Vehicle parking test, parking anomaly management method and computer equipment

Abstract

This application provides a vehicle parking test, parking anomaly management method, and computer equipment. The vehicle parking test method includes: generating a visualization interface based on perception information of a target vehicle during parking; the visualization interface includes a panoramic image sub-interface displaying a panoramic image of the target vehicle and an electronic map sub-interface displaying mapping information of the panoramic image of the target vehicle; determining anomalies and their anomaly information based on the visualization interface; generating an anomaly object file based on the anomaly information; and generating an anomaly detection video based on the anomaly object file, for use in obtaining the first parking perception test result of the target vehicle based on the anomaly detection video. Thus, based on the visualization interface, anomalies can be identified more intuitively, leading to the anomaly detection video, enabling more intuitive analysis of vehicle parking problems, obtaining parking perception test results, and improving the accuracy of vehicle perception.

Description

Technical Field

[0001] This application relates to the field of vehicle testing technology, and in particular to a vehicle parking test, parking anomaly management method, and computer equipment. Background Technology

[0002] With the development of automotive intelligence, vehicle parking technology has gradually evolved from traditional driver-controlled parking to intelligent parking such as assisted parking and automatic parking. Early autonomous parking methods relied primarily on the driver observing the parking environment and making relevant judgments to achieve parking. Current intelligent parking methods mainly use devices such as reversing radar and reversing cameras to perceive the parking environment, obtain sensory information, and then perform intelligent parking based on that information.

[0003] Whether it's assisted parking or automatic parking technology, intelligent parking technology relies on vehicle perception technology to perceive the parking environment. If the perception information is inaccurate, it can lead to safety hazards for the driver when using assisted parking based on the perception information, or it can lead to safety hazards for the vehicle when using automatic parking based on the perception information.

[0004] Therefore, it is necessary to test vehicle parking technology to obtain test results on perception accuracy. This allows developers to analyze problems based on the test results and optimize the vehicle parking technology. The key is to find a way to test vehicle parking technology effectively and obtain more intuitive test results on perception accuracy so that developers can more clearly analyze parking problems, identify anomalies, and optimize the technology to improve vehicle perception accuracy. Summary of the Invention

[0005] This application provides a vehicle parking test, parking anomaly management method, and computer device, which can more accurately display the perception information of vehicle parking tests, obtain more intuitive test results, and thus, based on the more intuitive test results, more intuitively analyze vehicle parking problems, find anomalies, optimize vehicle parking technology, and improve the perception accuracy of vehicles.

[0006] To address the aforementioned technical problems, in a first aspect, embodiments of this application provide a vehicle parking test method. The method includes: acquiring perception information of a target vehicle during parking; generating and displaying a visualization interface based on the perception information, the visualization interface including a panoramic image sub-interface and an electronic map sub-interface, the panoramic image sub-interface displaying a panoramic image of the target vehicle, and the electronic map sub-interface displaying mapping information of the panoramic image of the target vehicle; upon determining an abnormal object and corresponding abnormal information based on the visualization interface, generating an abnormal object file based on the abnormal information corresponding to the abnormal object; and generating an abnormal detection video based on the abnormal object file, for use in obtaining a first parking perception test result of the target vehicle based on the abnormal detection video.

[0007] Using the above technical solution, a panoramic image sub-interface and an electronic map sub-interface are generated based on the perception information obtained by the target vehicle during parking. These are used to display panoramic images. Anomalies and their anomaly information are then identified based on the panoramic image sub-interface and the electronic map sub-interface. An anomaly object file is generated based on the anomaly information, and an anomaly detection video is generated based on the anomaly object file. Finally, the first parking perception test result of the target vehicle is obtained based on the anomaly detection video. Thus, identifying anomalies based on a visual interface allows for more intuitive identification. Furthermore, generating an anomaly detection video from the anomaly object file enables more intuitive analysis of vehicle parking problems, further improving the accuracy of vehicle perception.

[0008] In one possible implementation of the first aspect above, the perceived information includes vehicle surrounding environment information and vehicle location information. The generation of a visualization interface based on the perceived information includes: obtaining a panoramic image based on the vehicle surrounding environment information to generate a panoramic image sub-interface; and generating an electronic map sub-interface within a preset area based on the vehicle location information and the panoramic image.

[0009] By adopting the above technical solution, a panoramic image sub-interface is generated based on the vehicle's surrounding environment information, and an electronic map sub-interface is generated within a preset area based on the vehicle's location information and the panoramic image. This results in a more comprehensive display of perception information and is more conducive to the analysis of perception anomalies.

[0010] In one possible implementation of the first aspect above, determining the abnormal object and the abnormal information corresponding to the abnormal object based on the visual interface includes: receiving the abnormal marking operation performed by the user based on the visual interface, and determining the abnormal object and the abnormal information corresponding to the abnormal object based on the abnormal marking operation.

[0011] In one possible implementation of the first aspect above, receiving abnormal point-marking operations performed by the user through the visual interface includes: displaying the point-marking interface based on the point-marking trigger operation performed by the user through the visual interface; and receiving the abnormal point-marking operation performed by the user on the point-marking interface.

[0012] By adopting the above technical solution, users can directly mark anomalies on the visual interface, which can more intuitively identify the abnormal objects and their corresponding abnormal information, and can more accurately perform anomaly detection and analysis.

[0013] In one possible implementation of the first aspect above, obtaining the first parking perception test result of the target vehicle based on the anomaly detection video includes: sending the anomaly detection video to the corresponding user so that the user can perform anomaly detection analysis based on the anomaly detection video to obtain the first parking perception test result of the target vehicle; or directly performing anomaly detection analysis based on the anomaly detection video to obtain the first parking perception test result of the target vehicle.

