Navigation application function test method, device, system, electronic equipment and storage medium

Abstract

This application relates to a method, apparatus, system, electronic device, and storage medium for testing navigation application functions. The method includes: acquiring a test task corresponding to the navigation application under test; running the navigation application under test on a test bench to execute the test task, simulating driving scenarios corresponding to the target road test data to obtain simulated road test data; and generating test results for the navigation application under test based on the simulated road test data and the actual operation data corresponding to the function under test. This technical solution, by using existing real-vehicle road test data on an indoor test bench, can significantly improve testing efficiency, shorten the testing cycle, and reduce the time and manpower costs of navigation application testing. Furthermore, the existing real-vehicle road test data can be reused for testing more navigation functions, ensuring that each navigation application version can be tested across all scenarios, thus improving the accuracy and comprehensiveness of navigation application testing.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically to methods, apparatus, systems, electronic devices, and storage media for testing navigation application functions. Background Technology

[0002] In-vehicle navigation is one of the most frequently used applications in the smart cockpit field. During vehicle operation, the accuracy and timeliness of various navigation functions, such as lane departure warnings, electronic eye alerts, section speed measurement, overspeed warnings, and highway service area displays, are crucial indicators affecting the user experience. When these functions are not recognized or misrecognized, the system cannot guide the user correctly, leading to wrong turns, detours, speeding, missing service areas, and causing user dissatisfaction and complaints. Therefore, in the field of in-vehicle navigation, the navigation performance of various functions is a key module in navigation testing.

[0003] Currently, existing navigation testing is mainly achieved through long-term, long-distance, and multi-scenario real-vehicle road tests. This testing method is time-consuming, resource-intensive, inefficient, and the test data is difficult to reuse. Furthermore, it cannot guarantee that every version of the navigation application can be tested to cover all scenarios. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, system, electronic device and storage medium for testing navigation application functions, so as to solve the problems of long testing cycle, large resource investment and low efficiency in the prior art.

[0005] To achieve the above objectives, the technical solution adopted in this application is as follows: According to one aspect of the embodiments of this application, a method for testing the functionality of a navigation application is provided, comprising: Obtain the test task corresponding to the navigation application to be tested. The test task is created based on target road test data corresponding to multiple test scenarios. The target road test data includes navigation route data, actual driving trajectory data and actual operation data corresponding to the function to be tested obtained from real vehicle road tests. The navigation application under test is run on a test bench to perform the test task, and driving simulation is performed on the actual driving scenario corresponding to the target road test data to obtain simulated road test data. Based on the simulated road test data and the actual operation data, test results for the corresponding functions of the navigation application under test are generated.

[0006] Optionally, the actual operation data includes actual yaw data; the simulated road test data includes simulated yaw data. The step of generating test results for the navigation application under test based on the simulated road test data and the actual operation data includes: Based on the simulated yaw data and the actual yaw data, the yaw test results corresponding to the navigation application under test are generated.

[0007] Optionally, the method further includes: Obtain candidate road test data; Extract drive test parameters from the candidate drive test data; The candidate road test data is filtered based on the road test parameters, and candidate road test data whose road test parameters do not meet the admission criteria are deleted to obtain the target road test data; wherein, the admission criteria include at least one of the following: The candidate road test data includes one-to-one corresponding navigation route data and actual driving trajectory data; The candidate road test data shall include at least the following road test parameters: driving start time, driving end time, actual vehicle yaw time, navigation application prompt yaw time, and road morphology; The start time of the journey is earlier than the actual vehicle yaw time; the actual vehicle yaw time is earlier than the end time of the journey. The actual driving time corresponding to the candidate road test data is greater than or equal to the preset time. When the actual vehicle yaw time is empty, it is determined that the candidate road test data corresponds to the actual vehicle yaw unidentified situation; The candidate road test data meets the test data collection specifications corresponding to the test scenario.

[0008] Optionally, the method further includes: According to the preset test scenario model, test scenario data corresponding to the test scenario is constructed using the target road test data; Select the test scenario data and the navigation application to be tested, and create the test task.

[0009] Optionally, the test task is performed by running the navigation application under test on the test bench, and driving simulation is conducted based on the navigation route data and actual driving trajectory data to obtain simulated road test data, including: The test scenario is replayed based on the test scenario data to simulate the actual driving scenario corresponding to the navigation route data and the actual driving trajectory data; Determine the actual deviation point based on the actual deviation data; Obtain the replay logs of the test scenario; Print the playback log to obtain the simulated yaw data corresponding to the actual yaw point.

[0010] Optionally, generating yaw test results for the navigation application under test based on the simulated yaw data and the actual yaw data includes generating at least one of the following yaw test indicators: The simulated yaw count is obtained based on the simulated yaw data, the active yaw count is obtained based on the actual yaw data, and the yaw success rate of the navigation application under test is calculated by the simulated yaw count and the actual yaw count. The simulated yaw time is obtained based on the simulated yaw data. Based on the actual yaw data, it is determined whether the yaw sent at the simulated yaw time is a false yaw. The number of false yaws corresponding to the navigation application under test is counted. Based on the simulated yaw data, the simulated yaw time, simulated yaw alert time, and the number of test scenarios with successful yaw are obtained, and the average yaw response time corresponding to the navigation application under test is calculated. Based on the scenario type corresponding to the test scenario data, calculate at least one of the following yaw test indicators for the navigation application under the scenario type: yaw success rate, number of false yaws, and average yaw response time.

[0011] Optionally, the method further includes: When the navigation application under test errs during the test, the simulated erroneous deviation data corresponding to the erroneous deviation of the navigation application under test is obtained; The navigation application under test is corrected for erroneous deviations based on the simulated erroneous deviation data. The test task is performed on the repaired navigation application under test on the test bench. If the repaired navigation application does not erroneously during the test, the repair of the navigation application is considered successful.

[0012] Optionally, generating the yaw test result corresponding to the navigation application under test based on the simulated yaw data and the actual yaw data includes: The error correction rate of the navigation application under test is calculated based on the number of successful repairs and the number of test scenarios in which erroneous deviations occurred during the test.

[0013] Optionally, the step of correcting the erroneous deviation of the navigation application under test based on the simulated erroneous deviation data includes: Obtain the playback logs corresponding to the test scenario where the navigation application under test erroneously deviates from its course; The playback logs are printed and analyzed to determine the type of mis-yawing data corresponding to the simulated mis-yawing data; Locate the code module corresponding to the aforementioned veergence error type in the navigation application under test and the code repair strategy; The code module is repaired according to the code repair strategy to obtain the navigation application under test after the erroneous deviation is repaired.

[0014] Optionally, the method further includes: Obtain the display data corresponding to the navigation car logo on the navigation interface when the navigation application under test is running; The display test result of the navigation car logo is determined based on the test scenario and the display data.

[0015] According to another aspect of the embodiments of this application, a navigation application function testing apparatus is provided, comprising: The acquisition module is used to acquire the test task corresponding to the navigation application under test. The test task is created based on target road test data corresponding to multiple test scenarios. The target road test data includes navigation route data obtained from real vehicle road tests, actual driving trajectory data, and actual operation data corresponding to the function under test. The execution module is used to run the navigation application under test to perform the test task, simulate driving in the actual driving scenario corresponding to the target road test data, and obtain simulated road test data. The generation module is used to generate test results for the navigation application under test based on the simulated road test data and the actual operation data.

