Intelligent network connection vehicle test evaluation method for closed test field area

Abstract

The invention provides an intelligent network connection vehicle test evaluation method of a closed test field area. The intelligent network connection vehicle test evaluation method for the closed test field area comprises the steps of test scene building, scene resource optimization configuration, test instance design, test process preparation, field entry instance test, test process detection, data validity evaluation, instance test ending, data trafficability evaluation based on manual review and test record playback. The method comprises the steps of test scene establishment, scene resource optimization configuration, test instance design, test process preparation, field entry instance test, test process detection, data validity evaluation, instance test ending, data trafficability evaluation based on manual review and test record playback. The invention provides a unified test evaluation method, and provides a comprehensive evaluation method which meets the requirements of online construction of a test scene, smooth cooperation of a test process, accurate monitoring of a test process, accurate evaluation of a test result, generation and filing of a test report and backtracking of the test result in a closed test field. Therefore, the test efficiency is improved, the test process is standardized, the test efficiency is improved, and the test conclusion accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent networked vehicle testing and evaluation, and in particular to an intelligent networked vehicle testing and evaluation method in a closed test area. Background Art

[0002] In today's automotive industry, the development of intelligent connected vehicles is changing with each passing day. With the continuous advancement of technology, performance and safety testing of intelligent connected vehicles has become crucial. It is common practice to test intelligent connected vehicles in closed test sites, but existing testing methods have many problems.

[0003] At present, the testing of intelligent networked vehicles is mainly carried out on actual roads, but there are many uncertainties and safety risks in actual road testing. For example, traffic flow is uncontrollable and the behavior of other vehicles and pedestrians is difficult to predict. During the testing process, the passability indicators of intelligent networked vehicles must meet national standards, which places extremely high demands on vehicle performance and the professionalism of testers.

[0004] In fact, there are dozens of test scenarios and more than one hundred test modes in the closed test field, which makes it difficult to manually run out effective data. In addition, during the running process, the testers lack intuitive and easy-to-understand data to guide the operation, and they are not clear when to accelerate and when to decelerate. This makes it extremely difficult to rely on manual operation to run out effective data during the actual running process of the vehicle. This not only affects the efficiency and accuracy of the test, but may also increase the risk and cost of the test. In addition, the existing test field evaluation method is insufficient in terms of the validity of the data and supporting the doubts of the car manufacturer. When the test results are announced, the car manufacturer may question the evaluation results, and the existing test methods cannot provide clear historical data to support the validity of the test evaluation. This not only affects the fairness and authority of the test, but may also lead to disputes and conflicts between the car manufacturer and the testing organization.

[0005] The existing test standards are difficult to achieve manually, the vehicle operation during the test has no basis, and the test evaluation results cannot withstand the doubts of the car manufacturer. The closed test field lacks a unified and scientific test evaluation method when conducting intelligent networked vehicle tests, resulting in insufficient reliability and comparability of the test results. Therefore, the present invention provides a test evaluation method for intelligent networked vehicles in a closed test field. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a test and evaluation method for intelligent connected vehicles in a closed test area, which solves the problem of low resource utilization in closed areas and possible increase in testing costs; manual review improves data accuracy, but the workload is large and time-consuming, making it difficult to quickly respond to large-scale testing needs, affecting overall testing efficiency.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for testing and evaluating an intelligent connected vehicle in a closed test area, comprising:

[0008] a. Build test scenarios: Create simulation scenarios based on test standards, and use modeling and virtual reality technologies to build realistic test scenarios, including various actual road conditions;

[0009] b. Optimized configuration of scene resources: Dynamically adjust the resource allocation within the site according to the test requirements, use intelligent scheduling algorithms to optimize the utilization of the site, and reduce the hardware equipment and time required for scene construction by optimizing the actual path of the vehicle;

[0010] c. Design test cases: Generate test cases based on vehicle type, scene complexity and test requirements, set specific speed, acceleration and path curve parameters, and optimize the path curve of the test vehicle based on the quadratic Bezier curve, whose mathematical expression is:

[0011] B(t)=(1-t) 2 P0+2(1-t)tP1+t 2 P2

[0012] Among them, t is a parameter, P0, P1, and P2 are control points on the path;

[0013] d. Test process preparation: Before the test, make a test appointment, schedule the test, and modify the vehicle network. The vehicle network modification includes installing and debugging sensors, positioning equipment, data acquisition equipment, on-board unit OBU, and true value auxiliary equipment to ensure the normal operation of the test equipment and real-time data collection;

