Intelligent test system based on Internet of Vehicles platform

Abstract

The invention relates to the technical field of vehicle intelligent testing, in particular to an intelligent testing system based on an Internet of Vehicles platform. The system comprises a vehicle test data acquisition module used for generating a management period and acquiring vehicle test data of a vehicle test in the management period; the vehicle driving state test module is used for analyzing and processing the vehicle driving state data to obtain a vehicle driving state analysis result; the vehicle driving track test module is used for analyzing and processing the vehicle driving track data to obtain a track similarity result; the vehicle driving control test module is used for analyzing and processing the vehicle driving control data to obtain a vehicle automatic control result; and the vehicle networking platform is used for receiving and analyzing the vehicle driving state analysis result, the track similarity result and the vehicle automatic control result, generating a vehicle test result characteristic value, and generating a vehicle test evaluation result based on the vehicle test result characteristic value. The processing efficiency of the vehicle test data can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle intelligent testing, and particularly to an intelligent testing system based on a vehicle networking platform. Background Art

[0002] With the continuous progress of technology and the promotion of the intelligent wave, vehicle networking technology, as an important part of the intelligent transportation system, has been widely applied and developed. Vehicle networking technology greatly improves the intelligent level of the transportation system by enabling intelligent communication between vehicles, between vehicles and people, and between vehicles and roads, providing strong guarantees for the safe and efficient operation of vehicles. And the intelligent testing system based on the vehicle networking platform is an indispensable part in the development of vehicle networking technology. It can comprehensively and efficiently test the performance, functions and security of the vehicle networking system to ensure the stable and reliable operation of the vehicle networking system.

[0003] In the prior art, in the face of a large amount of vehicle test data, the existing test systems often have low processing efficiency and low intelligence level, resulting in low processing efficiency of vehicle test data and inaccurate test evaluations.

[0004] Therefore, there is an urgent need for an intelligent testing system based on the vehicle networking platform to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent testing system based on the vehicle networking platform, which solves the technical problems that in the existing solutions, in the face of a large amount of vehicle test data, the existing test systems often have low processing efficiency and low intelligence level, resulting in low processing efficiency of vehicle test data and inaccurate test evaluations.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An intelligent testing system based on the vehicle networking platform, the system includes a vehicle test data acquisition module, a vehicle driving state testing module, a vehicle driving trajectory testing module, a vehicle driving control testing module, and a vehicle networking platform;

[0008] The vehicle test data acquisition module is used to generate a management cycle and acquire vehicle test data of the vehicle during the management cycle, wherein the vehicle test data includes vehicle driving state data, vehicle driving trajectory data, and vehicle driving control data;

[0009] The vehicle driving state testing module is used to analyze and process the vehicle driving state data, obtain a vehicle driving state analysis result, and send the vehicle driving state analysis result to the vehicle networking platform;

[0010] The vehicle driving trajectory test module is used to analyze and process vehicle driving trajectory data, obtain the trajectory similarity result, and send the trajectory similarity result to the vehicle networking platform;

[0011] The vehicle driving control test module is used to analyze and process vehicle driving control data, obtain the vehicle automatic control result, and send the vehicle automatic control result to the vehicle networking platform;

[0012] The vehicle networking platform is used to receive, analyze, and parse the vehicle driving state analysis result, trajectory similarity result, and vehicle automatic control result, generate the vehicle test result eigenvalue, and generate the vehicle test evaluation result based on the vehicle test result eigenvalue.

[0013] Furthermore, the vehicle driving state test module is used to analyze and process vehicle driving state data, and the process of obtaining the vehicle driving state analysis result specifically includes the following:

[0014] The vehicle driving state data includes the vehicle GPS data during the vehicle driving process. Based on the vehicle GPS data, the vehicle coordinates P of the (n + 1)-th frame are extracted n+1 X n+1 ,Y n+1 and the vehicle coordinates P of the n-th frame n X n ,Y n ;

[0015] Calculate X n+1 -X n to obtain dx, Y n+1 -Y n to obtain dy. After normalizing dx and dy, the vector

[0016] Based on the vector cross product operation, judge the left and right turns of the vehicle:

[0017]

[0018] where is the vector corresponding to the (n + 1)-th frame, is the vector corresponding to the n-th frame;

[0019] When m is less than 0, the vehicle drives to the right. When m is greater than 0, the vehicle drives to the left;

[0020] If there is at the n-th frame and 6 consecutive frames starting from the n-th frame satisfy then it is determined that the vehicle has a turning behavior;

[0021] Taking the vehicle driving to the right or left as the vehicle driving state analysis result.