[0016] According to another aspect of the embodiments of this application, a navigation application function testing system is provided, including: a navigation test server and a test bench; The navigation test server is used to store target road test data corresponding to multiple test scenarios, create test tasks corresponding to the navigation application under test based on the target road test data, and send the test tasks to the test bench; wherein, the target road test data includes navigation route data, actual driving trajectory data and actual operation data corresponding to the function under test obtained from real vehicle road tests; The test bench is used to run the navigation application under test to perform the test task, simulate driving in the actual driving scenario corresponding to the target road test data, obtain simulated road test data, and upload the simulated road test data to the navigation test server; The navigation test server is also used to generate test results for the navigation application under test based on the simulated road test data and the actual operation data.

[0017] According to another aspect of the embodiments of this application, a storage medium is also provided, the storage medium including a stored program that executes the above steps when the program is run.

[0018] According to another aspect of the embodiments of this application, an electronic device is provided, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory is used to store computer programs; The processor is used to implement the above method steps when executing a computer program.

[0019] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the above-described method steps.

[0020] The technical solutions provided in this application have the following advantages compared with the prior art: In this embodiment, a test task corresponding to the navigation application under test is obtained. The test task is created based on target road test data corresponding to multiple test scenarios. The target road test data includes navigation route data obtained from real vehicle road tests, actual driving trajectory data, and actual operation data corresponding to the function under test. The navigation application under test is run on a test bench to execute the test task, simulating driving scenarios corresponding to the target road test data to obtain simulated road test data. Based on the simulated road test data and the actual operation data, test results for the function under test corresponding to the navigation application under test are generated. By using existing real vehicle road test data, simulation tests are performed on the navigation application under test on a test bench to evaluate the function under test. This avoids the need for real-vehicle road testing after each feature modification or software upgrade of the navigation application. It eliminates the heavy reliance on real-vehicle road testing to discover and fix positioning and recognition issues in various navigation functions. This can greatly improve testing efficiency, shorten the testing cycle, and reduce the time and manpower costs of navigation application testing. Furthermore, existing real-vehicle road test data can be reused for testing more navigation functions, ensuring that each version of the navigation application can be tested in all scenarios, thus improving the accuracy and comprehensiveness of navigation application testing. Attached Figure Description

[0021] Figure 1 A flowchart illustrating a navigation application function testing method provided in this application embodiment; Figure 2 A flowchart illustrating a navigation application function testing method provided in another embodiment of this application; Figure 3 A schematic diagram of the interface of the yaw data acquisition tool provided in the embodiments of this application; Figure 4 A flowchart illustrating a navigation application function testing method provided in another embodiment of this application; Figure 5A flowchart illustrating a navigation application function testing method provided in another embodiment of this application; Figure 6 This is a schematic diagram illustrating the relationship between test scenario data, navigation route data, and driving trajectory data provided in the embodiments of this application. Figure 7 A schematic diagram of the interface of the yaw playback task platform provided in the embodiments of this application; Figure 8 A flowchart illustrating a navigation application function testing method provided in another embodiment of this application; Figure 9 A schematic diagram of the yaw playback display interface provided in an embodiment of this application; Figure 10 A flowchart illustrating a navigation application function testing method provided in another embodiment of this application; Figure 11 A flowchart illustrating a navigation application function testing method provided in another embodiment of this application; Figure 12 A schematic diagram showing the yaw test results interface for an embodiment of this application; Figure 13 A block diagram of a navigation application function testing device provided in an embodiment of this application; Figure 14 A block diagram of a navigation application function testing device provided in another embodiment of this application; Figure 15 A block diagram of a navigation application function testing system provided in this application embodiment; Figure 16 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0022] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0023] This application embodiment uses existing real-vehicle road test data to conduct simulation tests on the navigation application under test on a bench testing device, in order to test and evaluate the functions of the navigation application under test. The real-vehicle road test data can be data obtained by testers from real-vehicle road tests on different types of roads, such as main and auxiliary roads, elevated roads, tunnels, roundabouts, and other complex road morphologies, covering various traffic types such as highways, urban rail transit, and subways.

[0024] During real-vehicle road testing, a navigation route is planned within the in-vehicle navigation application of the test vehicle. After successful route calculation, the corresponding shape point data, i.e., navigation route data, is automatically generated. During the test vehicle's operation, the actual driving trajectory is automatically recorded. This actual driving trajectory data includes data recorded by inertial sensors, vehicle driving-related data, GPS positioning data, etc. Test personnel record the operation of the function under test by operating the test Human Machine Interface (HMI) during vehicle operation. All test data, including navigation route data, actual driving trajectory data, and actual operation data corresponding to the function under test, are recorded in the Automated Software Development Kit (AutoSDK) log.

[0025] Before bench testing, real-vehicle road test data required for software function testing can be extracted from the AutoSDK logs. Each real-vehicle road test corresponds to one AutoSDK log. A complete real-vehicle road test is marked by the tester's operation of the controls corresponding to the start and end of the test during the real-vehicle road test.

[0026] In this embodiment of the application, the navigation application used for the test vehicle in the actual road test can be different from the navigation application under test. That is, the simulation test is carried out on the test bench, and only the data obtained from the actual road test is used. There are no requirements for the navigation application used in the actual road test.

[0027] The following section first introduces a navigation application function testing method provided by an embodiment of the present invention.

[0028] Figure 1 This is a flowchart illustrating a navigation application function testing method provided in an embodiment of this application. Figure 1 As shown, the method includes the following steps S11-S13.

[0029] Step S11: Obtain the test task corresponding to the navigation application to be tested. The test task is created based on the target road test data corresponding to multiple test scenarios. The target road test data includes navigation route data, actual driving trajectory data and actual operation data corresponding to the function to be tested obtained from real vehicle road tests. Step S12: Run the navigation application under test on the test bench to perform test tasks, simulate driving scenarios corresponding to the target road test data, and obtain simulated road test data. Step S13: Based on the simulated road test data and actual operation data, generate the test results of the corresponding functions of the navigation application under test.

[0030] This application embodiment utilizes existing real-vehicle road test data to conduct simulation tests on the navigation application under test on a bench testing device, thereby evaluating the functions of the navigation application under test. This avoids the need for real-vehicle road testing after each function modification or software upgrade of the navigation application, and eliminates the heavy reliance on real-vehicle road testing to discover and fix positioning and recognition problems in various navigation functions. The testing method of this application embodiment can significantly improve testing efficiency, shorten the testing cycle, and reduce the time and manpower costs of navigation application testing. Furthermore, existing real-vehicle road test data can be reused for testing more navigation functions, reducing the investment cost for each navigation application function test. The average testing cost decreases as the number of tests increases. It also ensures that each navigation application version can be tested covering all scenarios, improving the accuracy and comprehensiveness of navigation application testing. If multiple test benches operate simultaneously, multi-segment and multi-scenario testing can be performed, further effectively reducing the testing time for navigation functions.