[0014] e. On-site test: coordinate the on-site test of the test vehicle through the information management system, monitor the task progress and automatically assign tasks to the corresponding testers;

[0015] f. Test process detection: During the test process, the sensor collects the speed, acceleration, and steering angle data of the test vehicle in real time, and detects the validity of the data according to the preset standards;

[0016] g. Data validity evaluation: First, the collected data is preliminarily screened to remove obvious outliers and erroneous data; then the validity of the data is judged by comparing it with the preset standard data. If there is any doubt about the data, further manual review and analysis is carried out;

[0017] The collected data is initially screened to remove outliers and then smoothed using a Kalman filter. The filter equation is:

[0018]

[0019] in, is the predicted state, K k is the Kalman gain, z k is the observed data, H k is the observation matrix;

[0020] h. Ending the instance test: After receiving the instruction to end the instance test, the truth value auxiliary device guides the test vehicle to the next starting point;

[0021] i. Data passability evaluation based on manual review: During the test, a high-resolution camera is used to shoot the entire test process. During the passability evaluation, the vehicle position and speed information in the video frame are extracted and calculated through image processing algorithms to ultimately determine whether the test vehicle triggers the warning normally within the target time interval. If the warning time meets the standard, it is considered to have passed. The reviewer carefully reviews and verifies the calculation results to ensure the accuracy of the data;

[0022] j. Test record playback: Provides visual playback of test data processes and results to ensure transparency of data and operations, and facilitates analysis and reproduction of test scenarios; when there are doubts about the review results, the test process can be questioned and reproduced through data playback and video analysis to ensure data traceability and accuracy.

[0023] The present invention provides a method for testing and evaluating an intelligent networked vehicle in a closed test area.

[0024] Beneficial effects:

[0025] First of all, the present invention proposes a unified test evaluation method to meet the comprehensive evaluation requirements of online test scenario construction, smooth test process collaboration, precise monitoring of the test process, accurate evaluation of test results, test report generation and archiving, and test result backtracking in a closed test field, thereby improving test efficiency, standardizing the test process, improving test efficiency, and enhancing the accuracy of test conclusions.

[0026] Secondly, the present invention significantly improves the utilization rate of resources in the closed test site and effectively reduces the testing cost by introducing virtual reality technology and intelligent scheduling algorithms. By simulating the test scene, optimizing the vehicle path curve and dynamically adjusting the site resources, the hardware equipment and scene construction time required for the test are reduced. The test system combines high-precision sensors and positioning equipment to ensure the accuracy of data collection and improve the operability of the test process. Through the information management system, tasks can be intelligently allocated and test schedules can be automatically adjusted to ensure a smooth and efficient test process. Overall, this method greatly improves the utilization rate of test site resources and reduces the operating costs of closed sites.

[0027] Third, the present invention proposes a test evaluation method that combines test data validity detection and test pass detection to improve the accuracy and efficiency of test evaluation; and adopts a manual review method based on video frame calculation of test data, which is convenient for manual review and calculation of pass standards, reduces the occurrence of misjudgment, improves the authority of test evaluation, and provides a method for intuitive playback of test process data and test evaluation results, solving the problem of subsequent automobile companies questioning the test results and lack of data and process evidence.

[0028] Fourth, the present invention significantly reduces the workload of manual review through an innovative data processing mechanism. The Kalman filter is combined to smooth the data, effectively remove outliers, and after automatically detecting abnormal data, the video frame is used for targeted manual review to ensure the accuracy and reliability of the test data. The test system is also equipped with a data playback and questioning recurrence mechanism, which further improves the traceability and transparency of the test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] At present, the testing of intelligent connected vehicles is mainly carried out on actual roads, but there are many uncertainties and safety risks in actual road testing. For example, traffic flow is uncontrollable, the behavior of other vehicles and pedestrians is difficult to predict, etc., the safety risk of actual road testing is high, and it is difficult to reproduce specific scenarios for targeted testing. In a closed test field, the test scenarios are changeable and the passability indicators of each scenario are high. In the actual vehicle test environment, it is very difficult to manually control the speed of the vehicle to run data that meets the standards. The test data requires the test vehicle and the vehicle under test to manually control the vehicle speed. During the actual vehicle test, there is a lack of effective testing methods for the collaborative working ability of various systems of intelligent connected vehicles, and it is difficult to determine when to accelerate and when to decelerate to achieve the test passability indicators.