[0014] By adopting the above technical solution, the first parking perception test result of the target vehicle can be obtained based on the anomaly detection video, which can provide a more intuitive and convenient way to obtain the parking perception test result.

[0015] In one possible implementation of the first aspect above, the exception information of the exception object includes test information, target obstacle, exception type, problem description, video length, test package, and test log path.

[0016] In one possible implementation of the first aspect described above, the exception object file is a comma-separated value file.

[0017] In one possible implementation of the first aspect above, the method further includes: fusing the panoramic image sub-interface and the electronic map sub-interface to obtain and display an auxiliary parking image for use in controlling the target vehicle to park in the target parking space based on the auxiliary parking image; determining parking result information of the target vehicle parking in the target parking space; and obtaining a second parking perception test result of the target vehicle based on the parking result information.

[0018] By adopting the above technical solution, the test vehicle can perform intelligent parking tests based on the parking result information obtained from the auxiliary parking image.

[0019] In one possible implementation of the first aspect above, the method further includes: determining the perception location information related to the target vehicle and the target obstacle when the target vehicle is located at any target position, wherein the perception location information includes multiple first position information of the target obstacle when the target vehicle is located at each target position obtained based on a perception algorithm and second position information of the target obstacle determined based on the distance information between the target vehicle and the target obstacle when the target vehicle is located at each target position obtained by manual measurement; and obtaining a third parking perception test result of the target vehicle based on the multiple first position information and the second position information.

[0020] By employing the above technical solution to test the location information of obstacles perceived by the target vehicle, the vehicle's perception technology can be better optimized.

[0021] In one possible implementation of the first aspect above, multiple first position information of the target obstacle is obtained based on a perception algorithm when the target vehicle is located at each target position, including: determining the target obstacle region in the panoramic image sub-interface based on a deep learning model; detecting the target obstacle region to obtain multiple first position information of the target obstacle.

[0022] In one possible implementation of the first aspect described above, when the target obstacle includes the corner points of the target parking space, the method further includes obtaining the distance information between the target vehicle and the corner points of the target parking space when the target vehicle is in the target position as follows: when the target vehicle is in the target position, a coordinate system is established with the center point of the rear axle of the target vehicle as the origin, the forward direction of the target vehicle as the x-axis, and the direction perpendicular to the x-axis as the y-axis; the coordinate information of the left rear wheel and the right rear wheel of the target vehicle is determined; the distance information between the left rear wheel of the target vehicle and the first corner point of the target parking space, and the distance information between the right rear wheel of the target vehicle and the first corner point of the target parking space are determined; the distance information between the left rear wheel or the right rear wheel and the other corner points of the target parking space except for the first corner point is determined.

[0023] In one possible implementation of the first aspect described above, the first position information is first coordinate information, and the second position information is second coordinate information. Based on multiple first and second position information, a third parking perception test result for the target vehicle is obtained, including: determining multiple first differences between multiple first coordinate information and the first values ​​corresponding to the first direction in the second coordinate information, and multiple second differences between the second values ​​corresponding to the second direction; determining the minimum difference, the maximum difference, the average of the first differences, and a first target difference located at a preset proportion after sorting the multiple first differences in ascending order; and determining the minimum difference, the maximum difference, the average of the second differences, and a second target difference located at a preset proportion after sorting the multiple second differences in ascending order; and obtaining the third parking perception test result for the target vehicle based on the target obstacle, the target position, and the minimum difference, the maximum difference, the average of the first differences, the first target difference, and the minimum difference, the maximum difference, the average of the second differences, and the second target difference.

[0024] In one possible implementation of the first aspect above, the method further includes: determining whether the parking environment of the target vehicle conforms to the specifications based on the building information model.

[0025] Secondly, this application also discloses a vehicle parking management method, including: determining parking perception test results, the parking perception test results including a first parking perception test result and a second parking perception test result, the first parking perception test result and the second parking perception test result being obtained based on the vehicle parking test method provided in the first aspect above; and adjusting the parking strategy of the target vehicle according to the parking test results.

[0026] By adopting the above technical solution and adjusting the parking strategy of the target vehicle based on the parking test results, the vehicle parking technology can be optimized and the user experience improved.

[0027] Thirdly, this application also discloses a computer device, including: a processor and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory, so that the computer device implements the vehicle parking test method provided in any of the implementations of the first aspect above, or implements the vehicle parking management method provided in the second aspect above.

[0028] Fourthly, this application also discloses a computer-readable storage medium storing a computer program that can be executed by a computer device to implement the vehicle parking test method provided in any of the implementations of the first aspect, or the vehicle parking management method provided in the second aspect.

[0029] Fifthly, this application also discloses a computer program product, including a computer program, which, when executed by a computer device, implements the vehicle parking test method provided by any of the implementations of the first aspect above, or the vehicle parking management method provided by the second aspect above. Attached Figure Description

[0030] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0031] Figure 1 This is a schematic flowchart of a vehicle parking test method provided in an embodiment of the present invention;

[0032] Figure 2 A schematic diagram of a visual interface provided in an embodiment of the present invention;

[0033] Figure 3 A schematic diagram of a dot-marking interface provided in an embodiment of the present invention;

[0034] Figure 4 A schematic diagram of an exception object file provided in an embodiment of the present invention;

[0035] Figure 5 This is another flowchart illustrating the vehicle parking test method provided in an embodiment of the present invention;

[0036] Figure 6 A schematic diagram illustrating the sensing distance testing principle provided in an embodiment of the present invention;

[0037] Figure 7 This is a schematic diagram illustrating the distance information between the target vehicle and the target parking space provided in an embodiment of the present invention.