[0031] In addition, since the navigation function test in this application is based on real vehicle road test data to simulate navigation and driving trajectory, it does not require the use of complex artificial intelligence technologies such as image recognition. Only simple statistical calculations are needed to accurately obtain the test results of the navigation function, which greatly reduces the test complexity, avoids the dependence of the test on the accuracy of the algorithm model, and eliminates the need to collect a large amount of image data for model training, thereby further reducing the test cost and improving the stability of the navigation function test.

[0032] In step S11 above, the functions to be tested in the navigation application may include: deviation warning, electronic eye warning, section speed measurement, overspeed warning, highway service area warning, highway toll station warning, etc. During a single test, one or more functions can be tested simultaneously.

[0033] In an optional embodiment, the corresponding target road test data can be automatically matched based on the function to be tested input or selected by the tester. Alternatively, the corresponding target road test data can be automatically matched based on the function to be tested input or selected by the tester and the test scenario to obtain the test task corresponding to the navigation application under test.

[0034] In step S11 above, the test scenario can be set based on at least one of the following parameters, including: road type, time period, weather type, etc. The test scenario can be determined according to the road type input or selected by the tester. For example, in the main road and auxiliary road test scenario, when creating the test task, the target test data is selected based on the real vehicle road test data of the main road and auxiliary road scenario; as for the elevated road and commuting rush hour test scenario, the target test data needs to be selected based on the real vehicle road test data of the elevated road during the commuting rush hour.

[0035] In step S11 above, the target road test data includes navigation route data, actual driving trajectory data, and actual operation data corresponding to the function under test obtained from real vehicle road tests. Specifically, the navigation route data includes the shape point data automatically generated after the in-vehicle navigation application plans the route based on the vehicle's current starting and ending positions during real vehicle road tests and the route calculation is successful. The actual driving trajectory data is the data corresponding to the actual driving trajectory of the vehicle during real vehicle road tests, including data recorded by vehicle sensors, vehicle driving-related data, GPS positioning data, etc. The actual operation data corresponding to the function under test includes the tester's operation log, which shows the tester's operations on the test data acquisition tool during real vehicle road tests, including clicking buttons and selecting road types, etc. For example, in yaw tests, the actual operational data includes the clicks made by test personnel on the "Active Yaw Report" and "False Yaw Report" buttons in the onboard test data acquisition tool during real-vehicle road tests; in electronic eye warning tests, the actual operational data includes the clicks made by test personnel on the "Electronic Eye Report" button in the onboard test data acquisition tool during real-vehicle road tests; in highway service area reminder tests, the actual operational data includes the clicks made by test personnel on the "Highway Service Area Report," "Service Area Refueling Service Report," "Service Area Charging Service Report," "Service Area Toilet Service Report," "Highway Service Area Catering Service Report," and "Highway Service Area Accommodation Service Report" buttons in the onboard test data acquisition tool during real-vehicle road tests; and so on.

[0036] In step S12 above, the test bench, also known as a test workbench, is a virtual testing environment used to verify the correctness of a design or model. In this embodiment, the test bench can schedule and execute test tasks according to testing requirements such as navigation application version and test scenario.

[0037] In step S13 above, after analyzing and evaluating the execution results of the test task on the test bench and the actual operation data in the real vehicle road test, the corresponding test results of the function under test can be obtained. The test results may include one or more evaluation indicators corresponding to the function under test; different functions under test have different evaluation indicators.

[0038] For example, the evaluation metrics for electronic eye alert tests include: success rate of electronic eye alerts, number of false or missed alerts, and average response time of electronic eye alerts (the time from when the alert is displayed during test playback to when the data is displayed at the electronic eye's coordinates). The evaluation metrics for highway service area alert tests include: success rate of highway service area alerts, number of false or missed alerts, average response time of highway service area alerts (the time from when the alert is displayed during test playback to when the data is displayed at the highway server's coordinates), and the accuracy of service availability alerts within the highway service area.

[0039] In an optional embodiment, when the function to be tested in the navigation application is a yaw function, it includes actual yaw data; the simulated road test data includes simulated yaw data. Step S13 above includes: generating a yaw test result corresponding to the navigation application under test based on the simulated yaw data and the actual yaw data.

[0040] The following provides a detailed description of the implementation method for yaw testing in navigation applications, illustrating the solution proposed in this application.

[0041] Figure 2 A flowchart illustrating a navigation application function testing method provided in another embodiment of this application. Figure 2 As shown, the yaw test method for navigation applications includes the following steps S21-S23.

[0042] Step S21: Obtain the test task corresponding to the navigation application to be tested. The test task is created based on the target road test data corresponding to multiple test scenarios. The target road test data includes navigation route data, actual driving trajectory data and actual deviation data obtained from real vehicle road tests. Step S22: Run the navigation application under test on the test bench to perform test tasks, simulate driving scenarios corresponding to the target road test data, and obtain simulated yaw data. Step S23: Generate yaw test results for the navigation application under test based on simulated yaw data and actual yaw data.

[0043] In this embodiment, existing real-vehicle road test data is used to conduct simulation tests on the navigation application under test on a bench testing device to evaluate its yaw function. This avoids the need for real-vehicle road testing after each modification or upgrade of the navigation application, and eliminates the heavy reliance on real-vehicle road testing to discover and fix yaw positioning and alert issues. This significantly improves testing efficiency, shortens the testing cycle, and reduces the time and manpower costs of navigation application testing.

[0044] In step S21 above, the actual yaw data includes the manipulated data corresponding to the active yaw control and / or erroneous yaw control in the on-board test data acquisition tool during the actual vehicle road test.

[0045] Figure 3 This is a schematic diagram of the interface of the yaw data acquisition tool provided in this application embodiment. This acquisition tool can be a standalone application or a separate interface within an in-vehicle navigation system. The in-vehicle navigation application is installed on the vehicle's infotainment system. During data acquisition, the navigation system is in a state where inertial navigation calibration has been completed to ensure that the acquired trajectory data includes: the detection results of the Inertial Measurement Unit (IMU), vehicle speed, and GPS positioning data, etc.

[0046] like Figure 3 As shown, the interface of the vehicle navigation yaw data acquisition tool (i.e., the vehicle test data acquisition tool) includes: a "driving trajectory recording" switch and a "navigation route recording" switch; a road shape selection box and "road start" and "road end" buttons for dividing different road shapes; recording buttons corresponding to different yaw types, such as "active yaw report" and "erroneous yaw report" buttons; a button for trajectory cutting; and so on.

[0047] The following is a detailed explanation of the yaw data collection process during actual vehicle road testing: I. Preparation Stage Enable the in-vehicle navigation application and the AutoSDK log switch.

[0048] like Figure 3 As shown, turn on the "Driving Trajectory Recording" and "Navigation Route Recording" switches in the vehicle test data acquisition tool to record the driving trajectory and navigation route during actual vehicle road testing.

