Vehicle testing method, device, computer equipment, readable storage medium and program product

Abstract

The application relates to a vehicle testing method, device, computer equipment, computer readable storage medium and computer program product. The method comprises the following steps: acquiring current weather information, current road information, a current category number of traffic signs and current dynamic information of all traffic participants in a current frame of an automatic driving scene; acquiring a first evaluation index, a second evaluation index, a third evaluation index and a fourth evaluation index of the automatic driving scene based on the current weather information, the current road information, the current category number and the current dynamic information respectively; acquiring a target evaluation index of the automatic driving scene based on the above evaluation indexes and corresponding weight values of each evaluation index; and testing a target vehicle in the automatic driving scene based on the target evaluation index. The method provided by the application can test the vehicle more accurately.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a vehicle testing method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] Testing based on autonomous driving scenarios is a primary method for evaluating the safety and intelligence level of autonomous driving systems. The quality of these scenarios directly impacts the effectiveness and efficiency of autonomous driving testing. Scenarios of varying complexity can effectively assess the intelligence level of intelligent driving systems. Currently, there are numerous evaluation metrics for simulation test scenarios, and scenario complexity, as an important indicator, lacks a unified calculation standard. Existing scenario complexity calculation schemes do not adequately consider scenario elements and fail to correlate different scenario elements in their calculations. Summary of the Invention

[0003] Therefore, it is necessary to provide a vehicle testing method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can perform correlation calculations on all scene elements to address the aforementioned technical problems.

[0004] In a first aspect, this application provides a vehicle testing method, the method comprising:

[0005] Obtain current weather information, current road information, current number of traffic signs of the current category, and current dynamic information of all traffic participants in the current frame for the autonomous driving scenario;

[0006] Based on the current weather information, the current road information, the current category quantity, and the current dynamic information, the first evaluation index, the second evaluation index, the third evaluation index, and the fourth evaluation index of the autonomous driving scenario are obtained respectively.

[0007] Based on the first evaluation index, the second evaluation index, the third evaluation index, the fourth evaluation index and the corresponding weight value of each evaluation index, the target evaluation index of the autonomous driving scenario is obtained. The target evaluation index is used to evaluate the test performance of the autonomous driving scenario for testing the vehicle.

[0008] Based on the target evaluation metrics, the target vehicle is tested in the autonomous driving scenario.

[0009] In one embodiment, the current weather information includes current light intensity, current visibility, current temperature, current humidity, current rainfall, and current snowfall; the step of obtaining a first evaluation index for the autonomous driving scenario based on the current weather information includes:

[0010] Obtain the optimal light intensity, preset maximum visibility, optimal temperature, optimal humidity, preset maximum snowfall, and preset maximum rainfall;

[0011] Obtain the first difference between the current light intensity and the optimal light intensity, the second difference between the preset maximum visibility and the current visibility, the third difference between the optimal temperature and the current temperature, and the fourth difference between the optimal humidity and the current humidity, and obtain the larger value between the second difference and 0;

[0012] The first evaluation index for the autonomous driving scenario is obtained based on the ratio between the first difference and the optimal light intensity, the ratio between the larger value and the preset maximum visibility, the ratio between the third difference and the optimal temperature, the ratio between the fourth difference and the optimal humidity, the ratio between the current rainfall and the preset maximum rainfall, and the ratio between the current snowfall and the preset maximum snowfall.

[0013] In one embodiment, the current road information includes the current road type, the current number of lanes, the current road slope, and the current road curvature; the step of obtaining a second evaluation index for the autonomous driving scenario based on the current road information includes:

[0014] Obtain the corresponding identifier, preset maximum number of lanes, preset maximum road gradient, and preset maximum road curvature for each road type;

[0015] Based on the identifier corresponding to the current road type, the maximum value between the current number of lanes and the preset maximum number of lanes, the minimum value between the current road slope and the preset maximum road slope, and the minimum value between the current road curvature and the preset maximum road curvature, a second evaluation index for the autonomous driving scenario is obtained.

[0016] In one embodiment, obtaining the third evaluation metric for the autonomous driving scenario based on the current category quantity includes:

[0017] Obtain the total number of preset categories of the traffic signs;

[0018] A third evaluation index for the autonomous driving scenario is obtained based on the ratio between the current number of categories and the total number of preset categories.