[0022] Further, the vehicle driving trajectory test module is used to analyze and process vehicle driving trajectory data to obtain a trajectory similarity result, which specifically includes the following processes:

[0023] The vehicle driving trajectory data includes the vehicle driving trajectory distance within the management period. The management period is segmented according to different collection points to obtain several management time periods, and the vehicle driving trajectory distances of several management time periods are determined based on the several management time periods;

[0024] The vehicle driving trajectory distances of several management time periods are subtracted pairwise to form a distance difference matrix M;

[0025] The distance difference matrix M is compared with the standard distance difference matrix to determine whether the matrix elements in the two matrices are the same, count the number of identical elements in the matrix elements, calculate the proportion of the number of identical elements in the matrix elements to the number of matrix elements in the standard distance difference matrix, record this proportion as the trajectory similarity, and use the trajectory similarity as the trajectory similarity result. Among them, the standard distance difference matrix is a preset distance difference matrix stored in the vehicle driving trajectory test module;

[0026] Alternatively, the vehicle driving trajectory data includes the average vehicle driving speeds of several management time periods. The average vehicle driving speed of each management time period is compared with the standard average vehicle driving speed of each management time period, the number of management time periods corresponding to the same average vehicle driving speed is statistically obtained, and the proportion of the number of management time periods corresponding to the same average vehicle driving speed to all management time periods within the management period is calculated. Record this proportion as the trajectory similarity, and use the trajectory similarity as the trajectory similarity result. Among them, the standard average vehicle driving speed of each management time period is a preset average vehicle driving speed stored in the vehicle driving trajectory test module.

[0027] Further, the vehicle driving control test module is used to analyze and process vehicle driving control data to obtain a vehicle automatic control result, which specifically includes the following processes:

[0028] The vehicle driving control data includes automatic braking control data;

[0029] Based on the automatic braking control data, obtain the vehicle speed V0 when the vehicle starts emergency braking, the longitudinal slope α of the vehicle driving direction, and the vehicle driving road surface adhesion coefficient φ;

[0030] Substitute the vehicle speed V0 when the vehicle starts emergency braking, the longitudinal slope α of the vehicle driving direction, and the vehicle driving road surface adhesion coefficient φ into the correlation formula to calculate the automatic braking distance S. The correlation formula is as follows:

[0031]

[0032] Take the automatic braking distance S as the vehicle automatic control result.

[0033] Furthermore, the vehicle driving control test module is used to analyze and process the vehicle driving control data to obtain the vehicle automatic control result, which specifically includes the following process:

[0034] The vehicle driving control data includes automatic steering control data;

[0035] Obtain the automatic steering speed V of the vehicle based on the automatic steering control data z , and obtain the horizontal distance H from the wheel hub on the steering side of the vehicle to the road boundary in the steering direction;

[0036] When automatically steering, calculate the steering wheel deflection angle ω, and the steering wheel deflection angle ω is obtained from the formula:

[0037]

[0038] where E is the first constant correction coefficient and Af is the steering wheel sensitivity coefficient;

[0039] Take the steering wheel deflection angle ω as the vehicle automatic control result.

[0040] Furthermore, the vehicle driving control test module is used to analyze and process the vehicle driving control data to obtain the vehicle automatic control result, which specifically includes the following process:

[0041] The vehicle driving control data includes automatic acceleration control data;

[0042] Based on the automatic acceleration control data, obtain the longitudinal distance, the preset expected longitudinal distance, and the acceleration time t, and calculate the difference d between the longitudinal distance and the preset expected longitudinal distance;

[0043] Calculate the automatic acceleration a:

[0044]

[0045] where V m is the expected speed;

[0046] Take the automatic acceleration a as the vehicle automatic control result.