[0049] II. Scene Acquisition Phase (1) Route planning When the vehicle is stationary, the in-vehicle navigation application searches for the destination, plans the route according to the data collection scenario, and enters navigation mode. (2) Startup trajectory recording Before starting to drive, clicking the "Start Track" button will record the start time of data collection for this scene and will also restart the recording of a complete driving track. (3) Record the road morphology based on the actual road conditions. When the vehicle enters a special road, select and click "Road Start"; when the vehicle leaves a road, select and click "End Navigation". Road types include: tunnels, elevated roads, roundabouts, main and auxiliary roads, parallel roads, branching roads, and ordinary roads. For example, when about to enter a tunnel, select "Tunnel" and click "Road Start"; after exiting the tunnel, select "Tunnel" and click "Road End".

[0050] III. Recording the Yaw Event Phase Collect the actual yaw time of the vehicle as the true yaw value to facilitate subsequent analysis of yaw success rate and yaw response time.

[0051] During the actual vehicle road test, when the vehicle actively veers off course, the test personnel immediately click the "Active Vessel Vessel Report" button after the vehicle actually enters the veergence section to record the time of the active veergence.

[0052] When the vehicle veers off course incorrectly, immediately click the "Report Off-Course Error" button. The time of the incorrect course error will be recorded in the log, and a new trajectory recording will begin.

[0053] IV. End of Scene Acquisition Phase When a deviation occurs, the navigation application will display "You have deviated from the course, and we are planning a route for you." After the vehicle has traveled a certain distance, click "End Track".

[0054] The driving trajectory is segmented, and a complete test scenario is formed from "trajectory start" to "trajectory end".

[0055] In an optional embodiment, during real-vehicle road testing, a route is planned within the navigation application. After successful route calculation, the corresponding shape point data is automatically generated. Test personnel drive off course, recording the actual time of vehicle deviation at branch points and the time it takes for the navigation application to respond to deviation prompts through the HMI interface of the data acquisition tool and printing logs. Vehicle movement generates a driving trajectory file, ultimately yielding the raw road test data required for evaluation: route shape points, driving trajectory, and actual operation logs.

[0056] In an optional embodiment, the quality of road test data may be flawed due to factors such as road conditions and manual operation. Therefore, it is necessary to filter the collected data from the actual vehicle road test to obtain the target road test data for subsequent bench testing. Figure 4 As shown, the above navigation application function testing method also includes steps S31-S33.

[0057] Step S31: Obtain candidate road test data; Step S32: Extract road test parameters from candidate road test data; Step S33: Filter the candidate road test data according to the road test parameters, delete the candidate road test data whose road test parameters do not meet the admission conditions, and obtain the target road test data.

[0058] The admission criteria include at least one of the following: Candidate road test data includes one-to-one navigation route data and actual driving trajectory data; Candidate road test data should include at least the following road test parameters: start time of driving, end time of driving, actual vehicle yaw time, yaw time indicated by navigation application, and road morphology; The start time of the journey is earlier than the actual vehicle yaw time; the actual vehicle yaw time is earlier than the end time of the journey. The actual driving time corresponding to the candidate road test data is greater than or equal to the preset time. When the actual vehicle yaw time is empty, determine the actual vehicle yaw unidentified situation corresponding to the candidate road test data; The candidate road test data meets the test data collection specifications corresponding to the test scenario.

[0059] In this embodiment, after filtering the data collected from real vehicle road tests, the data that meets the requirements for completeness and accuracy is used as the target road test data to create test tasks. By improving the quality of real vehicle road test data, the accuracy of subsequent navigation deviation function tests is guaranteed.

[0060] The above test scenarios can be divided into primary scenarios and different secondary scenarios under the primary scenarios. The test data collection specifications are different for different secondary scenarios.

[0061] For example, Level 1 scenarios can include main and auxiliary roads, elevated roads, tunnels, roundabouts, and branching roads. Main and auxiliary roads correspond to four Level 2 scenarios: driving on the main road, entering the auxiliary road from the main road, entering the main road from the auxiliary road, and driving on the auxiliary road. The test data collection specification for driving on the main road is to collect 1 km of parallel road driving trajectory data; the test data collection specification for entering the auxiliary road from the main road is to collect 1 km of main road driving trajectory data and 500 m of auxiliary road driving trajectory data. The test data collection specifications for other Level 1 and Level 2 scenarios are not detailed here. Different test scenarios require the collection of vehicle trajectory data over certain distances on different roads, so that different test scenarios can be accurately simulated and replayed during subsequent yaw tests on the test bench.

[0062] In optional embodiments, the candidate road test data may contain some dirty data. For example, one actual active yaw may correspond to multiple actual vehicle yaw times, and these multiple actual vehicle yaw times are close together, i.e., the tester mistakenly clicked the "Active Yaw Report" button multiple times; or, the navigation application's indicated yaw time is earlier than the actual vehicle yaw time, i.e., the tester did not click the "Active Yaw Report" button when the vehicle actively yawed, but only clicked the button to report the active yaw when the navigation application indicated yaw; and so on. If dirty data is identified in the candidate road test data, the candidate road test data can be repaired before being used as the target road test data. For the case where one actual active yaw corresponds to multiple actual vehicle yaw times, the earliest actual vehicle yaw time can be retained, and the other actual vehicle yaw times can be deleted; for the case where the navigation application's indicated yaw time is earlier than the actual vehicle yaw time, the actual vehicle yaw time can be modified to n seconds before the navigation application's indicated yaw time, where n can be determined based on the navigation application's average yaw response time, such as being set to 3-20 seconds. In this embodiment, by identifying and repairing dirty data, the quality of test data is improved, ensuring the accuracy of subsequent navigation yaw function tests.

[0063] In an optional embodiment, the data collected from real-vehicle road tests needs to be processed before it can be used to create test tasks. For example... Figure 5 As shown, the method further includes the following steps S41-S42. Step S41: Construct test scenario data corresponding to the test scenario using the target road test data according to the preset test scenario model; Step S42: Select the test scenario data and the navigation application to be tested, and create a test task.

[0064] In step S41 above, the preset test scenario model includes three parts: route, trajectory, and scenario. The route and trajectory are in a one-to-one correspondence; navigation route data, actual driving trajectory data, and their corresponding actual deviation data together constitute the test scenario data. For example... Figure 6 As shown, each navigation route data corresponds to a route ID, each actual driving trajectory data also corresponds to a trajectory ID, and the test scenario data generated based on both corresponds to a scenario ID. The route ID, trajectory ID, and scenario ID are all corresponding. Optionally, after obtaining the road test data from the onboard device and uploading it, test scenario data corresponding to the test scenario can be constructed using the road test data according to a preset test scenario model. Based on all the test scenario data, a test scenario database is built. This database can filter test scenario data based on test scenario parameters, such as scenario name, road type, test time period, and weather type.

[0065] In step S42 above, testers can select the navigation application to be tested, such as different versions of the navigation application, and select multiple test scenario data to create a test task. Alternatively, after selecting the navigation application to be tested, testers can input or select the required scenario parameters, such as road shape, and the system will automatically match test scenario data according to the scenario parameters to create a test task.

[0066] Yaw playback mission platform interface as follows Figure 7 As shown, multiple test tasks can be executed sequentially on the test bench, and these tasks can be sorted by priority. The interface displays the test task name, the number of test scenarios to be replayed for each test task, the version name of the navigation application under test, the current test status, and a "View" link for the evaluation report, including the test results, etc.