[0019] In one embodiment, the current dynamic information includes current position, current speed, current orientation, contour information, and type; the step of obtaining a fourth evaluation index for the autonomous driving scenario based on the current dynamic information includes:

[0020] Obtain the remaining traffic participants other than the target vehicle from all traffic participants, and identify the traffic participants among the remaining traffic participants who have a reachable path to the target vehicle as the target traffic participants;

[0021] For each target traffic participant, based on the current location, current speed, current orientation, contour information, and type, the current time distance, current occlusion relationship, current shortest distance, and current orientation relationship between the target vehicle and the target traffic participant are obtained;

[0022] Based on the current time distance, the current occlusion relationship, the current shortest distance, the current orientation relationship, and the type of the target traffic participant, a fourth evaluation index for the autonomous driving scenario is obtained.

[0023] In one embodiment, the step of obtaining the current time distance, current occlusion relationship, current shortest distance, and current orientation relationship between the target vehicle and the target traffic participant for each target traffic participant, based on the current location, current speed, current orientation, contour information, and type, includes:

[0024] The speed difference between the current speed of the target vehicle and the current speed of the target traffic participant is obtained, and the ratio between the speed difference and the total mileage of the minimum reachable path is determined as the current time distance;

[0025] Based on the current location, current orientation, outline information and type of the target vehicle, and the current location, current orientation, outline information and type of the target traffic participant, the current occlusion relationship between the target vehicle and the target traffic participant is obtained;

[0026] Based on the current location of the target vehicle and the current location of the target traffic participant, obtain the current shortest distance between the target vehicle and the target traffic participant;

[0027] Based on the current orientation and position of the target vehicle and the current position of the target traffic participant, the current positional relationship between the target vehicle and the target traffic participant is obtained.

[0028] Secondly, this application also provides a vehicle testing apparatus, the apparatus comprising:

[0029] The first acquisition module is used to acquire the current weather information, current road information, current number of traffic signs of the current category, and current dynamic information of all traffic participants in the current frame of the autonomous driving scenario;

[0030] The second acquisition module is used to acquire the first evaluation index, the second evaluation index, the third evaluation index and the fourth evaluation index of the autonomous driving scenario based on the current weather information, the current road information, the current category quantity and the current dynamic information, respectively.

[0031] The third acquisition module is used to acquire the target evaluation index of the autonomous driving scenario based on the first evaluation index, the second evaluation index, the third evaluation index, the fourth evaluation index and the corresponding weight value of each evaluation index. The target evaluation index is used to evaluate the test performance of the autonomous driving scenario for testing the vehicle.

[0032] The testing module is used to test the target vehicle in the autonomous driving scenario based on the target evaluation index.

[0033] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the methods in any of the above embodiments.

[0034] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the methods in any of the above embodiments.

[0035] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the methods in any of the above embodiments.

[0036] The aforementioned vehicle testing method, apparatus, computer equipment, computer-readable storage medium, and computer program product acquire current weather information, current road information, current number of traffic sign categories, and current dynamic information of all traffic participants in the current frame of an autonomous driving scenario. Based on the current weather information, current road information, current number of traffic signs, and current dynamic information, they acquire a first evaluation index, a second evaluation index, a third evaluation index, and a fourth evaluation index for the autonomous driving scenario. Based on the first evaluation index, the second evaluation index, the third evaluation index, the fourth evaluation index, and the corresponding weight value of each evaluation index, they acquire a target evaluation index for the autonomous driving scenario. The target evaluation index is used to evaluate the test performance of the vehicle in the autonomous driving scenario. Based on the target evaluation index, the target vehicle is tested in the autonomous driving scenario. The method provided in this application considers all possible scenario elements in the autonomous driving scenario and performs correlation calculations on these scenario elements to obtain the target evaluation index. The target evaluation index obtained in this way can more accurately evaluate the performance of the autonomous driving scenario, thereby enabling more precise vehicle testing. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart illustrating a vehicle testing method in one embodiment;

[0039] Figure 2 This is a flowchart illustrating a method for obtaining the first evaluation index in one embodiment;

[0040] Figure 3 A flowchart of a vehicle testing method in another embodiment;

[0041] Figure 4 This is a structural block diagram of a vehicle testing device in one embodiment;

[0042] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] In one embodiment, such as Figure 1 As shown, a vehicle testing method is provided. This embodiment illustrates the method applied to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0045] S102. Obtain the current weather information, current road information, current number of traffic signs of the current category, and current dynamic information of all traffic participants in the current frame for the autonomous driving scenario.