[0047] Furthermore, the vehicle networking platform is used to receive, analyze, and parse the vehicle driving state analysis result, the trajectory similarity result, and the vehicle automatic control result, and generate the vehicle test result characteristic value, which includes the following process:

[0048] Analyze the vehicle driving state analysis result, trajectory similarity result, and vehicle automatic control result: Based on the vehicle driving state analysis result, count the number of times the vehicle drives to the right or left, and find the difference from the preset number of driving times within the management cycle to obtain the number of times I when the vehicle driving state is not determined; obtain the trajectory similarity G based on the trajectory similarity result; obtain the automatic braking distance S, steering wheel deflection angle ω, and automatic acceleration a according to the vehicle automatic control result. Subtract the automatic braking distance S from the preset automatic braking distance threshold to obtain the automatic braking distance difference, subtract the steering wheel deflection angle ω from the preset steering wheel deflection angle threshold to obtain the steering wheel deflection angle difference, subtract the automatic acceleration a from the preset automatic acceleration threshold to obtain the automatic acceleration difference, and sum all the differences to obtain the difference C;

[0049] Substitute the number of times I when the vehicle driving state is not determined, the trajectory similarity G, and the difference C into the correlation formula to calculate the vehicle test result eigenvalue LMS. The correlation formula is as follows:

[0050]

[0051] Among them, Q is a constant.

[0052] Furthermore, generating a vehicle test evaluation result based on the vehicle test result eigenvalue includes: determining the evaluation grade interval corresponding to the vehicle test result eigenvalue, and using the evaluation grade interval as the evaluation result. Among them, each evaluation grade interval corresponds to a numerical interval of the vehicle test result eigenvalue.

[0053] Compared with the existing solutions, the beneficial effects achieved by the present invention:

[0054] The present invention generates a management cycle and obtains vehicle test data of vehicle tests within the management cycle. Among them, the vehicle test data includes vehicle driving state data, vehicle driving trajectory data, and vehicle driving control data; analyzes and processes the vehicle driving state data to obtain a vehicle driving state analysis result; analyzes and processes the vehicle driving trajectory data to obtain a trajectory similarity result; analyzes and processes the vehicle driving control data to obtain a vehicle automatic control result; receives the vehicle driving state analysis result, trajectory similarity result, and vehicle automatic control result and analyzes them to generate a vehicle test result eigenvalue, and generates a vehicle test evaluation result based on the vehicle test result eigenvalue, which can improve the processing efficiency of vehicle test data.

[0055] Furthermore, the present invention can comprehensively and efficiently test the vehicle operation state, the performance, functions, and safety of the vehicle networking system to ensure the stable and reliable operation of the vehicle networking system. Brief Description of the Drawings

[0056] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for use in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other accompanying drawings can also be obtained based on these drawings.

[0057] Figure 1 is the system block diagram of the intelligent test system based on the vehicle networking platform in the embodiment of the present invention;

[0058] Figure 2 is the system working flow chart of an intelligent test system based on the vehicle networking platform in the embodiment of the present invention. Detailed implementation manners

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0060] In addition, the described features, structures or characteristics can be combined in any suitable manner in one or more example embodiments. In the following description, many specific details are provided to give a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure can be practiced by omitting one or more of the specific details, or by using other methods, components, steps, etc. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0061] This embodiment provides an intelligent test system based on the vehicle networking platform, Figure 1 is the system block diagram of the intelligent test system based on the vehicle networking platform in the embodiment of the present invention. As Figure 1 shown, the system includes a vehicle test data acquisition module, a vehicle driving state test module, a vehicle driving trajectory test module, a vehicle driving control test module, and a vehicle networking platform;

[0062] The vehicle test data acquisition module is used to generate a management cycle and acquire vehicle test data during the management cycle. Among them, the vehicle test data includes vehicle driving state data, vehicle driving trajectory data, and vehicle driving control data;

[0063] The vehicle driving state test module is used to analyze and process the vehicle driving state data, obtain the vehicle driving state analysis result, and send the vehicle driving state analysis result to the vehicle networking platform;

[0064] The vehicle driving trajectory test module is used to analyze and process vehicle driving trajectory data, obtain the trajectory similarity result, and send the trajectory similarity result to the vehicle networking platform;

[0065] The vehicle driving control test module is used to analyze and process vehicle driving control data, obtain the vehicle automatic control result, and send the vehicle automatic control result to the vehicle networking platform;

[0066] The vehicle networking platform is used to receive, analyze, and generate the vehicle test result eigenvalue based on the vehicle driving state analysis result, the trajectory similarity result, and the vehicle automatic control result, and generate the vehicle test evaluation result based on the vehicle test result eigenvalue.