[0067] Optionally, testers can create new test tasks through "Create New Task", including: entering the test task name; setting the task priority; selecting the navigation application version to be tested; if the navigation application version to be tested is not available in the test bench, testers can also upload the software to be tested to the test bench; selecting test scenario data; and so on.

[0068] In an optional embodiment, after receiving the test task, the test bench runs the navigation application under test and uses real vehicle road test data to perform simulation to test the yaw function of the navigation application under test. Figure 8 As shown, step S22 includes steps S51-S54: Step S51: Replay the test scenario based on the test scenario data to simulate the actual driving scenario corresponding to the navigation route data and the actual driving trajectory data; Step S52: Determine the actual yaw point based on the actual yaw data; Step S53: Obtain the replay logs of the test scenario; Step S54: Print the playback log to obtain the simulated yaw data corresponding to the actual yaw point.

[0069] In this embodiment, the test bench automatically selects the test scenario data to be replayed in sequence, calls the AutoSDK's trajectory playback application program interface (API), tests and simulates navigation and driving in a real-world scenario, and prints the current time from the playback log to the actual yaw point coordinates, as well as the time when the system prompts yaw during playback, etc., to statistically analyze test metrics such as yaw success rate and response time during playback. The test bench can have an independent interface to display the playback status, such as... Figure 9As shown, one part of the interface displays the playback progress of the test task; the other part displays the interface of simulated navigation playback, that is, the navigation software under test displays the route navigation and driving trajectory.

[0070] By replaying test scenarios on a test bench, the actual driving conditions of the vehicle are simulated, allowing for the evaluation of the yaw performance of the navigation application under test on real-world road sections. This significantly reduces the reliance on real-vehicle road testing for yaw testing, eliminating the need for testers to drive on actual roads for each navigation application version upgrade, thus greatly reducing manpower costs. Furthermore, once data is collected from a real-vehicle road test, yaw performance testing can be conducted with each navigation version release, reducing the investment cost for positioning evaluation for each version. The average testing cost decreases with the number of tests. In addition, multiple test benches can be run simultaneously to conduct yaw tests on multiple road sections and in multiple scenarios, effectively reducing the testing time.

[0071] In an optional embodiment, after obtaining simulated yaw data from the test, the yaw test result is obtained by combining it with actual yaw data for analysis and evaluation. Step S23 includes the calculation of at least one of the following test results: (1) Obtain the number of simulated yawings based on the simulated yawing data, obtain the number of active yawings based on the actual yawing data, and calculate the yawing success rate of the navigation application under test based on the number of simulated yawings and the number of actual yawings. Among them, the number of active yaw counts is the number of times the test personnel clicked the active yaw button during actual vehicle road testing; (2) Obtain the simulated yaw time based on the simulated yaw data, determine whether the yaw sent at the simulated yaw time is a false yaw based on the actual yaw data, and count the number of false yaws corresponding to the navigation application under test. (3) Based on the simulated yaw data, obtain the simulated yaw time, simulated yaw reminder time and the number of test scenarios with successful yaw, and calculate the average yaw response time corresponding to the navigation application under test; Each time a yaw recalculation is triggered during the test, a successful yaw test can be counted. (4) Based on the scenario type corresponding to the test scenario data, calculate at least one of the following yaw test indicators for the navigation application under the scenario type: yaw success rate, number of false yaws and average yaw response time.

[0072] Therefore, the yaw test analysis in this embodiment can accurately obtain the test results of the yaw function without the need to use artificial intelligence technologies such as image recognition. This greatly reduces the test complexity, avoids the dependence of the test on the accuracy of the algorithm model, and eliminates the need to collect a large amount of image data for model training, thereby further reducing the test cost and improving the stability of navigation function testing.

[0073] Furthermore, yaw test analysis can be performed by scenario, analyzing yaw test metrics such as yaw success rate, number of false yaws, and yaw response time. This allows for more targeted analysis of the yaw recognition performance of the navigation software under test in different scenarios, as well as the scenarios where yaw problems are mainly concentrated, thus enabling more targeted solutions to yaw issues. Moreover, it can accurately identify routes prone to yaw anomalies, improving the efficiency of detecting and addressing yaw anomalies.

[0074] The aforementioned actual yaw data includes the manipulated data corresponding to the active yaw control and / or erroneous yaw control in the onboard test data acquisition tool during real-vehicle road testing. Therefore, in the process of generating the yaw test result corresponding to the navigation application under test based on the simulated yaw data and the actual yaw data, step S23 further includes: determining the active yaw data based on the manipulated data corresponding to the active yaw control; and / or, determining the erroneous yaw data based on the manipulated data corresponding to the erroneous yaw control.

[0075] Active yaw data may include active yaw time and active yaw coordinates; erroneous yaw data may include erroneous yaw time and erroneous yaw coordinates. During real-vehicle road testing, testers click on active yaw or erroneous yaw, and based on the testers' clicks, active yaw data and erroneous yaw data are obtained for subsequent yaw index analysis.

[0076] During testing, false deviations may occur. These include situations where the navigation application identifies a deviation when none has occurred, or where a deviation has occurred but the navigation software fails to recognize it. The occurrence of false deviations indicates a potential bug in the navigation application's code; failure to fix this bug will severely impact the accuracy of the navigation application's deviation warnings. Therefore, in optional embodiments, such as... Figure 10 As shown, the method further includes steps S61-S64: Step S61: When the navigation application under test errs during the test, acquire the simulated erroneous deviation data corresponding to the erroneous deviation of the navigation application under test. Step S62: Correct the erroneous deviations of the navigation application under test based on the simulated erroneous deviation data; Step S63: Perform a test task on the repaired navigation application under test on the test bench; Step S64: When the repaired navigation application under test does not erroneously deviate during the test, the repair of the navigation application under test is determined to be successful.

[0077] In this embodiment, if a erroneous deviation occurs during testing, the navigation application can be automatically repaired. After repair, the navigation application is tested again until the erroneous deviation is eliminated. This effectively solves the erroneous deviation problem caused by code bugs, improving the efficiency of erroneous deviation verification and resolution.

[0078] Optional, such as Figure 11 As shown, step S62 above, which corrects the erroneous deviation of the navigation application under test based on the simulated erroneous deviation data, includes steps S71-S74: Step S71: Obtain the playback logs corresponding to the test scenario in which the navigation application under test erroneously deviates from its course; Step S72: Print and analyze the playback log to determine the type of mis-yawing data corresponding to the simulated mis-yawing data; Step S73: Locate the code module and code repair strategy corresponding to the type of veergency in the navigation application under test; Step S74: Repair the code module according to the code repair strategy to obtain the navigation application to be tested after the erroneous course correction.