[0038] Figure 8 A schematic diagram of the third parking perception test results provided in an embodiment of the present invention;

[0039] Figure 9 This is a schematic flowchart of a vehicle parking management method provided in an embodiment of the present invention;

[0040] Figure 10 This is a schematic diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0041] As mentioned earlier, vehicle parking technology needs to be tested to obtain test results on perception accuracy. This allows developers to analyze problems based on the test results and optimize the vehicle parking technology. The key is to find a way to test vehicle parking technology effectively and obtain more intuitive test results on perception accuracy so that developers can more easily analyze parking problems, identify anomalies, and optimize the technology to improve vehicle perception accuracy.

[0042] Based on this, this application provides a vehicle parking testing method. It generates a visual interface based on the vehicle's perception information during the parking process, identifies abnormal objects and their corresponding anomaly information within the visual interface, generates an anomaly object file, generates an anomaly detection video based on the anomaly object file, and then obtains the vehicle's parking perception test results based on the anomaly detection video. Thus, the visual interface allows for more intuitive analysis of vehicle parking problems and identification of anomalies, while the anomaly detection video facilitates more intuitive vehicle parking optimization for developers.

[0043] like Figure 1 As shown, the vehicle parking test method provided in this application specifically includes the following steps.

[0044] S100: Acquire perception information of the target vehicle during the parking and driving process.

[0045] S200 generates and displays a visualization interface based on perceived information. The visualization interface includes a panoramic image sub-interface and an electronic map sub-interface. The panoramic image sub-interface displays a panoramic image of the target vehicle, and the electronic map sub-interface displays the mapping information of the panoramic image of the target vehicle.

[0046] S300, after determining the abnormal object and the corresponding abnormal information based on the visual interface, generates an abnormal object file based on the abnormal information corresponding to the abnormal object.

[0047] S400 generates an anomaly detection video based on the anomaly object file, which is used to obtain the first parking perception test result of the target vehicle based on the anomaly detection video.

[0048] First, step S100 is executed to obtain the perception information of the target vehicle during the parking process, wherein the perception information includes the surrounding environment information of the vehicle and the vehicle position information.

[0049] The vehicle's surrounding environment information is obtained by capturing images from the vehicle's 360-degree panoramic camera. The vehicle's location information is obtained by the vehicle positioning module. Specifically, the vehicle positioning module can be a high-precision positioning module.

[0050] Next, step S200 is executed, a visualization interface is generated based on the perceived information, and the visualization interface is displayed.

[0051] like Figure 2 As shown, the visualization interface includes panoramic image sub-interfaces (B, D, F) and electronic map sub-interfaces (A, C, E).

[0052] The panoramic imaging sub-interface displays a panoramic image of the target vehicle, such as... Figure 2 As shown, the panoramic image includes a panoramic view obtained based on the captured image, as well as vehicles, obstacles, and parking spaces in the panoramic view.

[0053] The electronic map sub-interface displays the mapping information of the panoramic image of the target vehicle, such as... Figure 2 As shown, the mapping information includes vehicles, obstacles, parking space numbers, and corner coordinates of each parking space. It also includes vehicle trajectory information.

[0054] The electronic map sub-interface also displays other alternative parking spaces beyond the panoramic image range, expanding the range of parking space selection and improving the user experience.

[0055] In the implementation of this application, the visualization interface is generated based on the perceived information, including: obtaining a panoramic image based on the vehicle's surrounding environment information to generate a panoramic image sub-interface, and generating an electronic map sub-interface within a preset area based on the vehicle's location information and the panoramic image.

[0056] For example, a panoramic image of the target vehicle is displayed, and parking spaces, corner points of the parking spaces, parking space numbers, and obstacles in the panoramic image are marked and displayed to form a panoramic image sub-interface.

[0057] Furthermore, based on the vehicle's location information and the panoramic image, the vehicle and the panoramic image are filled and mapped, and parking spaces that are not fully displayed in the panoramic image are completed, generating an electronic map sub-interface. And, as... Figure 2 As shown, A is the single-frame display interface, and C is the fused display interface generated by mapping and fusion based on the F-graph.

[0058] Next, step S300 is executed: after determining the abnormal object and the abnormal information corresponding to the abnormal object based on the visual interface, an abnormal object file is generated based on the abnormal information corresponding to the abnormal object.

[0059] In the implementation of this application, determining the abnormal object and the corresponding abnormal information based on the visual interface includes: receiving the abnormal point operation performed by the user based on the visual interface, and determining the abnormal object and the corresponding abnormal information based on the abnormal point operation.

[0060] The exception information for the exception object includes the test time, test object, exception type, problem description, video length, test package, and test log path.

[0061] Furthermore, receiving abnormal point-marking operations performed by the user through the visual interface includes: displaying the point-marking interface based on the point-marking trigger operation performed by the user through the visual interface; and receiving abnormal point-marking operations performed by the user on the point-marking interface.

[0062] For example, when a user determines that an object is displayed incorrectly based on the visual interface, they can click on the corresponding area to trigger an error marker operation, which will then redirect them to a page such as... Figure 3 The interface shown automatically fills in the time displayed in the visualization interface for the corresponding panoramic image and its mapping information, generates the test time, and determines the test object corresponding to the user's clicked area. The test object is automatically filled in; if the test object is incorrectly filled, the user can modify it. The corresponding log / bag file path is also retrieved, allowing the user to select the exception type, video length, and fill in a problem description. Of course, if... Figure 3 As shown, users can also delete or copy abnormal information.