[0067] In summary, the present invention generates a management cycle and obtains vehicle test data of vehicle tests within the management cycle, where the vehicle test data includes vehicle driving state data, vehicle driving trajectory data, and vehicle driving control data; analyzes and processes the vehicle driving state data to obtain a vehicle driving state analysis result; analyzes and processes the vehicle driving trajectory data to obtain a trajectory similarity result; analyzes and processes the vehicle driving control data to obtain a vehicle automatic control result; receives, analyzes, and generates the vehicle test result eigenvalue based on the vehicle driving state analysis result, the trajectory similarity result, and the vehicle automatic control result, and generates the vehicle test evaluation result based on the vehicle test result eigenvalue, which can improve the processing efficiency of vehicle test data, comprehensively and efficiently test the vehicle operation state, the performance, function, and safety of the vehicle networking system, and ensure the stable and reliable operation of the vehicle networking system.

[0068] It should be noted that the vehicle test data acquisition module, the vehicle driving state test module, the vehicle driving trajectory test module, the vehicle driving control test module, and the vehicle networking platform can all achieve two-way communication to complete data transmission. The vehicle networking platform is a comprehensive interactive wireless network system that collects vehicle's own environment and status information based on information such as vehicle position, speed, and route through devices such as GPS (Global Positioning System), RFID (Radio Frequency Identification), sensors, and camera image processing. The vehicle networking platform analyzes and processes this information through the Internet and computer technology to achieve the organic interaction between automobiles, roads, and people, and realize the intelligence of vehicles and traffic.

[0069] In some embodiments, the process of the vehicle driving state test module analyzing and processing vehicle driving state data to obtain a vehicle driving state analysis result specifically includes the following:

[0070] The vehicle driving state data includes the vehicle GPS data during the vehicle driving process, and the vehicle coordinates P of the (n + 1)-th frame are extracted based on the vehicle GPS data n+1 X n+1 , Y n+1 and the vehicle coordinates P of the n-th frame n X n , Y n ;

[0071] Calculate X n+1 -X n to obtain dx, Y n+1 -Y n to obtain dy, and after normalizing dx and dy, a vector is obtained

[0072] Based on the vector cross product operation, determine the left and right turns of the vehicle:

[0073]

[0074] wherein, is the vector corresponding to the (n + 1)-th frame, is the vector corresponding to the n-th frame;

[0075] When m is less than 0, the vehicle drives to the right, and when m is greater than 0, the vehicle drives to the left;

[0076] If there is at the n-th frame and 6 consecutive frames starting from the n-th frame satisfy then it is determined that the vehicle has a turning behavior;

[0077] Taking the vehicle driving to the right or to the left as the vehicle driving state analysis result.

[0078] In some embodiments, the vehicle driving trajectory test module is used to analyze and process the vehicle driving trajectory data to obtain the trajectory similarity result, which specifically includes the following process:

[0079] The vehicle driving trajectory data includes the vehicle driving trajectory distance within the management period. The management period is segmented according to different collection points to obtain a number of management time periods, and the vehicle driving trajectory distances of the number of management time periods are determined based on the number of management time periods;

[0080] Calculate the difference between every two of the vehicle driving trajectory distances of the number of management time periods and form a distance difference matrix M;

[0081] Compare the distance difference matrix M with the standard distance difference matrix, determine whether the matrix elements in the two matrices are the same, count the number of identical elements in the matrix elements, calculate the ratio of the number of identical elements in the matrix elements to the number of matrix elements in the standard distance difference matrix, record this ratio as the trajectory similarity, and use the trajectory similarity as the trajectory similarity result. Among them, the standard distance difference matrix is a preset distance difference matrix stored in the vehicle driving trajectory test module;

[0082] Alternatively, the vehicle driving trajectory data includes the average driving speeds of the vehicle in several management periods. Compare the average driving speed of the vehicle in each management period with the standard average driving speed of the vehicle in each management period, count the number of management periods corresponding to the same average driving speed of the vehicle, and calculate the ratio of the number of management periods corresponding to the same average driving speed of the vehicle to all management periods within the management cycle. Record this ratio as the trajectory similarity, and use the trajectory similarity as the trajectory similarity result. Among them, the standard average driving speed of each management period is a preset average driving speed of the vehicle stored in the vehicle driving trajectory test module.