[0079] For example, in a main-auxiliary road scenario, if yaw recognition fails, the erroneous yaw type is "Main-auxiliary road yaw recognition failure." Based on this erroneous yaw type, the yaw threshold parameter in the navigation application code can be automatically located. Since the current yaw threshold parameter may be too large to accurately identify scenarios like main-auxiliary roads, the code repair strategy can be to reduce the yaw threshold parameter. For example, if the current yaw threshold parameter is 100 meters, it can be reduced by 20 meters, i.e., adjusted to 80 meters, and then tested again. If erroneous yaw still occurs, the yaw threshold parameter can be reduced by another 20 meters, i.e., adjusted to 60 meters, and then tested again, until erroneous yaw in the main-auxiliary road scenario is eliminated. The above yaw threshold parameter adjustment values ​​are only examples. In actual erroneous yaw repair, the parameter values ​​for each adjustment can be set according to the actual situation.

[0080] Optionally, based on the occurrence and repair of erroneous deviations, the deviation test results of the navigation application can be further obtained. Step S23 above includes: calculating the erroneous deviation repair rate of the navigation application under test based on the number of successful repairs of the navigation application under test and the number of test scenarios in which erroneous deviations occurred during the test.

[0081] In this way, after the test is completed, corresponding evaluation reports can be generated according to the test scenario and navigation application version, such as... Figure 12As shown, the yaw test data statistics interface displays the corresponding yaw test metrics for each test task, including active yaw success rate, yaw response time, number of erroneous yaws, erroneous yaw correction rate, etc. Optionally, testers can query the corresponding test results on this interface based on the navigation version or the task name, or they can filter the test results of all relevant test tasks by selecting the test scenario type.

[0082] If the test scenario data includes erroneous deviation data, i.e. erroneous deviations occur during actual vehicle road testing, even though the navigation application version used in the actual vehicle road test may differ from the navigation application version used in the bench test, it is still necessary to check whether erroneous deviations still occur at the actual erroneous deviation points during bench testing. If there are no erroneous deviations, then the navigation application version used in the bench test has fixed the erroneous deviation issue; if erroneous deviations still occur, then the navigation application version used in the bench test should be fixed.

[0083] In an optional embodiment, the method can also test whether the navigation car logo is displayed normally in the navigation application. The method further includes: obtaining the display data corresponding to the navigation car logo on the navigation interface when the navigation application under test is running; and determining the display test result of the navigation car logo based on the test scenario and the display data.

[0084] For example, in a main road and auxiliary road test scenario, if the simulated vehicle is driving on the main road, the navigation car icon should always be displayed on the main road; when the navigation route is driving on the main road and the simulated vehicle enters the auxiliary road from the main road, the navigation application triggers a yaw recalculation, the new route starts from the auxiliary road, and the navigation car icon should be displayed on the auxiliary road.

[0085] For example, if the car logo is constantly moving when the vehicle speed is 0, or if the vehicle speed is not 0 but the car logo remains stationary, it indicates that the car logo display is abnormal.

[0086] In this way, by testing the display of navigation car logos, we can further judge the performance of navigation applications, thereby conducting a more comprehensive and accurate evaluation of navigation applications.

[0087] The overall process of the navigation application yaw test method in this embodiment is described below.

[0088] Step A1: Obtain real vehicle road test data; This real-vehicle road test data can be obtained by testing different test vehicles using different navigation application versions on different actual roads. Step A2: Perform data quality checks on the actual vehicle road test data. If the data meets the requirements, proceed to step A3; otherwise, return to step A1. Data quality inspection includes checking the completeness and accuracy of real vehicle road test data. For yaw test requirements, one or more data admission conditions can be set. Real vehicle road test data that meets the admission conditions are considered to meet the quality requirements; otherwise, the data will be rejected. If there is dirty data in the actual vehicle road test data, it can be repaired, and the repaired data can then be tested for data quality. Step A3: Process the real vehicle road test data to construct an evaluation data set; Based on the preset test scenario model, test scenario data is generated using real vehicle road test data, and then an evaluation data set is constructed. Step A4: Create a test task; Create a test task based on the selected test scenario data and the navigation application to be tested; Step A5: Perform a yaw test on the vehicle navigation system; Run the navigation application under test on the test bench and perform test tasks to conduct yaw tests. Step A6: Output the test report; The output test report can include multiple yaw test metrics, such as yaw success rate, number of false yaws, and average yaw response time. It can also output the corresponding yaw test metrics for the navigation application in different scenario types.

[0089] The navigation application testing method in this embodiment features low dependence on real vehicles, low cost, short cycle time, high stability, and practicality. In terms of application scenarios, it covers different road types, road grades, and cities; it can effectively improve testing efficiency and reduce testing costs; it can be used in various stages of the product development process, such as in-vehicle navigation testing, R&D self-testing, and regression testing after problem fixing; and it can be reused for testing various navigation functions.

[0090] The following are embodiments of the apparatus of this application, which can be used to execute the embodiments of the method of this application.

[0091] Figure 13 This is a block diagram of a navigation application function testing device provided in an embodiment of this application. This device can be implemented as part or all of an electronic device through software, hardware, or a combination of both. Figure 13 As shown, the navigation application function testing device includes: The acquisition module 101 is used to acquire the test task corresponding to the navigation application under test. The test task is created based on target road test data corresponding to multiple test scenarios. The target road test data includes navigation route data obtained from real vehicle road tests, actual driving trajectory data, and actual operation data corresponding to the function under test. Execution module 102 is used to run the navigation application under test to perform the test task, simulate driving in the actual driving scenario corresponding to the target road test data, and obtain simulated road test data; The generation module 103 is used to generate test results for the navigation application under test based on the simulated road test data and the actual operation data.

[0092] Optionally, the actual operation data includes actual yaw data; the simulated road test data includes simulated yaw data; the generation module 103 is used to generate yaw test results corresponding to the navigation application under test based on the simulated yaw data and the actual yaw data.

[0093] Optional, such as Figure 14 As shown, the device also includes: a parameter extraction module 104 and a filtering module 105; Module 101 is used to acquire candidate road test data; Parameter extraction module 104 is used to extract road test parameters from the candidate road test data; The filtering module 105 is used to filter the candidate road test data according to the road test parameters, delete candidate road test data whose road test parameters do not meet the admission criteria, and obtain the target road test data; wherein, the admission criteria include at least one of the following: The candidate road test data includes one-to-one corresponding navigation route data and actual driving trajectory data; The candidate road test data shall include at least the following road test parameters: driving start time, driving end time, actual vehicle yaw time, navigation application prompt yaw time, and road morphology; The start time of the journey is earlier than the actual vehicle yaw time; the actual vehicle yaw time is earlier than the end time of the journey. The actual driving time corresponding to the candidate road test data is greater than or equal to the preset time. When the actual vehicle yaw time is empty, it is determined that the candidate road test data corresponds to the actual vehicle yaw unidentified situation; The candidate road test data meets the test data collection specifications corresponding to the test scenario.

[0094] Optional, such as Figure 14 As shown, the device also includes: Scene data construction module 106 is used to construct test scene data corresponding to the test scene according to the preset test scene model and the target road test data. The task creation module 107 is used to select the test scenario data and the navigation application to be tested, and create the test task.

[0095] Optionally, the execution module 102 is used to replay the test scenario based on the test scenario data to simulate the actual driving scenario corresponding to the navigation route data and the actual driving trajectory data; determine the actual deviation point based on the actual deviation data; obtain the replay log of the test scenario; print the replay log to obtain the simulated deviation data corresponding to the actual deviation point.