[0063] Of course, users can also add new data, import historical data, describe test data, visualize test data, and save data.

[0064] When the user clicks "Save Data", a process like this is generated: Figure 4 The exception object file shown.

[0065] In this implementation, the exception object file is a comma-separated values ​​file (CSV file).

[0066] like Figure 4 As shown, the CSV file includes exception information for the exception object.

[0067] Next, step S400 is executed to generate an anomaly detection video based on the anomaly object file, which is used to obtain the first parking perception test result of the target vehicle based on the anomaly detection video.

[0068] In one implementation of this application, obtaining the first parking perception test result of the target vehicle based on the anomaly detection video includes: sending the anomaly detection video to the corresponding user so that the user can perform anomaly detection analysis based on the anomaly detection video to obtain the first parking perception test result of the target vehicle.

[0069] For example, anomaly detection videos for each anomaly object are generated based on the test time, test object, problem description, video length, and bag / log path in the anomaly object file. This allows developers (i.e., users) to intuitively study the anomaly detection videos to determine the parking perception problem of the target vehicle (as an example of the first parking perception test results).

[0070] The anomaly detection video file name includes information such as the test object, anomaly type, and problem description. For example, the anomaly detection video file name is [Wall_Wall Missing_Corner Wall Not Detected 3_8815.mp4].

[0071] In another implementation of this application, obtaining the first parking perception test result of the target vehicle based on the anomaly detection video includes: directly performing anomaly detection analysis based on the anomaly detection video to obtain the first parking perception test result of the target vehicle.

[0072] For example, an anomaly detection tool performs anomaly detection analysis based on anomaly detection videos to identify anomaly points in order to obtain the parking perception problem of the target vehicle (as an example of the first parking perception test results).

[0073] This application implements a parking scenario perception result visualization problem discovery tool. It generates a visual interface based on vehicle perception information and displays this information on the vehicle's large screen to demonstrate the entire process of a fully automated parking scenario, from the start of parking until the vehicle is parked in the target space. It also designs a real-time parking scenario perception problem recording and archiving tool to record anomaly information of user-identified abnormal objects, generating corresponding abnormal object files. This allows for the generation of anomaly detection videos based on the anomaly object files, and the determination of the first parking perception test result based on the anomaly detection videos. Thus, based on the visual interface, anomaly object analysis can be performed intuitively, and the anomaly detection videos enable more intuitive parking test analysis to obtain parking perception test results.

[0074] like Figure 5 As shown, in another implementation of this application, the vehicle parking test method provided by this application further includes the following steps.

[0075] The S500 integrates the panoramic image sub-interface and the electronic map sub-interface to obtain and display auxiliary parking images, which are then used to control the target vehicle to park in the target parking space based on the auxiliary parking images.

[0076] S600 determines the parking result information of the target vehicle being parked in the target parking space.

[0077] Based on the parking result information, the S700 obtains the second parking perception test result of the target vehicle.

[0078] In this application, the panoramic image sub-interface and the electronic map sub-interface are displayed together as an automatic parking image (i.e., an auxiliary parking image), so that the target vehicle can park in the target parking space according to the auxiliary parking image, the parking result information of the target vehicle parking in the target parking space is determined, and then the second parking perception test result of the target vehicle is determined according to the parking result information.

[0079] The parking result information includes the target vehicle's current location information and current panoramic image information. The second parking perception test results include the target vehicle being fully parked in the target parking space, the target vehicle not being fully parked in the target parking space, and the target vehicle being parked in the wrong parking space.

[0080] Alternatively, the parking result information can be the coordinates of the target vehicle and the four corner points of the target parking space, so as to determine the second parking perception result of the target vehicle based on the coordinates of the target vehicle and the coordinates of the four corner points.

[0081] Furthermore, in assisted parking scenarios, the vehicle needs to perceive the distance to the target obstacle in order to plan the parking path in real time based on the perceived distance information. Therefore, it is necessary to test the distance information perceived by the target vehicle.

[0082] For example, testing the distance perception of a target vehicle to the corner of a parking space in an underground parking garage can determine whether the distance information perceived by the target vehicle to the corner of the parking space is accurate. Another example is testing the distance perception of a target vehicle to various obstacles in an open area to determine whether the distance information perceived by the target vehicle to the obstacles is accurate.

[0083] Currently, the industry commonly uses Remote Traffic Microwave Sensors (RT devices) to test the distance between target vehicles and target obstacles, obtaining parking perception test results based on the distance information perceived by the target vehicle and the distance information obtained from the RT device. However, this equipment uses Global Positioning System (GPS) for positioning. In underground parking garages, signal strength is poor, making GPS signals unavailable. Therefore, testing with RT devices is not applicable to underground parking garages.

[0084] Furthermore, in existing technologies, signal switching equipment is installed to enhance signal strength in underground parking garages. This allows for the testing of the distance between target vehicles and obstacles using a ground truth (RT) device. However, signal switching equipment is complex to install and expensive, and due to limited signal coverage, it can only test a few parking spaces. Moreover, since distance perception relies on the signal coverage strength of the switching equipment, measurement accuracy errors can occur when signal coverage is poor.