[0046] First, the raw data of the autonomous driving scenario is acquired. Then, the current weather data, current road information, current number of traffic signs of the current category, and current dynamic information of all traffic participants in the raw data are sorted in ascending order according to the timestamp. After the sorted data are matched with the fields in the simulation scenario proto, they are written frame by frame into different channels (storage buffers, responsible for storing events) for saving.

[0047] S104. Based on the current weather information, current road information, current category quantity, and current dynamic information, obtain the first evaluation index, second evaluation index, third evaluation index, and fourth evaluation index for the autonomous driving scenario.

[0048] Each evaluation metric is used to assess the impact of corresponding scenario elements in autonomous driving scenarios on vehicle testing.

[0049] S106. Based on the first evaluation index, the second evaluation index, the third evaluation index, the fourth evaluation index and the corresponding weight value of each evaluation index, obtain the target evaluation index for the autonomous driving scenario. The target evaluation index is used to evaluate the test performance of the vehicle in the autonomous driving scenario.

[0050] The target evaluation indicators can be determined by the following formula:

[0051]

[0052] In the formula, C is the target evaluation index. As the primary evaluation indicator, The corresponding weight value for the first evaluation indicator. As the second evaluation indicator, The corresponding weight value for the second evaluation indicator. As the third evaluation indicator, The corresponding weight value for the third evaluation indicator. As the fourth evaluation indicator, This represents the corresponding weight value for the fourth evaluation indicator.

[0053] When the autonomous driving scenario is an urban scenario, in order to increase the impact of obstacle vehicles and intersections on the target evaluation index, K2 and K4 are generally set to 0.35, k1 to 0.1, and k3 to 0.2. When the autonomous driving scenario is another scenario, the weight values ​​can also be other values. This application embodiment does not make specific limitations on this.

[0054] S108. Based on the target evaluation indicators, test the target vehicle in the autonomous driving scenario.

[0055] If the target evaluation index is greater than the preset index, it indicates that the overall performance of the current autonomous driving scenario is good and it can be used as a testing scenario for the target vehicle. If the target evaluation index is less than the preset index, it indicates that the overall performance of the current autonomous driving scenario is poor, and the results of vehicle testing in this scenario will not be accurate. Therefore, this scenario will not be used as a testing scenario for the target vehicle.

[0056] Scenes can be labeled using dimensions such as mean, maximum, median, and longest subsequence. These metrics can also be used to quickly filter and organize scenes in the scene library, improving scene maintenance efficiency.

[0057] In the aforementioned vehicle testing method, the current weather information, current road information, current number of traffic sign categories, and current dynamic information of all traffic participants in the current frame of the autonomous driving scenario are obtained. Based on the current weather information, current road information, current number of traffic signs, and current dynamic information, a first evaluation index, a second evaluation index, a third evaluation index, and a fourth evaluation index for the autonomous driving scenario are obtained. Based on the first evaluation index, the second evaluation index, the third evaluation index, the fourth evaluation index, and the corresponding weight value of each evaluation index, a target evaluation index for the autonomous driving scenario is obtained. The target evaluation index is used to evaluate the test performance of the vehicle in the autonomous driving scenario. Based on the target evaluation index, the target vehicle is tested in the autonomous driving scenario. The method provided in this application considers all possible scenario elements in the autonomous driving scenario and performs correlation calculations on these scenario elements to obtain the target evaluation index. The target evaluation index obtained in this way can more accurately evaluate the performance of the autonomous driving scenario, thereby enabling more precise testing of the vehicle.

[0058] In some embodiments, such as Figure 2 As shown, the current weather information includes current light intensity, current visibility, current temperature, current humidity, current rainfall, and current snowfall. Based on the current weather information, the first evaluation metric for the autonomous driving scenario is obtained, including:

[0059] S202, obtain the optimal light intensity, preset maximum visibility, optimal temperature, optimal humidity, preset maximum snowfall, and preset maximum rainfall.

[0060] S204. Obtain the first difference between the current light intensity and the optimal light intensity, the second difference between the preset maximum visibility and the current visibility, the third difference between the optimal temperature and the current temperature, and the fourth difference between the optimal humidity and the current humidity, and obtain the larger value between the second difference and 0.