[0096] Optionally, the generation module 103 is used to generate at least one of the following yaw test metrics: The simulated yaw count is obtained based on the simulated yaw data, the active yaw count is obtained based on the actual yaw data, and the yaw success rate of the navigation application under test is calculated by the simulated yaw count and the actual yaw count. The simulated yaw time is obtained based on the simulated yaw data. Based on the actual yaw data, it is determined whether the yaw sent at the simulated yaw time is a false yaw. The number of false yaws corresponding to the navigation application under test is counted. Based on the simulated yaw data, the simulated yaw time, simulated yaw alert time, and the number of test scenarios with successful yaw are obtained, and the average yaw response time corresponding to the navigation application under test is calculated. Based on the scenario type corresponding to the test scenario data, calculate at least one of the following yaw test indicators for the navigation application under the scenario type: yaw success rate, number of false yaws, and average yaw response time.

[0097] Optional, such as Figure 14 As shown, the device also includes: a yaw correction module 108; The acquisition module 101 is used to acquire simulated erroneous deviation data corresponding to the erroneous deviation of the navigation application under test during the test. The veer error repair module 108 is used to repair the veer error of the navigation application under test based on the simulated veer error data. Execution module 102 is used to perform the test task on the repaired navigation application under test on the test bench; The generation module 103 is used to determine that the repair of the navigation application under test is successful when the repaired navigation application under test does not erroneously during the test.

[0098] Optionally, the generation module 103 is used to calculate the error correction rate of the navigation application under test based on the number of successful repairs and the number of test scenarios in which erroneous deviations occurred during the test.

[0099] Optionally, the deviance correction module 108 is used to obtain the replay logs corresponding to the test scenario in which the navigation application under test experiences a deviance error; to print and analyze the replay logs to determine the deviance error type corresponding to the simulated deviance error data; to locate the code module and code repair strategy corresponding to the deviance error type in the navigation application under test; and to repair the code module according to the code repair strategy to obtain the navigation application under test after deviance error correction.

[0100] Optionally, the acquisition module 101 is used to acquire the display data corresponding to the navigation car logo on the navigation interface when the navigation application under test is running; the generation module 103 is used to determine the display test result of the navigation car logo based on the test scenario and the display data.

[0101] The apparatus of this application embodiment acquires a test task corresponding to a navigation application under test. The test task is created based on target road test data corresponding to multiple test scenarios. The target road test data includes navigation route data obtained from real vehicle road tests, actual driving trajectory data, and actual operation data corresponding to the function under test. The navigation application under test is run on a test bench to execute the test task, simulating driving in the actual driving scenarios corresponding to the target road test data to obtain simulated road test data. Based on the simulated road test data and the actual operation data, test results for the function under test corresponding to the navigation application under test are generated. By using existing real vehicle road test data, simulation tests are performed on the navigation application under test on a test bench to evaluate the function under test. This avoids the need for real-vehicle road testing after each feature modification or software upgrade of the navigation application. It eliminates the heavy reliance on real-vehicle road testing to discover and fix positioning and recognition issues in various navigation functions. This can greatly improve testing efficiency, shorten the testing cycle, and reduce the time and manpower costs of navigation application testing. Furthermore, existing real-vehicle road test data can be reused for testing more navigation functions, ensuring that each version of the navigation application can be tested in all scenarios, thus improving the accuracy and comprehensiveness of navigation application testing.

[0102] Figure 15 This is a block diagram of a navigation application function testing system provided in an embodiment of this application. Figure 15 As shown, the system includes a navigation test server 11 and a test bench 12.

[0103] The navigation test server 11 is used to store target road test data corresponding to multiple test scenarios, create test tasks corresponding to the navigation application under test based on the target road test data, and send the test tasks to the test bench 12; wherein, the target road test data includes navigation route data obtained from real vehicle road tests, actual driving trajectory data, and actual operation data corresponding to the function under test; Test bench 12 is used to run the navigation application under test to perform test tasks, simulate driving scenarios corresponding to the target road test data, obtain simulated road test data, and upload the simulated road test data to navigation test server 11; The navigation test server 11 is also used to generate test results for the corresponding functions of the navigation application under test based on simulated road test data and actual operation data.

[0104] The navigation test server 11 includes: a data storage unit 111, used to store real vehicle road test data reported by the test vehicle navigation system; The data quality detection unit 112 is used to perform data quality detection on the actual vehicle road test data, delete data that does not meet the requirements, and store the data that meets the requirements as the target road test data in the data storage unit 111. The data processing unit 113 is used to generate test scenario data from target road test data according to a preset test scenario model, and then construct an evaluation data set. The test task system 114 is used to create test tasks based on the selected navigation application to be tested and test scenario data, and send the test tasks to the test bench 12.

[0105] After the test bench 12 completes the test, it uploads the simulated road test data to the test task system 114. The test task system 114 generates the test results of the corresponding function of the navigation application under test based on the simulated road test data and the actual operation data, and outputs a test report. The test task system 114 can also display the test results and bench playback process on an interface for testers to view.

[0106] In summary, the technical solution of this application embodiment utilizes existing real-vehicle road test data to conduct simulation tests on a bench testing device to evaluate the functions of the navigation application under test. This avoids the need for real-vehicle road testing after each function modification or software upgrade, and eliminates the heavy reliance on real-vehicle road testing to discover and fix positioning and recognition problems in various navigation functions. This significantly improves testing efficiency, shortens the testing cycle, and reduces the time and manpower costs of navigation application testing. Furthermore, the existing real-vehicle road test data can be reused for testing more navigation functions, ensuring that each navigation application version can undergo testing covering all scenarios, thus improving the accuracy and comprehensiveness of navigation application testing.

[0107] This application also provides an electronic device, such as... Figure 16 As shown, the electronic device may include: a processor 1501, a communication interface 1502, a memory 1503, and a communication bus 1504, wherein the processor 1501, the communication interface 1502, and the memory 1503 communicate with each other through the communication bus 1504.

[0108] Memory 1503 is used to store computer programs; When the processor 1501 executes the computer program stored in the memory 1503, it implements the steps of the method embodiments described above.

[0109] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0110] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0111] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0112] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0113] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method embodiments.

[0114] It should be noted that the above-described embodiments of the apparatus, electronic devices, and computer-readable storage media are basically similar to the method embodiments, so the descriptions are relatively simple. For relevant details, please refer to the descriptions of the method embodiments.

[0115] It should be further clarified that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0116] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method of testing a navigation application function, characterized by, include: The test task corresponding to the navigation application under test is obtained. The test task is created based on target road test data corresponding to multiple test scenarios. The target road test data includes navigation route data, actual driving trajectory data and actual operation data corresponding to the function under test obtained from real vehicle road tests. The function under test includes: deviation warning. The actual operation data includes the records made by the testers on the execution of the function under test during the real vehicle road test, including records of active deviation and / or records of erroneous deviation. The test task is performed by running the navigation application under test on the test bench. Driving simulation is performed on the actual driving scenario corresponding to the target road test data to obtain the simulated road test data corresponding to the function under test. The simulated road test data includes simulated yaw data, which includes data corresponding to the actual yaw points during the execution of the test task. Obtain the simulated road test data corresponding to the actual operation data from the simulated road test data. Based on the actual operation data and its corresponding simulated road test data, and taking the actual operation data as the truth value corresponding to the function under test, generate the test result of the navigation application under test.