[0083] In some embodiments, the vehicle driving control test module is used to analyze and process vehicle driving control data to obtain the vehicle automatic control result, which specifically includes the following process:

[0084] The vehicle driving control data includes automatic braking control data;

[0085] Based on the automatic braking control data, obtain the vehicle speed V0 when the vehicle starts emergency braking, the longitudinal slope α of the vehicle driving direction, and the road surface adhesion coefficient φ of the vehicle driving;

[0086] Substitute the vehicle speed V0 when the vehicle starts emergency braking, the longitudinal slope α of the vehicle driving direction, and the road surface adhesion coefficient φ of the vehicle driving into the correlation formula to calculate the automatic braking distance S. The correlation formula is as follows:

[0087]

[0088] Use the automatic braking distance S as the vehicle automatic control result.

[0089] In some embodiments, the vehicle driving control test module is used to analyze and process vehicle driving control data to obtain the vehicle automatic control result, which specifically includes the following process:

[0090] The vehicle driving control data includes automatic steering control data;

[0091] Based on the automatic steering control data, obtain the automatic steering speed V of the vehicle z , and obtain the horizontal distance H from the wheel hub on the steering side of the vehicle to the road boundary in the steering direction;

[0092] When automatically steering, calculate the steering wheel deflection angle ω, and the steering wheel deflection angle ω is obtained from the formula:

[0093]

[0094] where E is the first constant correction coefficient and Af is the steering wheel sensitivity coefficient;

[0095] Take the steering wheel deflection angle ω as the vehicle automatic control result.

[0096] In some embodiments, the vehicle driving control test module is used to analyze and process vehicle driving control data to obtain the vehicle automatic control result, which specifically includes the following process:

[0097] The vehicle driving control data includes automatic acceleration control data;

[0098] Based on the automatic acceleration control data, obtain the longitudinal distance, the preset expected longitudinal distance, and the acceleration time t, and calculate the difference d between the longitudinal distance and the preset expected longitudinal distance;

[0099] Calculate the automatic acceleration a:

[0100]

[0101] where V m is the expected speed;

[0102] Take the automatic acceleration a as the vehicle automatic control result.

[0103] Furthermore, Figure 2 is the system working flow chart of an intelligent test system based on the vehicle networking platform according to an embodiment of the present invention. As Figure 2 shown, the vehicle networking platform is used to receive, analyze, and generate vehicle test result characteristic values for the vehicle driving state analysis result, the trajectory similarity result, and the vehicle automatic control result, including the following steps:

[0104] Step S201, analyze the vehicle driving state analysis result, trajectory similarity result, and vehicle automatic control result: Based on the vehicle driving state analysis result, count the number of times the vehicle drives to the right or left, and subtract it from the preset number of driving times within the management cycle to obtain the number of times I when the vehicle driving state is not determined; obtain the trajectory similarity G based on the trajectory similarity result; obtain the automatic braking distance S, steering wheel deflection angle ω, and automatic acceleration a according to the vehicle automatic control result. Subtract the automatic braking distance S from the preset automatic braking distance threshold to obtain the automatic braking distance difference, subtract the steering wheel deflection angle ω from the preset steering wheel deflection angle threshold to obtain the steering wheel deflection angle difference, subtract the automatic acceleration a from the preset automatic acceleration threshold to obtain the automatic acceleration difference, and sum up all the differences to obtain the difference C;

[0105] Step S202, substitute the number of times I when the vehicle driving state is not determined, the trajectory similarity G, and the difference C into the correlation formula to calculate the vehicle test result eigenvalue LMS. The correlation formula is as follows:

[0106]

[0107] where Q is a constant.

