.
3. The method of claim 1, wherein performing the problem test on the road test data and determining the problem data in the road test data further comprises:
and preprocessing the road test data to remove non-numerical value dead pixels and/or micro bump data in the road test data.
4. The method according to any one of claims 1 to 3, wherein the performing the operation on the road test data according to the problem model and screening out the problem data in the road test data comprises:
importing the road test data into the problem model, and traversing the logic branch of the problem model;
and if the logic branch test of the problem model is not passed, returning the problem data and exiting the logic branch test.
5. The method of claim 4, wherein the logical branch comprises at least one of:
signal state logic branch, threshold setting logic branch.
6. A method according to any of claims 1-3, wherein the problem model comprises at least one of: the road test vehicle comprises a sensor perception data verification model, a driving feeling and/or functional rationality decision verification model and a controller decision output verification model of the road test vehicle.
7. The method according to any one of claims 1-3, further comprising:
and analyzing and visualizing the test result of the road test vehicle through a chart area and/or a data pivot table.
8. A device for testing the function of an advanced driving assistance system, comprising:
the data acquisition module is used for acquiring road test data of a road test vehicle, wherein the road test data comprises CAN bus data and ADAS sensor data;
the data operation module is used for operating the road test data according to a problem model and screening out problem data in the road test data;
and the result generation module is used for generating a test result of the advanced driving assistance system function of the road test vehicle according to the problem data.
9. The apparatus of claim 8, wherein the data acquisition module is specifically configured to:
and acquiring and fusing the CAN bus data and the ADAS sensor data to obtain the road test data of the road test vehicle.
10. The apparatus of claim 8, further comprising a preprocessing module configured to perform a preprocessing operation on the road test data to remove non-numerical bad points and/or micro-bump data in the road test data.
11. The apparatus of claims 8-10, wherein the data operation module is further configured to:
importing the road test data into the problem model, and traversing the logic branch of the problem model;
and if the logic branch test of the problem model is not passed, returning the problem data and exiting the logic branch test.
12. The apparatus of claim 11, wherein the logical branch comprises at least one of:
signal state logic branch, threshold setting logic branch.
13. The apparatus of claims 8-10, wherein the problem model comprises at least one of: the road test vehicle comprises a sensor perception data verification model, a driving feeling and/or functional rationality decision verification model and a controller decision output verification model of the road test vehicle.
14. The device according to any one of claims 8 to 10, further comprising a visualization module for analyzing and visualizing the test results of the road test vehicle through a chart area and/or a pivot table.
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