[0032] Moreover, closed test sites are limited by resources such as venues, testers, and test equipment. The test of intelligent connected vehicles adopts an appointment system. Automakers must test data that meets the indicators within the appointment time. This requires a smooth test process in the test site, accurate tracking of the test process, and efficient automated review of test results. If the conclusion of the test pass can be manually reviewed, misjudgment can be avoided. After the test passes, if the automaker has doubts about the test results later, an effective method is needed to reproduce the scenario of the test pass. However, the test evaluation methods of existing closed test sites are not unified, and it is difficult to meet the above requirements.

[0033] To solve these problems, Figure 1 As shown, an embodiment of the present invention provides a method for testing and evaluating an intelligent connected vehicle in a closed test area, comprising:

[0034] a. Build test scenarios: Create simulation scenarios based on test standards, and use modeling technology and virtual reality technology to build realistic test scenarios, including various actual road conditions.

[0035] The scene design in step a can dynamically simulate a variety of test scenarios by combining virtual reality technology with weather, lighting, and road environment variables. The test scenarios built include various actual road conditions such as urban roads, highways, and complex intersections. During the scene modeling process, the length, width, slope, curve radius and other parameters of the road are accurately set, as well as the location and working rules of facilities such as traffic lights, signs and markings. At the same time, according to different test requirements, the environmental factors of the scene, such as weather conditions and lighting conditions, can be flexibly adjusted.

[0036] b. Optimized configuration of scene resources: Dynamically adjust the resource allocation within the site according to the test requirements, use intelligent scheduling algorithms to optimize the utilization of the site, and reduce the hardware equipment and time required for scene construction by optimizing the actual path of the vehicle.

[0037] According to the test requirements, the system dynamically adjusts the resource allocation within the site, improves the utilization rate of site resources, and reduces unnecessary equipment waste. The adaptive adjustment of site resources can not only adjust resources according to test requirements, but also dynamically change resource allocation strategies according to real-time test conditions to improve test efficiency. Site resource allocation introduces intelligent scheduling algorithms to adjust resource configurations for different scenarios according to test requirements, improve the utilization rate of site resources, and reduce the use of hardware equipment. The test system is also equipped with multi-sensor fusion technology to improve the synchronization and consistency of data from different sensors to improve data accuracy.

[0038] c. Design test cases: Generate test cases based on vehicle type, scene complexity and test requirements, set specific speed, acceleration and path curve parameters, and optimize the path curve of the test vehicle based on the quadratic Bezier curve, whose mathematical expression is:

[0039] B(t)=(1-t) 2 P0+2(1-t)tP1+t 2 P2

[0040] Among them, t is a parameter, P0, P1, and P2 are control points on the path, and the design of the test instance in step c is based on the type and complexity of the vehicle. Combined with the requirements of different scenarios, multi-dimensional test instances are automatically generated to meet the testing requirements of different vehicles.

[0041] In this step, different test instances will be tested at different locations due to road length, equipment and other requirements. To facilitate the operation and description of test personnel, the combination of test instance and test location is called test instance. Test instance includes the attributes of test instance and test location. To guide on-site test personnel, configurations of test area, test area, safety area, special points and RSI area are added to increase the probability of valid data.

[0042] d. Test process preparation: Before the test, make a test appointment, schedule the test, and transform the vehicle network. The vehicle network transformation includes installing and debugging sensors, positioning equipment, data acquisition equipment, on-board unit OBU, and true value auxiliary equipment to ensure the normal operation of the test equipment and real-time data collection.

[0043] This step begins the testing process through the information management system, including test appointment, test scheduling, vehicle network transformation, as well as on-site testing, system effectiveness evaluation, manual review of test data, test data archiving, and a series of other processes, which will be described in detail later.

[0044] Test appointment mainly involves filling out an appointment test application, which mainly includes test items, test time, and test vehicles, providing data basis for the center's test personnel scheduling. Test scheduling mainly provides intelligent test instances, time, idle test personnel, idle background vehicles, and equipment selection based on appointment information, and generates test tasks.

[0045] e. On-site instance test: Coordinate the on-site test of the test vehicle through the information management system, monitor the task progress and automatically assign tasks to the corresponding testers.