[0061] S206. Based on the ratio between the first difference and the optimal light intensity, the ratio between the larger value and the preset maximum visibility, the ratio between the third difference and the optimal temperature, the ratio between the fourth difference and the optimal humidity, the ratio between the current rainfall and the preset maximum rainfall, and the ratio between the current snowfall and the preset maximum snowfall, obtain the first evaluation index for the autonomous driving scenario.

[0062] The process for determining the first evaluation index is shown in the following formula:

[0063]

[0064] In the formula, L i Given the current light intensity, L s For the optimal light intensity, V max To preset the maximum visibility, V i For current visibility, T s For the optimal temperature, T i H is the current temperature. s For optimal humidity, H i R represents the current humidity. i R represents the current rainfall. max To preset the maximum rainfall, S i S represents the current snowfall amount. max This is the preset maximum snowfall.

[0065] In this embodiment, a first evaluation index is determined based on weather information from multiple dimensions, which makes the subsequent evaluation of the performance of autonomous driving scenarios based on the first evaluation index more accurate, thereby making the testing process of the target vehicle more accurate.

[0066] In some embodiments, the current road information includes the current road type, the current number of lanes, the current road slope, and the current road curvature; based on the current road information, a second evaluation index for the autonomous driving scenario is obtained, including: obtaining the identifier corresponding to each road type, the preset maximum number of lanes, the preset maximum road slope, and the preset maximum road curvature; based on the identifier corresponding to the current road type, the maximum value between the current number of lanes and the preset maximum number of lanes, the minimum value between the current road slope and the preset maximum road slope, and the minimum value between the current road curvature and the preset maximum road curvature, the second evaluation index for the autonomous driving scenario is obtained.

[0067] The road types include straight roads, ramps, bridges, tunnels, and intersections. Straight roads are identified by the identifier 0, while the other road types are identified by the identifier 1.

[0068] The process for determining the second evaluation indicator is shown in the following formula:

[0069]

[0070] In the formula, R is the identifier for the current road type. c R represents the current number of lanes. max To preset the maximum number of lanes, for example, R max R is 5. slope Given the current road gradient, To preset the maximum road gradient, for urban roads and highways, the road gradient is generally between 7-12%. Take 10%, R rate Given the current road curvature, To preset the maximum road curvature, for urban roads and highways, the road curvature is generally between 0.002 and 0.01. Here, we will... Take 0.01.

[0071] In this embodiment, a second evaluation index is determined based on multiple road information, which makes the subsequent evaluation of the performance of the autonomous driving scenario based on the second evaluation index more accurate, thereby making the testing process of the target vehicle more accurate.

[0072] In some embodiments, a third evaluation index for the autonomous driving scenario is obtained based on the current number of categories, including: obtaining the total number of preset categories of traffic signs; and obtaining the third evaluation index for the autonomous driving scenario based on the ratio between the current number of categories and the total number of preset categories.

[0073] The categories of traffic signs can include regulatory signs, warning signs, directional signs, tourist area signs, work area signs, auxiliary signs, and special signs, with a maximum of 7 preset categories.

[0074] The process for determining the third evaluation indicator is shown in the following formula:

[0075]

[0076] In the formula, S c S represents the current number of categories. max This is the preset total number of categories.

[0077] In this embodiment, a third evaluation index is determined based on the current number of categories and the preset total number of categories, so that the subsequent evaluation of the performance of autonomous driving scenarios based on the third evaluation index is more accurate, thereby making the testing process of the target vehicle more accurate.

[0078] In some embodiments, the current dynamic information includes current location, current speed, current orientation, contour information, and type; based on the current dynamic information, a fourth evaluation metric for the autonomous driving scenario is obtained, including: obtaining the remaining traffic participants among all traffic participants excluding the target vehicle, and identifying the traffic participants among the remaining traffic participants with a reachable path to the target vehicle as target traffic participants; for each target traffic participant, based on the current location, current speed, current orientation, contour information, and type, obtaining the current time distance, current occlusion relationship, current shortest distance, and current orientation relationship between the target vehicle and the target traffic participant; and based on the current time distance, current occlusion relationship, current shortest distance, current orientation relationship, and type of the target traffic participant, obtaining the fourth evaluation metric for the autonomous driving scenario.

[0079] The current location relationship can include the target traffic participant being directly in front of, to the side front, to the side rear, and directly behind the target vehicle; the type of the target traffic participant can be a pedestrian, bicycle, car, etc.