[0085] Based on this, the present application provides a vehicle parking test method for testing the perception distance of a target vehicle to a target obstacle. The method includes determining the perception position information related to the target vehicle and the target obstacle when the target vehicle is located at any target position. The perception position information includes multiple first position information of the target obstacle when the target vehicle is located at each target position based on a perception algorithm and second position information of the target obstacle determined based on the distance information between the target vehicle and the target obstacle when the target vehicle is located at each target position obtained by manual measurement. Based on the multiple first position information and the second position information, a third parking perception test result of the target vehicle is obtained.

[0086] In the implementation of this application, when parking, when the target vehicle is at the target position, it also senses the first coordinate information of the target obstacle (as an example of the first position information). It needs to determine the second coordinate information of the target obstacle (as an example of the second position information) based on the distance information between the target vehicle and the target obstacle measured manually. The first coordinate information sensed by the target vehicle is tested according to the second coordinate information to obtain the third parking perception test result, so as to determine the accuracy of the target vehicle's distance perception of the target obstacle on the x-axis and y-axis according to the third parking perception test result.

[0087] In this system, a coordinate system is established with the center of the rear axle of the target vehicle as the origin, the forward direction of the target vehicle as the x-axis, and the direction perpendicular to the x-axis as the y-axis. The distances of the target vehicle relative to the target obstacle in the x-axis direction and the distances of the target vehicle relative to the target obstacle in the y-axis direction are obtained when the target vehicle is at the target position. Based on the distances in the x-axis direction and the y-axis direction, the first coordinate information of the target obstacle when the target vehicle is at the target position is obtained.

[0088] In the implementation of this application, multiple first coordinate information of the target vehicle and the target obstacle are obtained based on the perception algorithm when the target vehicle is located at each target position.

[0089] First, the target obstacle region in the panoramic image sub-interface is determined based on the deep learning model. A coordinate system is established with the rear axle center of the vehicle as the origin. The target obstacle region is detected to obtain multiple first coordinate information (x, y) of the target obstacle when the target vehicle is in the target position.

[0090] For example, based on an image recognition model (as an example of a deep learning model), the images in the panoramic image sub-interface are analyzed during multiple tests to obtain the target obstacle region, and the coordinates of the target obstacle are identified based on a perception algorithm to obtain the first coordinate information of the corresponding target obstacle.

[0091] Furthermore, in the implementation of this application, when the target obstacle includes the corner points of the target parking space, the distance information between the target vehicle and the corner points of the target parking space when the target vehicle is in the target position is obtained in the following way: When the target vehicle is in the target position, a coordinate system is established with the center point of the rear axle of the target vehicle as the origin, the forward direction of the target vehicle as the x-axis, and the direction perpendicular to the x-axis as the y-axis; the coordinate information of the left rear wheel and the right rear wheel of the target vehicle is determined, and the distance information between the left rear wheel of the target vehicle and the first corner point of the target parking space, the distance information between the right rear wheel of the target vehicle and the first corner point of the target parking space, and the distance information between the left rear wheel / right rear wheel of the target vehicle and the other corner points of the target parking space other than the first corner point are obtained based on manual measurement.

[0092] For example, a coordinate system is established with the center point of the rear axle of the vehicle as the origin O, the direction of vehicle movement as the x-axis, and the direction perpendicular to the x-axis as the y-axis.

[0093] like Figure 6 Part (a) shows and Figure 6 As shown in section (b), given the vehicle width, the left rear wheel R1 is located on the y-axis with coordinates (0, 1). (Vehicle width), the right rear wheel R2 is located on the y-axis, with coordinates (0, ...). (Vehicle width).

[0094] With the target vehicle located at target position 1 and its driving trajectory shown as 1, 2, and 3, the distance d1 from the left rear wheel R1 to the corner point A of the target parking space (as an example of the first corner point) and the distance d2 from the right rear wheel to the corner point A of the target parking space are manually measured using a measuring tape. The position information of the target parking space relative to the target vehicle (e.g., whether the target parking space is on the left or right side of the target vehicle) is recorded and stored.

[0095] Furthermore, the distances d3 from the left rear wheel R1 to parking space corner point B (as an example of other corner points), d4 from the left rear wheel R1 to parking space corner point C (as an example of other corner points), and d5 from the left rear wheel R1 to parking space corner point D (as an example of other corner points) are manually measured, stored, and generated as follows: Figure 7 The distance storage file shown.

[0096] like Figure 7As shown, when the target vehicle stops at a certain point (i.e., the target location), for example, when the target vehicle stops at point 1, a new test data is added. The test data includes the start time, end time, bag / log path, parking space type, number of test drives, point number, target parking space number, target parking space location, test type, vehicle width and wheelbase, distance to point A, distance to point B, distance to point C, distance to point D, length and width of the parking space, etc.

[0097] The start time is the time the vehicle remains at the target location, and the end time is the time the vehicle leaves the target location. The start and end times can be automatically generated based on the panoramic imagery during the test. The bag / log path records the calculation results of the perception algorithm automatically generated during the test. The parking space type is selected by the user, such as horizontal, vertical, or angled parking spaces. The number of tests is the number of times the distance between the target vehicle and the corresponding corner point of the same target parking space is tested when the target vehicle is at the same target location. The point is the number of the target vehicle's target location. The target parking space number is the target vehicle's ID number. At any given time, the target vehicle can identify all surrounding parking spaces and record the ID numbers of each parking space in the perception results; the target parking space number is the number of the parking space to be tested. The test type is the parking space corner point accuracy type; different test types call different calculation scripts. Vehicle width and wheelbase are the vehicle's width and wheelbase; for the same vehicle, the width and wheelbase remain constant. The distance to point A is the measured distance between point A and points R1 and R2. The distance to point B is the measured distance between point B and points R1 and / or R2 respectively. The distance to point C is the measured distance between point C and points R1 and / or R2 respectively. The distance to point D is the measured distance between point D and points R1 and / or R2 respectively.