2. The method of claim 1, wherein, The actual operational data includes actual yaw data; Based on the actual operation data and its corresponding simulated road test data, and using the actual operation data as the truth value corresponding to the function under test, test results for the navigation application under test are generated, including: Based on the simulated yaw data and the actual yaw data, the yaw test results corresponding to the navigation application under test are generated.

3. The method of claim 2, wherein, The method further includes: Obtain candidate road test data; Extract drive test parameters from the candidate drive test data; The candidate road test data is filtered based on the road test parameters, and candidate road test data whose road test parameters do not meet the admission criteria are deleted to obtain the target road test data; wherein, the admission criteria include at least one of the following: The candidate road test data includes one-to-one corresponding navigation route data and actual driving trajectory data; The candidate road test data shall include at least the following road test parameters: driving start time, driving end time, actual vehicle yaw time, navigation application prompt yaw time, and road morphology; The start time of the journey is earlier than the actual vehicle yaw time; the actual vehicle yaw time is earlier than the end time of the journey. The actual driving time corresponding to the candidate road test data is greater than or equal to the preset time. When the actual vehicle yaw time is empty, it is determined that the candidate road test data corresponds to the actual vehicle yaw unidentified situation; The candidate road test data meets the test data collection specifications corresponding to the test scenario.

4. The method of claim 2, wherein, The method further includes: According to the preset test scenario model, test scenario data corresponding to the test scenario is constructed using the target road test data; Select the test scenario data and the navigation application to be tested, and create the test task.

5. The method of claim 4, wherein, The navigation application under test is run on a test bench to perform the test task. Driving simulation is conducted based on the navigation route data and actual driving trajectory data to obtain simulated road test data corresponding to the function under test, including: The test scenario is replayed based on the test scenario data to simulate the actual driving scenario corresponding to the navigation route data and the actual driving trajectory data; Determine the actual deviation point based on the actual deviation data; Obtain the replay logs of the test scenario; Print the playback log to obtain the simulated yaw data corresponding to the actual yaw point.

6. The method of claim 4, wherein, The step of generating yaw test results for the navigation application under test based on the simulated yaw data and the actual yaw data includes generating at least one of the following yaw test metrics: The simulated yaw count is obtained based on the simulated yaw data, the active yaw count is obtained based on the actual yaw data, and the yaw success rate of the navigation application under test is calculated by the simulated yaw count and the actual yaw count. The simulated yaw time is obtained based on the simulated yaw data. Based on the actual yaw data, it is determined whether the yaw sent at the simulated yaw time is a false yaw. The number of false yaws corresponding to the navigation application under test is counted. Based on the simulated yaw data, the simulated yaw time, simulated yaw alert time, and the number of test scenarios with successful yaw are obtained, and the average yaw response time corresponding to the navigation application under test is calculated. Based on the scenario type corresponding to the test scenario data, calculate at least one of the following yaw test indicators for the navigation application under the scenario type: yaw success rate, number of false yaws, and average yaw response time.

7. The method according to claim 2, characterized in that, The method further includes: When the navigation application under test errs during the test, the simulated erroneous deviation data corresponding to the erroneous deviation of the navigation application under test is obtained; The navigation application under test is corrected for erroneous deviations based on the simulated erroneous deviation data. The test task is performed on the repaired navigation application under test on the test bench. If the repaired navigation application does not erroneously during the test, the repair of the navigation application is considered successful.

8. The method according to claim 7, characterized in that, The step of generating yaw test results for the navigation application under test based on the simulated yaw data and the actual yaw data includes: The error correction rate of the navigation application under test is calculated based on the number of successful repairs and the number of test scenarios in which erroneous deviations occurred during the test.

9. The method according to claim 7, characterized in that, The step of correcting the navigation application under test based on the simulated erroneous deviation data includes: Obtain the playback logs corresponding to the test scenario where the navigation application under test erroneously deviates from its course; The playback logs are printed and analyzed to determine the type of mis-yawing data corresponding to the simulated mis-yawing data; Locate the code module corresponding to the aforementioned veergence error type in the navigation application under test and the code repair strategy; The code module is repaired according to the code repair strategy to obtain the navigation application under test after the erroneous deviation is repaired.

10. The method according to claim 1, characterized in that, The method further includes: Obtain the display data corresponding to the navigation car logo on the navigation interface when the navigation application under test is running; The display test result of the navigation car logo is determined based on the test scenario and the display data.

11. A navigation application function testing device, characterized in that, include: The acquisition module is used to acquire the test tasks corresponding to the navigation application under test. The test tasks are created based on target road test data corresponding to multiple test scenarios. The target road test data includes navigation route data, actual driving trajectory data, and actual operation data corresponding to the function under test obtained from real vehicle road tests. The function under test includes: deviation warning. The actual operation data includes the records made by the testers on the execution of the function under test during the real vehicle road test, including records of active deviation and / or records of erroneous deviation. The execution module is used to run the navigation application under test to perform the test task, simulate driving in the actual driving scenario corresponding to the target road test data, and obtain simulated road test data corresponding to the function under test; the simulated road test data includes simulated yaw data, which includes data corresponding to the actual yaw points during the execution of the test task. The generation module is used to obtain the simulated road test data corresponding to the actual operation data from the simulated road test data, and generate the test result of the navigation application under test based on the actual operation data and its corresponding simulated road test data, with the actual operation data as the truth value corresponding to the function under test.

12. A navigation application function testing system, characterized in that, include: Navigation test server and test bench; The navigation test server is used to store target road test data corresponding to multiple test scenarios, create test tasks corresponding to the navigation application under test based on the target road test data, and send the test tasks to the test bench; wherein, the target road test data includes navigation route data, actual driving trajectory data, and actual operation data corresponding to the function under test obtained from real vehicle road tests; the function under test includes: deviation warning; the actual operation data includes records made by testers during the real vehicle road tests of the function under test, including records of active deviation and / or records of erroneous deviation; The test bench is used to run the navigation application under test to perform the test task, simulate driving in the actual driving scenario corresponding to the target road test data, obtain simulated road test data corresponding to the function under test, and upload the simulated road test data to the navigation test server; the simulated road test data includes simulated yaw data, which includes data corresponding to the actual yaw points during the execution of the test task. The navigation test server is further configured to acquire simulated road test data corresponding to the actual operation data from the simulated road test data, and generate test results for the navigation application under test based on the actual operation data and its corresponding simulated road test data, with the actual operation data as the truth value corresponding to the function under test.

13. An electronic device, characterized in that, include: The processor, communication interface, memory, and communication bus are connected, with the processor, communication interface, and memory communicating with each other via the communication bus. The memory is used to store computer programs; When the processor executes the computer program, it implements the steps of the method described in any one of claims 1-10.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method described in any one of claims 1-10.

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