[0080] In this embodiment, a fourth evaluation index is determined based on multiple dynamic information, which makes the subsequent evaluation of the performance of autonomous driving scenarios based on the fourth evaluation index more accurate, thereby making the testing process of the target vehicle more accurate.

[0081] In some embodiments, for each target traffic participant, based on the current location, current speed, current orientation, contour information, and type, the current time distance, current occlusion relationship, current shortest distance, and current orientation relationship between the target vehicle and the target traffic participant are obtained, including: obtaining the speed difference between the current speed of the target vehicle and the current speed of the target traffic participant, and determining the current time distance as the ratio between the speed difference and the total mileage of the minimum reachable path; obtaining the current occlusion relationship between the target vehicle and the target traffic participant based on the current location, current orientation, contour information, and type of the target vehicle, and the current location, current orientation, contour information, and type of the target traffic participant; obtaining the current shortest distance between the target vehicle and the target traffic participant based on the current location of the target vehicle and the current location of the target traffic participant; and obtaining the current orientation relationship between the target vehicle and the target traffic participant based on the current orientation and current location of the target vehicle, and the current location of the target traffic participant.

[0082] The process for determining the fourth evaluation indicator is shown in the following formula:

[0083]

[0084] In the formula, V car V represents the current speed of the target vehicle. obLet r_dis be the current speed of the target traffic participant, r_dis be the total mileage of the minimum reachable path, and Area be the distance between the points. s Let Area be the volume of the portion of the target traffic participant that is obscured by other traffic participants, with the target vehicle as the origin. all Let F be the total volume of the target traffic participants, dis be the current shortest distance between the target vehicle and the target traffic participants, and F be the total volume of the target traffic participants. i For the current orientation, Ob type The category of the target traffic participants.

[0085] In this embodiment, by determining the current time distance, current occlusion relationship, current shortest distance, and current orientation relationship, the determined fourth evaluation index is made more accurate.

[0086] In one embodiment, another vehicle testing method is provided, the flowchart of which is as follows: Figure 3 As shown, the method includes the following:

[0087] S1: Data input, converting CSV and Rosbag data to the simulation scenario format;

[0088] S2: Calculation of simulation scenario complexity;

[0089] S2.1: Calculate weather complexity;

[0090] S2.2: Calculate road complexity;

[0091] S2.3: Calculate the complexity of transportation facilities;

[0092] S2.4: Calculate the complexity of traffic participants;

[0093] S2.5: Aggregate computation of single complexity;

[0094] S3: Associate the scenes in the scene library with the computational complexity.

[0095] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0096] Based on the same inventive concept, this application also provides a vehicle testing apparatus for implementing the vehicle testing method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more vehicle testing apparatus embodiments provided below can be found in the limitations of the vehicle testing method described above, and will not be repeated here.

[0097] In one exemplary embodiment, such as Figure 4 As shown, a vehicle testing device 400 is provided, including: a first acquisition module 401, a second acquisition module 402, a third acquisition module 403, and a testing module 404, wherein:

[0098] The first acquisition module 401 is used to acquire the current weather information, current road information, current number of traffic signs of the current category, and current dynamic information of all traffic participants in the current frame of the autonomous driving scenario.

[0099] The second acquisition module 402 is used to acquire the first evaluation index, the second evaluation index, the third evaluation index and the fourth evaluation index of the autonomous driving scenario based on the current weather information, the current road information, the current category quantity and the current dynamic information, respectively.

[0100] The third acquisition module 403 is used to acquire a target evaluation index for the autonomous driving scenario based on the first evaluation index, the second evaluation index, the third evaluation index, the fourth evaluation index, and the corresponding weight value of each evaluation index. The target evaluation index is used to evaluate the test performance of the autonomous driving scenario for testing the vehicle.

[0101] The test module 404 is used to test the target vehicle in the autonomous driving scenario based on the target evaluation index.