[0098] Furthermore, the above test data is saved to obtain a distance test file, which is convenient for subsequent testing.

[0099] Furthermore, during parking perception testing, as the vehicle moves towards the target parking space, the perception algorithm determines the coordinates of each corner point of the target parking space when the vehicle is at any target position. Since the perception software performs multiple perceptions and tests based on the perception algorithm, the initial coordinates of each corner point of the target parking space when the vehicle is at the target position are obtained from multiple tests, with each test yielding multiple coordinates perceived by the perception software. In this way, multiple initial coordinates of the four corner points of the target parking space (e.g., corner points A, B, C, and D) can be obtained.

[0100] Furthermore, based on the coordinates of the left rear wheel, the right rear wheel, the distance between the left rear wheel and the first corner point, and the distance between the right rear wheel and the first corner point in the stored distance test file, the coordinates of the first corner point are obtained.

[0101] like Figure 6 As shown in section (c), circle 1 is established with the left rear wheel R1 as the origin and d1 as the radius, and circle 2 is established with the right rear wheel R2 as the origin and d2 as the radius. Circle 1 and circle 2 intersect at two points. Based on the location information of the target parking space, the remaining point A is the coordinate information of the corner point A of the target parking space (as an example of the second coordinate information of the first corner point).

[0102] Furthermore, based on the coordinates of the first corner point (i.e., corner point A) and the distances between the left rear wheel and the other corner points of the target parking space (excluding the first corner point), the coordinates of each corner point are determined.

[0103] Taking parking space corner point B as an example, such as Figure 6 As shown in section (d), given the coordinates of corner point A, the coordinates of the left rear wheel R1, and the distance from the left rear wheel R1 to corner point B of the parking space, the coordinate information of corner point B is calculated (as an example of the second coordinate information of other corner points).

[0104] Similarly, we obtain the coordinate information of corner point c and corner point d (as another example of other corner points) (as a second example of coordinate information for other corner points).

[0105] Of course, in the implementation of this application, the coordinate information of each corner point can also be determined based on the coordinate information of the first corner point (i.e., corner point A) and the distance between the right rear wheel and the other corner points of the target parking space excluding the first corner point.

[0106] Furthermore, in the implementation of this application, the third parking perception test result of the target vehicle is obtained based on multiple first location information and second location information, including: determining the difference between each first location information and the second location information; determining the minimum difference, the maximum difference, the average of the differences, and the target difference at a preset ratio after sorting the differences from smallest to largest; and obtaining the third parking perception test result of the target vehicle based on the target obstacle, the target position, the minimum difference, the maximum difference, the average of the differences, and the target difference.

[0107] For example, taking the first position information as the first coordinate information and the second position information as the second coordinate information, when the target vehicle is parking in the target parking space, the perception algorithm based on the perception software will calculate multiple first coordinate information of the four corner points of the target parking space when the target vehicle is located at any target position.

[0108] When the target vehicle is located at the corresponding target position, the distance between the left and right rear wheels relative to corner point A, and the distance between the left rear wheel and corner points B, C, and D are manually measured to generate a distance storage file. Based on the distance storage file, the distance between the left and right rear wheels of the target vehicle at the corresponding target position and the first corner point of the target parking space is determined. The second coordinate information of corner point A when the target vehicle is located at the target position is determined based on the method described above, as well as the second coordinate information of corner points B, C, and D when the target vehicle is located at the target position is determined based on the method described above.

[0109] For the same target location and the same target parking space, determine multiple first differences (first values) corresponding to the x-axis (as an example of the first direction) in multiple first coordinate information and second coordinate information, as well as the minimum difference, maximum difference, average of the first differences, and the first target difference at a preset proportional position after sorting the first differences from smallest to largest. Also determine the differences (second values) corresponding to the y-axis in multiple first coordinate information and second coordinate information, as well as the minimum difference, maximum difference, average of the differences, and the second target difference at a preset proportional position after sorting the differences from smallest to largest.

[0110] The third parking perception test result is obtained based on the target obstacle, the target position, and the minimum difference, maximum difference, average difference and first target difference in the first difference corresponding to the x-axis, and the minimum difference, maximum difference, average difference and second target difference in the second difference corresponding to the y-axis.

[0111] like Figure 8 As shown, taking the values ​​corresponding to the x-axis as an example, the test results are based on the distances of the target vehicle at different target positions (e.g., point 1, point 2, point 3, point 4, point 5) relative to the corner points (e.g., corner A (corner_a), corner B (corner_b), corner C (corner_c), corner D (corner_d)) of the target vehicle in the x-axis direction.

[0112] Wherein, min(m) is the minimum value among the differences in the x-axis direction between the first and second coordinate information obtained based on the perception algorithm during multiple tests. max(m) is the maximum value among the differences in the x-axis direction between the first and second coordinate information obtained based on the perception algorithm. mean(m) is the average value among the differences in the x-axis direction between the first and second coordinate information obtained based on the perception algorithm. std2 is the value within a preset range after sorting the differences in the x-axis direction between the first and second coordinate information obtained based on the perception algorithm from smallest to largest. The preset range can be the value at the 95th position of the sorted differences. Of course, the preset range can be adjusted according to actual needs, for example, it can be accurate to 95.44%.