[0046] This step is the on-site test, which communicates and shares information in real time through the information management system to clarify their respective responsibilities and tasks. The system will automatically assign tasks, track and remind the progress of tasks. For example, when the test vehicle needs maintenance, the system will promptly notify the relevant personnel and arrange the time and place. The on-site tester drives the test vehicle to the starting point of the test area and sends the platform an instruction to start the test. The true value auxiliary equipment prompts the speed, distance and other information in real time according to the standards required by the test instance in the test task, assists the tester in driving the vehicle, and after triggering the sound and light warning, brakes and stops, and sends the platform to end this round of testing instructions. Therefore, during the test, under the instruction of the true value auxiliary equipment, the tester can intuitively view the position, speed, acceleration and deceleration opinions of the test vehicle, and the data of the effectiveness warning on the terminal. Record the test data to ensure the accuracy and completeness of the data.

[0047] According to the test process, multiple rounds of testing can be carried out on intelligent connected vehicles. In the actual vehicle test, the testers work closely together to control the speed of the test vehicle to create different test conditions.

[0048] f. Test process detection: During the test process, the sensor collects the speed, acceleration, and steering angle data of the test vehicle in real time, and the system detects the validity of the data according to the preset standards. The data collection in step f can also collect environmental parameters such as weather and road conditions to provide data support for comprehensive evaluation.

[0049] Through big data analysis and artificial intelligence algorithms, these data are processed and analyzed in real time. Once an abnormal situation is detected, an alarm will be immediately issued and corresponding measures will be taken.

[0050] g. Data validity evaluation: First, the collected data is preliminarily screened to remove obvious outliers and erroneous data; then the validity of the data is judged by comparing it with the preset standard data. If there are any doubts about the data, further manual review and analysis are carried out.

[0051] This step begins with the evaluation of system effectiveness. For the effectiveness detection of test data, a multiple verification mechanism is adopted. The collected data is preliminarily screened, outliers are removed, and smoothed through the Kalman filter. The filtering equation is:

[0052]

[0053] in, is the predicted state, K k is the Kalman gain, z k is the observed data, H k is the observation matrix. The validity detection of the data in step g is combined with the Kalman filter to perform data smoothing to improve the stability and accuracy of the data.

[0054] Then, the validity of the data is determined by comparing it with the preset standard data. If there is any doubt about the data, it will be further manually reviewed and analyzed, and conclusions will be drawn and fed back to the on-site test personnel. There are two specific requirements for judgment: 1. Determine whether the vehicle is within the test range; 2. Whether the distance between the vehicle and the collision point is within the standard range, and whether the speed within the range meets the standard requirements.

[0055] Regarding requirement 1, the method for determining whether the vehicle is within the test range is as follows:

[0056] Consider the vehicle as a point p and the test section as a polygon. To determine whether a point is inside the polygon, draw a horizontal ray from the point (any ray will do, but horizontal is easier to calculate) and determine the number of intersections between the ray and the polygon. If the number of intersections is odd, the point is inside the polygon; if it is even, the point is outside the polygon.

[0057] For requirement 2, determine whether the distance between the vehicle and the collision point is within the standard range and whether the speed within the range meets the standard requirements in the following ways:

[0058] The shortest distance between two points in the scene of the test vehicle is calculated by the Haversing formula, which is:

[0059]

[0060] Among them, Δφ and Δλ are the differences in latitude and longitude of the two points, r is the radius of the earth, which is approximately 6,371 kilometers, d is the shortest distance, φ1 and φ2 are the latitudes of the two points, λ1 and λ2 are the longitudes of the two points, Δφ=φ2-φ1 is the difference in latitude, and Δλ=λ2-λ1 is the difference in longitude.

[0061] The Haversing formula can calculate the great circle distance between the latitude and longitude of two given points, that is, the shortest distance on the surface of the earth. Determine whether the shortest distance is within the standard range.

[0062] Moreover, if the speed meets the requirements within the standard range of plus or minus one, it is sufficient to directly judge whether the speed meets the standard requirements.

[0063] h. Ending the instance test: After receiving the instruction to end the instance test, the truth value auxiliary device guides the test vehicle to the next starting point.

[0064] i. Data passability evaluation based on manual review: After the current instance test is completed, according to the aforementioned on-site instance test and data validity evaluation, during the test, a high-resolution camera is used to shoot the entire test process. During the passability evaluation, the image processing algorithm is used to extract and calculate the vehicle position, speed and other information in the video frame, and finally determine whether the test vehicle triggers the warning normally within the target time interval. If the warning time meets the standard, it is considered to have passed. The reviewer will carefully review and verify the calculation results to ensure the accuracy of the data.