[0102] In some embodiments, the current weather information includes current light intensity, current visibility, current temperature, current humidity, current rainfall, and current snowfall; the second acquisition module 402 is further configured to acquire the optimal light intensity, preset maximum visibility, optimal temperature, optimal humidity, preset maximum snowfall, and preset maximum rainfall; acquire a first difference between the current light intensity and the optimal light intensity, a second difference between the preset maximum visibility and the current visibility, a third difference between the optimal temperature and the current temperature, and a fourth difference between the optimal humidity and the current humidity, and acquire the larger value between the second difference and 0; based on the ratio of the first difference to the optimal light intensity, the ratio of the larger value to the preset maximum visibility, the ratio of the third difference to the optimal temperature, the ratio of the fourth difference to the optimal humidity, the ratio of the current rainfall to the preset maximum rainfall, and the ratio of the current snowfall to the preset maximum snowfall, acquire a first evaluation index for the autonomous driving scenario.

[0103] In some embodiments, the current road information includes the current road type, the current number of lanes, the current road slope, and the current road curvature; the second acquisition module 402 is further configured to acquire the identifier corresponding to each road type, the preset maximum number of lanes, the preset maximum road slope, and the preset maximum road curvature; and based on the identifier corresponding to the current road type, the maximum value between the current number of lanes and the preset maximum number of lanes, the minimum value between the current road slope and the preset maximum road slope, and the minimum value between the current road curvature and the preset maximum road curvature, to acquire the second evaluation index of the autonomous driving scenario.

[0104] In some embodiments, the second acquisition module 402 is further configured to acquire the total number of preset categories of traffic signs; and based on the ratio between the current number of categories and the total number of preset categories, acquire a third evaluation index for the autonomous driving scenario.

[0105] In some embodiments, the current dynamic information includes current position, current speed, current orientation, contour information, and type; the second acquisition module 402 includes:

[0106] The first acquisition unit is used to acquire the remaining traffic participants other than the target vehicle among all traffic participants, and to identify the traffic participants among the remaining traffic participants who have a reachable path to the target vehicle as the target traffic participants.

[0107] The second acquisition unit is used to acquire, for each target traffic participant, the current time distance, current occlusion relationship, current shortest distance and current orientation relationship between the target vehicle and the target traffic participant based on the current position, the current speed, the current orientation, the contour information and the type;

[0108] The third acquisition unit is used to acquire the fourth evaluation index of the autonomous driving scenario based on the current time distance, the current occlusion relationship, the current shortest distance, the current orientation relationship, and the type of the target traffic participant.

[0109] In some embodiments, the second acquisition unit is further configured to acquire the speed difference between the current speed of the target vehicle and the current speed of the target traffic participant, and determine the ratio between the speed difference and the total mileage of the minimum reachable path as the current time distance; acquire the current occlusion relationship between the target vehicle and the target traffic participant based on the current position, current orientation, contour information and type of the target vehicle, and the current position, current orientation, contour information and type of the target traffic participant; acquire the current shortest distance between the target vehicle and the target traffic participant based on the current position of the target vehicle and the current position of the target traffic participant; and acquire the current orientation relationship between the target vehicle and the target traffic participant based on the current orientation and current position of the target vehicle, and the current position of the target traffic participant.

[0110] Each module in the aforementioned vehicle testing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0111] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a vehicle testing method.

[0112] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0113] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps: acquiring current weather information, current road information, current number of traffic sign categories, and current dynamic information of all traffic participants in an autonomous driving scenario in the current frame; acquiring a first evaluation index, a second evaluation index, a third evaluation index, and a fourth evaluation index for the autonomous driving scenario based on the current weather information, the current road information, the current number of traffic signs, and the current dynamic information; acquiring a target evaluation index for the autonomous driving scenario based on the first evaluation index, the second evaluation index, the third evaluation index, the fourth evaluation index, and the corresponding weight value of each evaluation index, wherein the target evaluation index is used to evaluate the test performance of the autonomous driving scenario for testing a vehicle; and testing a target vehicle in the autonomous driving scenario based on the target evaluation index.

[0114] In one embodiment, the process of a processor executing a computer program to obtain a first evaluation index for the autonomous driving scenario based on the current weather information includes: obtaining the optimal light intensity, preset maximum visibility, optimal temperature, optimal humidity, preset maximum snowfall, and preset maximum rainfall; obtaining a first difference between the current light intensity and the optimal light intensity, a second difference between the preset maximum visibility and the current visibility, a third difference between the optimal temperature and the current temperature, and a fourth difference between the optimal humidity and the current humidity, and obtaining the larger value between the second difference and 0; and obtaining the first evaluation index for the autonomous driving scenario based on the ratio of the first difference to the optimal light intensity, the ratio of the larger value to the preset maximum visibility, the ratio of the third difference to the optimal temperature, the ratio of the fourth difference to the optimal humidity, the ratio of the current rainfall to the preset maximum rainfall, and the ratio of the current snowfall to the preset maximum snowfall.