[0113] For example, for point 1, min(m) is the minimum difference (0.0152) in the x-axis direction between the first coordinate information of corner point A (corner_a) obtained based on the perception algorithm and the second coordinate information of corner point A (corner_a) obtained based on the distance measured manually when the target vehicle is located at point 1 during the parking test. max(m) is the maximum difference (0.2011) in the x-axis direction between the first coordinate information of corner point A (corner_a) obtained based on the perception algorithm and the second coordinate information of corner point A (corner_a) obtained based on the distance measured manually when the target vehicle is located at point 1 during the parking test. mean(m) is the average value (0.062) of the difference in the x-axis direction between the first coordinate information of corner point A (corner_a) obtained based on the perception algorithm and the second coordinate information of corner point A (corner_a) obtained based on the distance of corner point A (corner_a) measured manually when the target vehicle is located at point 1 during the parking test. std2 is the value (0.1975) of the difference in the x-axis direction between the first coordinate information of corner point A (corner_a) obtained based on the perception algorithm and the second coordinate information of corner point A (corner_a) obtained based on the distance of corner point A (corner_a) measured manually, after sorting from smallest to largest.

[0114] Of course, it will also obtain the distance test results of the target vehicle at different target positions (such as point 1, point 2, point 3, point 4, point 5) relative to the corner points of the target vehicle (such as corner point A (corner_a), corner point B (corner_b), corner point C (corner_c), corner point D (corner_d)) in the y-axis direction.

[0115] In this application, the method of implementation also determines whether the parking environment of the target vehicle complies with the specifications based on the Building Information Model (BMI model).

[0116] For example, BIM models can be used to automatically check whether the design of parking spaces, ramps, and driveways in a garage meets the standards, thereby improving the speed and efficiency of the inspection.

[0117] The vehicle parking test method provided in this application tests the parking display image during the vehicle parking process, the parking planning during the vehicle parking process, and the distance perception during the vehicle parking process, making the vehicle parking test more comprehensive.

[0118] The implementation method of this application is as follows: Figure 9 As shown, a method for managing abnormal vehicle parking is also provided, including:

[0119] S10, confirm the parking perception test results.

[0120] The parking perception test results include the first parking perception test results and the second parking perception test results.

[0121] Of course, the parking perception test results can also include the results of the third parking perception test.

[0122] The results of the first parking perception test, the second parking perception test, and the third parking perception test were obtained based on the aforementioned vehicle parking test method.

[0123] S20 adjusts the parking strategy for the target vehicle based on the parking perception test results.

[0124] For example, based on the parking test results, the perception algorithm, image generation technology, and assisted parking technology of the target vehicle can be adjusted to overcome the abnormal problems in the perception test results, so that the vehicle can achieve intelligent parking more accurately and better.

[0125] The vehicle parking test method and vehicle parking anomaly management method provided in this application can be applied to computer equipment, specifically computers, cloud computers, remote servers, and other such devices.

[0126] Taking a computer as an example, the computer is equipped with a parking scene perception result visualization problem discovery tool and a parking scene real-time perception problem recording and archiving tool. By acquiring the vehicle's perception information, the parking scene perception result visualization problem discovery tool generates a visualization interface based on the perception information, allowing users to identify abnormal objects based on the visualization interface. Clicking on the location of the abnormal object in the visualization interface activates the parking scene real-time perception problem recording and archiving tool, which displays, for example... Figure 3The interface for recording abnormal information allows users to input abnormal information about objects. Upon receiving a "save data" operation from the user, an abnormal object file is generated. The real-time parking scenario perception problem recording and archiving tool also generates an abnormal detection video based on the abnormal object file, thus obtaining the first parking perception test result for the target vehicle from the abnormal detection video. Further, the computer merges the panoramic image sub-interface and the electronic map sub-interface to obtain and display an auxiliary parking image. This image is then connected to the target vehicle, and the computer controls the target vehicle to park in the target parking space based on the auxiliary parking image. Real-time parking result information is obtained, and a second parking perception test result for the target vehicle is obtained based on this parking result information. Finally, the user remotely adjusts the parking strategy of the target vehicle based on the first and second parking perception test results obtained by the computer.

[0127] Please see Figure 10 , Figure 10 The diagram shown is a schematic representation of the structure of a computer device provided in an embodiment of this application. Figure 10 As shown, the computer device may include: transceiver 121, processor 122, and memory 123.

[0128] Processor 122 executes computer execution instructions stored in memory, causing it to perform the technical solutions of the vehicle parking test method or vehicle parking anomaly management method described in the above embodiments. Processor 122 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital data processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0129] The memory 123 is connected to the processor 122 via the system bus and completes communication between them. The memory 123 is used to store computer program instructions.

[0130] For example, and not as a limitation, memory 123 may include a hard disk drive (HDD), a floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 123 may include removable or non-removable (or fixed) media. Where appropriate, memory 123 may be internal or external to the integrated gateway device. In a particular embodiment, memory 123 is non-volatile solid-state memory. In a particular embodiment, memory 123 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable read-only ROM (PROM), an erasable programmable read-only ROM (EPROM), an electrically erasable programmable read-only ROM (EEPROM), an electrically alterable read-only ROM (EAROM), or flash memory, or a combination of two or more of these. Transceiver 121 can be used to obtain the task to be run and its configuration information.

[0131] The system bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0132] This application also provides a chip for executing instructions, which is used to execute the technical solutions of the vehicle parking test method, vehicle parking management method, or vehicle parking anomaly management method in the above embodiments.