[0065] In this step, the tester can obtain the test process data frame by frame based on the video file of the on-site test, such as the latitude and longitude of the vehicle position at the corresponding moment of the video frame, the vehicle speed, and the warning time, so as to facilitate the review and calculation of the passability standard. This solves the traditional test data copying log data from the equipment and manual calculation method, avoids the occurrence of misjudgment, and improves the authority of the test evaluation.

[0066] In addition, different examples and different standards will result in different calculation methods. I will not go into details here. For example, the high-precision map displays the position of a single frame of the vehicle in real time, and you can intuitively see whether the warning frame is within the test range: the vehicle position is displayed in real time in the high-precision map. The processing of vehicle positioning data involves the optimization of position and posture, using SLAM and Bundle Adjustment methods and smoothing algorithms.

[0067] It should be noted that steps g and i respectively carry out test data validity evaluation and test passability evaluation. This embodiment combines the two to improve the problem of low test data quality, thereby improving test efficiency. It determines whether the vehicle is in the test area, whether the speed and TTC difference in the test area meet the standard requirements, and automatically gives the result of valid or invalid test data. Only test data that passes the validity test needs to be manually reviewed, and then the test data passability test is carried out, so as to draw test conclusions more quickly and improve the test operation efficiency of the closed test field.

[0068] j. Test record playback: Provides a visual playback function for test data process and results to ensure the transparency of data and operations, and facilitates analysis and reproduction of test scenarios. The test data visual playback function in step j can play back the test process in real time to ensure the transparency of the test process. This step provides a method for intuitive playback of test process data and test evaluation results to solve the problem of subsequent car companies questioning the test results and lacking data and process evidence.

[0069] When there are doubts about the manual review results, the test situation at that time can be replayed to display the marked areas such as the test area, test area, safety area, warning area, vehicle operation status and data, warning time, etc. This step uses data playback and video analysis to question and reproduce the test process to ensure the traceability and accuracy of the data.

[0070] k. Test data archiving: After the test is completed, the test report is automatically archived and the test results can be traced back.

[0071] In summary, the present invention designs a unified evaluation method for intelligent connected vehicle testing in a closed test field, which makes the entire test evaluation process more standardized and normalized, and reduces errors and disputes caused by inconsistent evaluation methods. According to the information management system and efficient communication and coordination mechanism, the connection between each link is closer, which reduces unnecessary waiting and repetitive work, thereby saving a lot of time and resources, and improving the test collaboration process and test efficiency.

[0072] In addition, the present invention also adopts data validity detection and passability detection, as well as a manual review method based on video frame calculation, to ensure the accuracy and reliability of the data, and provide a solid legal basis for the test evaluation results. At the same time, the intuitive playback method allows the car manufacturer to clearly understand the entire test process, reducing doubts and disputes caused by information opacity, improving the authority and credibility of the test evaluation, and providing an effective response to doubts about the test evaluation results.

[0073] The present invention realizes automatic collection, intelligent analysis and objective evaluation of test data, and ensures the convenience, accuracy and fairness of the test process.

[0074] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for testing and evaluating an intelligent connected vehicle in a closed test area, characterized in that: include: a. Build test scenarios: Create simulation scenarios based on test standards, and use modeling and virtual reality technologies to build realistic test scenarios, including various actual road conditions; b. Optimized configuration of scene resources: Dynamically adjust the resource allocation within the site according to the test requirements, use intelligent scheduling algorithms to optimize the utilization of the site, and reduce the hardware equipment and time required for scene construction by optimizing the actual path of the vehicle; c. Design test cases: Generate test cases based on vehicle type, scenario complexity and test requirements, set specific speed, acceleration and path curve parameters, and optimize the path curve of the test vehicle based on the quadratic Bezier curve; d. Test process preparation: Before the test, make a test appointment, schedule the test, and modify the vehicle network. The vehicle network modification includes installing and debugging sensors, positioning equipment, data acquisition equipment, on-board unit OBU, and true value auxiliary equipment to ensure the normal operation of the test equipment and real-time data collection; e. On-site test: coordinate the on-site test of the test vehicle through the information management system, monitor the task progress and automatically assign tasks to the corresponding testers; f. Test process detection: During the test process, the sensor collects the speed, acceleration, and steering angle data of the test vehicle in real time, and detects the validity of the data according to the preset standards; g. Data validity evaluation: First, the collected data is preliminarily screened to remove obvious outliers and erroneous data; then the validity of the data is judged by comparing it with the preset standard data. If there is any doubt about the data, further manual review and analysis is carried out; The collected data are preliminarily screened to remove outliers and smoothed using a Kalman filter; h. Ending the instance test: After receiving the instruction to end the instance test, the truth value auxiliary device guides the test vehicle to the next starting point; i. Data passability evaluation based on manual review: During the test, a high-resolution camera is used to shoot the entire test process. During the passability evaluation, the vehicle position and speed information in the video frame are extracted and calculated through image processing algorithms to ultimately determine whether the test vehicle triggers the warning normally within the target time interval. If the warning time meets the standard, it is considered to have passed. The reviewer carefully reviews and verifies the calculation results to ensure the accuracy of the data; j. Test record playback: Provides visual playback of test data process and results to ensure transparency of data and operations, and facilitates analysis and reproduction of test scenarios; When there are doubts about the review results, the test process can be questioned and reproduced through data playback and video analysis to ensure the traceability and accuracy of the data.