[0115] In one embodiment, the process of obtaining a second evaluation index for the autonomous driving scenario based on the current road information, implemented by the processor executing a computer program, includes: obtaining an identifier corresponding to each road type, a preset maximum number of lanes, a preset maximum road gradient, and a preset maximum road curvature; and obtaining the second evaluation index for the autonomous driving scenario based on the identifier corresponding to the current road type, the maximum value between the current number of lanes and the preset maximum number of lanes, the minimum value between the current road gradient and the preset maximum road gradient, and the minimum value between the current road curvature and the preset maximum road curvature.

[0116] In one embodiment, the process of a processor executing a computer program to obtain a third evaluation index for the autonomous driving scenario based on the current number of categories includes: obtaining the total number of preset categories of traffic signs; and obtaining the third evaluation index for the autonomous driving scenario based on the ratio between the current number of categories and the total number of preset categories.

[0117] In one embodiment, the process of obtaining a fourth evaluation metric for the autonomous driving scenario based on the current dynamic information, implemented by the processor executing a computer program, includes: obtaining the remaining traffic participants among all traffic participants excluding the target vehicle; identifying traffic participants among the remaining traffic participants with a reachable path to the target vehicle as target traffic participants; for each target traffic participant, obtaining the current time distance, current occlusion relationship, current shortest distance, and current orientation relationship between the target vehicle and the target traffic participant based on the current position, the current speed, the current orientation, the contour information, and the type; and obtaining the fourth evaluation metric for the autonomous driving scenario based on the current time distance, the current occlusion relationship, the current shortest distance, the current orientation relationship, and the type of the target traffic participant.

[0118] In one embodiment, when a processor executes a computer program, it implements the following for each target traffic participant: obtaining the current time distance, current occlusion relationship, current shortest distance, and current orientation relationship between the target vehicle and the target traffic participant based on the current location, current speed, current orientation, contour information, and type. This includes: obtaining the speed difference between the current speed of the target vehicle and the current speed of the target traffic participant, and determining the current time distance as the ratio between the speed difference and the total mileage of the minimum reachable path; obtaining the current occlusion relationship between the target vehicle and the target traffic participant based on the current location, current orientation, contour information, and type of the target vehicle, and the current location, current orientation, contour information, and type of the target traffic participant; obtaining the current shortest distance between the target vehicle and the target traffic participant based on the current location of the target vehicle and the current location of the target traffic participant; and obtaining the current orientation relationship between the target vehicle and the target traffic participant based on the current orientation and current location of the target vehicle, and the current location of the target traffic participant.

[0119] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0120] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0121] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0122] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0123] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0124] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A vehicle testing method, characterized in that, The method includes: Obtain current weather information, current road information, current number of traffic signs of the current category, and current dynamic information of all traffic participants in the current frame for the autonomous driving scenario; Based on the current weather information, the current road information, the current category quantity, and the current dynamic information, the first evaluation index, the second evaluation index, the third evaluation index, and the fourth evaluation index of the autonomous driving scenario are obtained respectively. The current dynamic information includes current position, current speed, current orientation, contour information, and type; the acquisition process of the fourth evaluation index includes: determining the remaining traffic participants among all traffic participants excluding the target vehicle, and identifying traffic participants among the remaining traffic participants with a reachable path to the target vehicle as target traffic participants; for any target traffic participant, acquiring the speed difference between the current speed of the target vehicle and the current speed of the target traffic participant, as well as the path length of the current shortest distance and the current minimum reachable path between the target vehicle and the target traffic participant, and acquiring the total volume of the target traffic participant; when the target vehicle and the target traffic participant intersect... When there are obstructions among traffic participants, based on the current position, current orientation, outline information, and type of the target vehicle, and the current position, current orientation, outline information, and type of the target traffic participant, the obstruction volume of the portion of the target traffic participant obstructed by the obstruction is determined; the sum of the ratio between the speed difference and the path length, the ratio between the obstruction volume and the total volume, and the reciprocal of the current shortest distance is obtained; the product of the sum, the representation value of the current orientation of the target traffic participant, and the representation value of the type of the target traffic participant is obtained, and the sum of the products of all traffic participants is determined as the fourth evaluation index; Based on the first evaluation index, the second evaluation index, the third evaluation index, the fourth evaluation index and the corresponding weight value of each evaluation index, the target evaluation index of the autonomous driving scenario is obtained. The target evaluation index is used to evaluate the test performance of the autonomous driving scenario for testing the vehicle. Based on the target evaluation metrics, the target vehicle is tested in the autonomous driving scenario.