[0133] This application also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on the processor of a computer device, the processor of the computer device executes the technical solution of the vehicle parking test method or vehicle parking management method described in the above embodiments.

[0134] In some possible implementations, various aspects of the methods provided in this application can also be implemented as a program product, which includes program code. When the program product is run on the processor of a computer device, the program code is used to cause the processor of the computer device to perform the steps of the methods in the various exemplary implementations of this application described above. For example, the computer device can execute the vehicle parking test method or the vehicle parking management method described in the embodiments of this application.

[0135] The program product may take the form of any combination of one or more readable media. A readable medium may be a readable data medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CDROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0136] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solutions of the vehicle parking test method or the vehicle parking management method in the above embodiments.

[0137] It should be noted that, in addition to the specific embodiments described above, those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Although the description of this application is presented in conjunction with preferred embodiments, this does not mean that the features of this invention are limited to this implementation. On the contrary, the purpose of describing the invention in conjunction with the implementation is to cover other options or modifications that may be derived from this application. To provide a thorough understanding of this application, many specific details are included in the above description, and this application may also be implemented without using these details. Furthermore, to avoid confusion or obscuring the focus of this application, some specific details will be omitted in the description. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0138] It should be noted that in this specification, similar reference numerals and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0139] It should be noted that the terms "first" and "second" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0140] It should be noted that some structural or methodological features may be shown in the accompanying drawings in a specific arrangement and / or order. However, it should be understood that such a specific arrangement and / or order may not be necessary. Rather, in some embodiments, these features may be arranged in a manner and / or order different from that shown in the illustrative drawings. Furthermore, including structural or methodological features in a particular figure does not imply that such features are required in all embodiments, and in some embodiments, these features may be omitted or may be combined with other features.

[0141] Although this application has been illustrated and described with reference to certain preferred embodiments, those skilled in the art should understand that the above description is a further detailed explanation of the application in conjunction with specific implementations, and should not be construed as limiting the specific implementation of the application to these descriptions. Those skilled in the art can make various changes in form and detail, including some simple deductions or substitutions, without departing from the spirit and scope of this application.

Claims

1. A vehicle parking test method, characterized in that, The method includes: Acquire perception information of the target vehicle during the parking and driving process; A visualization interface is generated and displayed based on the perceived information. The visualization interface includes a panoramic image sub-interface and an electronic map sub-interface. The perceived information includes vehicle surrounding environment information and vehicle location information. The panoramic image sub-interface is a sub-interface generated by marking the panoramic image obtained based on the vehicle surrounding environment information. The panoramic image sub-interface displays the panoramic image of the target vehicle. The electronic map sub-interface is a sub-interface within a preset area generated by filling and mapping the target vehicle and the panoramic image based on the vehicle location information and the panoramic image. The electronic map sub-interface displays the mapping information of the panoramic image of the target vehicle. When a user determines that there is an abnormal object with a display error based on the visualization interface, the user clicks on the corresponding area of ​​the visualization interface to trigger an operation, jumps to the display interface, and automatically fills in the time of displaying the corresponding panoramic image and the mapping information of the panoramic image in the visualization interface, generates a test time, and determines the abnormal object corresponding to the area clicked by the user and automatically fills in the abnormal object. The system receives abnormal point-marking operations performed by the user on the point-marking interface. The abnormal point-marking operations include the user's modification operation on the abnormal object when the abnormal object is filled incorrectly on the point-marking interface, the user's operation to obtain the test package and test log path corresponding to the abnormal object, the operation to select the abnormal type and video length corresponding to the abnormal object, and the operation to fill in the problem description of the abnormal object. Based on the anomaly tracking operation, the anomaly object and the anomaly information corresponding to the anomaly object are determined. The anomaly information corresponding to the anomaly object includes the test time, the anomaly object, the anomaly type, the problem description, the video length, the test package, and the test log path. Based on the exception information corresponding to the exception object, generate an exception object file including the exception information corresponding to the exception object; An anomaly detection video is generated based on the anomaly information corresponding to the anomaly object included in the anomaly object file, so as to obtain the first parking perception test result of the target vehicle based on the anomaly detection video.

2. The vehicle parking test method according to claim 1, characterized in that, The first parking perception test result of the target vehicle is obtained based on the anomaly detection video, including: The anomaly detection video is sent to the corresponding user, enabling the user to perform anomaly detection analysis based on the video and obtain the first parking perception test result of the target vehicle; or Anomaly detection analysis is performed directly based on the anomaly detection video to obtain the first parking perception test result of the target vehicle.

3. The vehicle parking test method according to claim 2, characterized in that, The exception object file is a comma-separated value file.

4. The vehicle parking test method according to claim 3, characterized in that, The method further includes: The panoramic image sub-interface and the electronic map sub-interface are fused to obtain an auxiliary parking image, which is then displayed to control the target vehicle to park in the target parking space based on the auxiliary parking image. Determine the parking result information of the target vehicle being parked in the target parking space; Based on the parking result information, the second parking perception test result of the target vehicle is obtained.

5. A method for managing abnormal vehicle parking, characterized in that, The method includes: Determine the parking perception test results, which include a first parking perception test result and a second parking perception test result, wherein the first parking perception test result and the second parking perception test result are obtained based on the vehicle parking test method described in claim 4; Based on the parking perception test results, adjust the parking strategy of the target vehicle.

6. A computer device, characterized in that, The computer device includes: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory to enable the computer device to implement the vehicle parking test method according to any one of claims 1-4, or to enable the computer device to implement the vehicle parking anomaly management method according to claim 5.

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