2. The method for testing and evaluating an intelligent connected vehicle in a closed test area according to claim 1, characterized in that: The scenario design described in step a can dynamically simulate a variety of test scenarios by combining weather, lighting, and road condition environmental variables through virtual reality technology. The site resource allocation described in step b adjusts the resource configuration of different scenarios according to test requirements by introducing an intelligent scheduling algorithm, thereby improving the utilization rate of site resources and reducing the use of hardware equipment.

3. The method for testing and evaluating an intelligent connected vehicle in a closed test area according to claim 1, characterized in that: The test instance described in step c includes the attributes of the test instance and the test location. In order to guide the on-site testers in testing, the configurations of the test area, test area, safety area, special points, and RSI area are added to increase the probability of valid data. The design of the test instance is based on the type and complexity of the vehicle, combined with the requirements of different scenarios, and multi-dimensional test instances are automatically generated; in step d, the test appointment is to fill in the appointment test application, and the items to be filled in include test items, test time, and test vehicles, providing data basis for the scheduling of central testers; test scheduling is to intelligently give the test instance, time, idle testers, idle background vehicles, and equipment selection based on the appointment information, and generate test tasks.

4. The method for testing and evaluating an intelligent connected vehicle in a closed test area according to claim 1, characterized in that: In step e, real-time communication and sharing of information are carried out through the information management system to clarify respective responsibilities and tasks. The information management system automatically assigns tasks, tracks and reminds the progress of tasks. The on-site test personnel drive the test vehicle to the starting point of the test area and issue a command to the platform to start the test. The true value auxiliary equipment prompts speed, distance and other information in real time according to the standards required by the test instance in the test task, assists the test personnel in driving the vehicle, brakes and stops the vehicle after triggering the sound and light warning, and issues a command to end this round of testing. The data collection described in step f can also collect environmental parameters such as weather and road conditions.

5. The method for testing and evaluating an intelligent connected vehicle in a closed test area according to claim 1, characterized in that: There are two requirements for determining the validity of the data in step g, namely, determining whether the vehicle is within the test interval; and whether the distance between the vehicle and the collision point is within the standard range, and whether the speed within the range meets the standard requirements; The method for determining whether the vehicle is within the test interval is as follows: the vehicle is regarded as a point p, the test interval is regarded as a polygon, and a point is determined to be within the polygon. A horizontal ray is drawn from the point, and the number of intersections between the ray and the polygon is determined. If the number of intersections is an odd number, the point is within the polygon, and if it is an even number, the point is outside the polygon. Determine whether the distance between the vehicle and the collision point is within the standard range and whether the speed within the range meets the standard requirements in the following way: Calculate the shortest distance between two points of the test vehicle in the scene using the Haversing formula, the formula is: Among them, Δφ and Δλ are the latitude and longitude differences of the two points respectively, r is the radius of the earth, d is the shortest distance, φ1 and φ2 are the latitudes of the two points; It is sufficient to determine whether the shortest distance is within the standard range and whether the speed meets the standard requirements.

6. The method for testing and evaluating an intelligent connected vehicle in a closed test area according to claim 1, characterized in that: In step i, different test instances have different passing standards and calculation methods, and the results of valid and invalid test data are automatically given. Only test data that passes the validity test needs to be manually reviewed for test data passability evaluation.

7. The method for testing and evaluating an intelligent connected vehicle in a closed test area according to claim 1, characterized in that: In step j, when there are doubts about the manual review results, the test situation at that time can be replayed. The test data visualization playback function can replay the test process in real time, showing the marked areas of the test area, test area, safety area, warning area, vehicle operation status and data, and warning time.

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