[0108] In some embodiments, generating a vehicle test evaluation result based on the vehicle test result eigenvalue includes: determining the evaluation grade interval corresponding to the vehicle test result eigenvalue, and using the evaluation grade interval as the evaluation result. Among them, each evaluation grade interval corresponds to a numerical interval of the vehicle test result eigenvalue.

[0109] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0110] Those of ordinary skill in the art will realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0111] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0112] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only for some logical function divisions, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0113] The unit described as a separation component may or may not be physically separated. The component displayed as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0114] As mentioned above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. The intelligent testing system based on the Internet of Vehicles platform is characterized by: The system includes a vehicle test data acquisition module, a vehicle driving state test module, a vehicle driving trajectory test module, a vehicle driving control test module, and a vehicle networking platform; The vehicle test data acquisition module is used to generate a management cycle and acquire vehicle test data of vehicle tests within the management cycle, wherein the vehicle test data includes vehicle driving state data, vehicle driving trajectory data, and vehicle driving control data; The vehicle driving state test module is used to analyze and process the vehicle driving state data, obtain the vehicle driving state analysis results, and send the vehicle driving state analysis results to the Internet of Vehicles platform; The vehicle driving trajectory test module is used to analyze and process the vehicle driving trajectory data, obtain the trajectory similarity result, and send the trajectory similarity result to the Internet of Vehicles platform; The vehicle driving control test module is used to analyze and process the vehicle driving control data, obtain the vehicle automatic control results, and send the vehicle automatic control results to the Internet of Vehicles platform; The Internet of Vehicles platform is used to receive and analyze vehicle driving state analysis results, trajectory similarity results and vehicle automatic control results, generate vehicle test result characteristic values, and generate vehicle test evaluation results based on the vehicle test result characteristic values.

2. The intelligent testing system based on the Internet of Vehicles platform according to claim 1 is characterized in that: The vehicle driving state test module is used to analyze and process the vehicle driving state data, and obtaining the vehicle driving state analysis results specifically includes the following processes: The vehicle driving state data includes the vehicle GPS data during the vehicle driving process, and the vehicle coordinates P of the n+1th frame are extracted based on the vehicle GPS data. n+1 (X n+1 ,Y n+1 ) and the vehicle coordinates P of the nth frame n (X n ,Y n ); Calculate X n+1 -X n Get dx, Y n+1 -Y n Get dy, normalize dx and dy to get the vector Determine the vehicle's left or right turn based on vector cross multiplication: in, is the vector corresponding to the n+1th frame, is the corresponding vector of the nth frame; When m is less than 0, the vehicle moves to the right, and when m is greater than 0, the vehicle moves to the left; If there is a And the 6 frames starting from the nth frame continuously meet Then it is judged that the vehicle has a turning behavior; Whether the vehicle is driving right or left is taken as the vehicle driving state analysis result.

3. The intelligent testing system based on the Internet of Vehicles platform according to claim 1 is characterized in that: The vehicle driving trajectory test module is used to analyze and process the vehicle driving trajectory data to obtain the trajectory similarity results. The process includes: The vehicle driving trajectory data includes the vehicle driving trajectory distance within the management period, the management period is segmented according to different collection points to obtain a number of management time periods, and the vehicle driving trajectory distances of a number of management time periods are determined based on the number of management time periods; The vehicle driving trajectory distances of several management periods are subtracted from each other and a distance difference matrix M is formed; The distance difference matrix M is compared with the standard distance difference matrix to determine whether the matrix elements in the two matrices are the same, and the number of the same elements in the matrix elements is counted, and the ratio of the number of the same elements in the matrix elements to the number of matrix elements in the standard distance difference matrix is ​​calculated, and the ratio is recorded as the trajectory similarity, and the trajectory similarity is taken as the trajectory similarity result, wherein the standard distance difference matrix is ​​a preset distance difference matrix stored in the vehicle driving trajectory test module; Alternatively, the vehicle driving trajectory data includes the average vehicle driving speed of several management time periods, and the average vehicle driving speed of each management time period is compared with the standard average vehicle driving speed of each management time period, and the number of management time periods corresponding to the same average vehicle driving speed is obtained by statistics, and the proportion of the number of management time periods corresponding to the same average vehicle driving speed to all management time periods in the management cycle is calculated, and the proportion is recorded as trajectory similarity, and the trajectory similarity is used as the trajectory similarity result, wherein the standard average vehicle driving speed of each management time period is a preset average vehicle driving speed stored in the vehicle driving trajectory test module.