2. The method according to claim 1, characterized in that, The current weather information includes current light intensity, current visibility, current temperature, current humidity, current rainfall, and current snowfall; the first evaluation index for the autonomous driving scenario, based on the current weather information, includes: Obtain the optimal light intensity, preset maximum visibility, optimal temperature, optimal humidity, preset maximum snowfall, and preset maximum rainfall; Obtain the first difference between the current light intensity and the optimal light intensity, the second difference between the preset maximum visibility and the current visibility, the third difference between the optimal temperature and the current temperature, and the fourth difference between the optimal humidity and the current humidity, and obtain the larger value between the second difference and 0; The first evaluation index for the autonomous driving scenario is obtained based on the ratio between the first difference and the optimal light intensity, the ratio between the larger value and the preset maximum visibility, the ratio between the third difference and the optimal temperature, the ratio between the fourth difference and the optimal humidity, the ratio between the current rainfall and the preset maximum rainfall, and the ratio between the current snowfall and the preset maximum snowfall.

3. The method according to claim 1, characterized in that, The current road information includes the current road type, current number of lanes, current road slope, and current road curvature; the second evaluation index for the autonomous driving scenario, based on the current road information, includes: Obtain the corresponding identifier, preset maximum number of lanes, preset maximum road gradient, and preset maximum road curvature for each road type; Based on the identifier corresponding to the current road type, the maximum value between the current number of lanes and the preset maximum number of lanes, the minimum value between the current road slope and the preset maximum road slope, and the minimum value between the current road curvature and the preset maximum road curvature, a second evaluation index for the autonomous driving scenario is obtained.

4. The method according to claim 1, characterized in that, The process of obtaining a third evaluation metric for the autonomous driving scenario based on the current number of categories includes: Obtain the total number of preset categories of the traffic signs; A third evaluation index for the autonomous driving scenario is obtained based on the ratio between the current number of categories and the total number of preset categories.

5. A vehicle testing device, characterized in that, The device includes: The first acquisition module is used to acquire the current weather information, current road information, current number of traffic signs of the current category, and current dynamic information of all traffic participants in the current frame of the autonomous driving scenario; The second acquisition module is used to acquire the first evaluation index, the second evaluation index, the third evaluation index and the fourth evaluation index of the autonomous driving scenario based on the current weather information, the current road information, the current category quantity and the current dynamic information, respectively. The current dynamic information includes current position, current speed, current orientation, outline information, and type; the second acquisition module is further configured to determine the remaining traffic participants among all traffic participants excluding the target vehicle, and identify traffic participants among the remaining traffic participants with a reachable path to the target vehicle as target traffic participants; for any target traffic participant, the module acquires the speed difference between the current speed of the target vehicle and the current speed of the target traffic participant, as well as the path length of the current shortest distance and the current minimum reachable path between the target vehicle and the target traffic participant, and acquires the total volume of the target traffic participant; when the target vehicle and the target traffic participant... In the case of obstructions between the target and the traffic participants, based on the current position, current orientation, outline information, and type of the target vehicle, and the current position, current orientation, outline information, and type of the target traffic participant, the obstruction volume of the portion of the target traffic participant obstructed by the obstruction is determined; the sum of the ratio between the speed difference and the path length, the ratio between the obstruction volume and the total volume, and the reciprocal of the current shortest distance is obtained; the product of the sum, the representation value of the current orientation of the target traffic participant, and the representation value of the type of the target traffic participant is obtained, and the sum of the products of all traffic participants is determined as the fourth evaluation index; The third acquisition module is used to acquire the target evaluation index of the autonomous driving scenario based on the first evaluation index, the second evaluation index, the third evaluation index, the fourth evaluation index and the corresponding weight value of each evaluation index. The target evaluation index is used to evaluate the test performance of the autonomous driving scenario for testing the vehicle. The testing module is used to test the target vehicle in the autonomous driving scenario based on the target evaluation index.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. 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 according to any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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