4. The intelligent testing system based on the Internet of Vehicles platform according to claim 1 is characterized in that: The vehicle driving control test module is used to analyze and process the vehicle driving control data to obtain the vehicle automatic control results, which specifically includes the following processes: The vehicle driving control data includes automatic braking control data; Based on the automatic braking control data, the vehicle speed V0 when the vehicle starts emergency braking, the longitudinal slope α in the vehicle's driving direction, and the vehicle's driving road adhesion coefficient φ are obtained; Substitute the vehicle speed V0 when the vehicle starts emergency braking, the longitudinal slope α in the vehicle's driving direction, and the vehicle's driving road adhesion coefficient φ into the correlation formula to calculate the automatic braking distance S. The correlation formula is as follows: The automatic braking distance S is taken as the vehicle automatic control result.

5. The intelligent testing system based on the Internet of Vehicles platform according to claim 1 is characterized in that: The vehicle driving control test module is used to analyze and process the vehicle driving control data to obtain the vehicle automatic control results, which specifically includes the following processes: The vehicle driving control data includes automatic steering control data; Obtain the vehicle's automatic steering speed V based on the automatic steering control data z , obtain the horizontal distance H from the wheel hub on the steering side of the vehicle to the road boundary in the steering direction; When automatically steering, calculate the steering wheel deflection angle ω, which is obtained by the formula: Wherein, E is the first constant correction coefficient, Af is the steering wheel sensitivity coefficient; The steering wheel deflection angle ω is taken as the vehicle automatic control result.

6. The intelligent testing system based on the Internet of Vehicles platform according to claim 1 is characterized in that: The vehicle driving control test module is used to analyze and process the vehicle driving control data to obtain the vehicle automatic control results, which specifically includes the following processes: The vehicle driving control data includes automatic acceleration control data; Acquiring a longitudinal distance, a preset expected longitudinal distance, and an acceleration time t based on the automatic acceleration control data, and calculating a difference d between the longitudinal distance and the preset expected longitudinal distance; Calculate the automatic acceleration a: Among them, V m is the expected speed; The automatic acceleration a is taken as the vehicle automatic control result.

7. The intelligent testing system based on the Internet of Vehicles platform according to claim 1 is characterized in that: The Internet of Vehicles platform is used to receive and analyze vehicle driving status analysis results, trajectory similarity results, and vehicle automatic control results, and generate vehicle test result feature values. The process includes: The vehicle driving state analysis results, trajectory similarity results and vehicle automatic control results are analyzed: based on the vehicle driving state analysis results, the number of times the vehicle is driving to the right or left is counted, and the difference is calculated with the preset driving number in the management cycle to obtain the number of times the vehicle driving state is not determined I; based on the trajectory similarity results, the trajectory similarity G is obtained; According to the vehicle automatic control result, an automatic braking distance S, a steering wheel deflection angle ω and an automatic acceleration a are obtained, the automatic braking distance S is subtracted from a preset automatic braking distance threshold to obtain an automatic braking distance difference, the steering wheel deflection angle ω is subtracted from a preset steering wheel deflection angle threshold to obtain a steering wheel deflection angle difference, the automatic acceleration a is subtracted from a preset automatic acceleration threshold to obtain an automatic acceleration difference, and all differences are summed to obtain a difference C; Substitute the number of times the vehicle driving state is not determined I, the trajectory similarity G and the difference C into the correlation formula to calculate the vehicle test result characteristic value LMS, and the correlation formula is as follows: Where Q is a constant.

8. The intelligent testing system based on the Internet of Vehicles platform according to claim 1 is characterized in that: Generating a vehicle test evaluation result based on a vehicle test result characteristic value includes: determining an evaluation level interval corresponding to the vehicle test result characteristic value, and using the evaluation level interval as the evaluation result, wherein each evaluation level interval corresponds to a vehicle test result characteristic value interval.

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