Vehicle infrastructure collaborative application scene test report generation method and system

Abstract

The invention discloses a vehicle-road cooperation application scene test report generation method and system, and relates to the technical field of intelligent traffic, and the method comprises the steps: collecting vehicle driving speed, temperature, wind speed and traffic flow data through a sensor, and carrying out the preprocessing work; key features are extracted from the preprocessed data set, and vehicle condition features are obtained; and constructing a behavior prediction model by using a deep Q network, and inputting the vehicle condition characteristics into the behavior prediction model to obtain behavior prediction values of the vehicle in different scenes. According to the method, a closed-loop test process can be formed from data acquisition, model prediction and actual test, and the closed-loop mechanism not only can improve the quality of data and the accuracy of the model, but also can simulate a real traffic environment through a mixed reality technology, so that the performance of the vehicle-road cooperation system is evaluated more accurately; in addition, by comparing and analyzing a prediction result and actual test data, the performance of the behavior prediction model can be effectively evaluated, and the model and system design is further optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation, and particularly to a method and system for generating a test report for vehicle-road cooperation application scenarios. Background Art

[0002] In recent years, with the rapid development of intelligent transportation systems and autonomous driving technologies, vehicle-road cooperation technology has become one of the important research directions in the field of intelligent transportation; the vehicle-road cooperation technology aims to improve traffic safety, reduce traffic accidents, alleviate traffic congestion, and enhance the driving experience through information interaction between vehicles and road infrastructure; in this context, the method for generating a test report for vehicle-road cooperation application scenarios has become a key link for verifying and optimizing the performance of vehicle-road cooperation systems.

[0003] Existing vehicle-road cooperation test and evaluation methods mostly focus on the impact analysis of single factors, only considering the sensor data of the vehicle itself or only evaluating the performance of a specific function, and failing to fully consider various environmental factors, including the impact of weather and traffic flow on vehicle behavior, resulting in deficiencies in the comprehensiveness and accuracy of test results. In addition, most existing test methods rely on expert experience and simple statistical analysis to construct an evaluation system, lacking a systematic evaluation framework that can automatically adapt to different scenarios. Especially when facing increasingly complex traffic environments and diverse requirements, traditional methods are difficult to meet the growing test accuracy requirements. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for generating a test report for vehicle-road cooperation application scenarios, which solves the problem that existing vehicle-road cooperation test and evaluation methods mostly focus on the impact analysis of single factors, only considering the sensor data of the vehicle itself or only evaluating the performance of a specific function, and failing to fully consider various environmental factors, including the impact of weather and traffic flow on vehicle behavior, resulting in deficiencies in the comprehensiveness and accuracy of test results. In addition, most existing test methods rely on expert experience and simple statistical analysis to construct an evaluation system, lacking a systematic evaluation framework that can automatically adapt to different scenarios. Especially when facing increasingly complex traffic environments and diverse requirements, traditional methods are difficult to meet the growing test accuracy requirements.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for generating a test report for vehicle-road cooperation application scenarios, which includes,

[0008] S1: Collect vehicle driving speed, temperature, wind speed, and traffic flow data using sensors and perform preprocessing work;

[0009] S2: Extract key features from the preprocessed dataset to obtain vehicle condition features;

[0010] S3: Use a deep Q-network to construct a behavior prediction model, input the vehicle condition features into the behavior prediction model, and obtain the behavior prediction values of the vehicle under different scenarios;

[0011] S4: Collect real-time data, input the real-time data into the behavior prediction model to obtain real-time behavior prediction values, and compare and analyze the real-time behavior prediction values with the behavior prediction values;

[0012] S5: Calculate comprehensive evaluation indicators based on the comparison and analysis results, evaluate the performance of the behavior prediction model, and generate a test report.

[0013] As a preferred solution of the method for generating a test report for the vehicle-road collaborative application scenario described in the present invention, wherein: the step of collecting vehicle driving speed, temperature, wind speed, and traffic flow data using sensors and performing preprocessing work is as follows:

[0014] Install a speed sensor, a temperature sensor, a wind speed sensor, and a flow sensor on the vehicle to collect vehicle driving speed, temperature, wind speed, and traffic flow data;

[0015] Preprocess the collected data;

[0016] Integrate the preprocessed vehicle driving speed, temperature, wind speed, and traffic flow data into a dataset, and the expression is:

[0017] x(t) = (v(t), T(t), W(t), F(t));

[0018] Wherein, x(t) is the dataset at time t, t represents the time node, and v(t), T(t), W(t), and F(t) are the vehicle driving speed, ambient temperature, wind speed, and traffic flow at time t, respectively.

[0019] As a preferred solution of the method for generating a test report for the vehicle-road collaborative application scenario described in the present invention, wherein: the step of extracting key features from the preprocessed dataset to obtain vehicle condition features is as follows:

[0020] Generate a speed feature based on the vehicle driving speed data, and the expression is:

[0021]

[0022] Wherein, c1(t) represents the speed feature within time t; v(t + iΔt) represents the vehicle driving speed at time t + iΔt; is the average speed within the time window, n is the number of samples within the time window, and Δt is the sampling interval time;

[0023] Generate temperature features based on temperature data, and the expression is:

[0024]

[0025] where c2(t) is the temperature feature of the vehicle; T(t) represents the ambient temperature at time t; T ref is the reference temperature, T max and T min are the maximum and minimum temperature thresholds respectively;

[0026] Generate wind speed features based on wind speed data, and the expression is:

[0027]

[0028] where c3(t) is the wind speed feature of the vehicle; c3(t) represents the wind speed at time t; W max is the maximum wind speed threshold, and α is the attenuation coefficient;

[0029] Generate traffic flow features based on traffic flow data, and the expression is:

[0030]

[0031] where c4(t) represents the traffic flow feature of the vehicle; F(t) represents the traffic flow at time t; F max is the maximum traffic flow threshold; β and γ are adjustment coefficients;

[0032] Integrate the speed feature, temperature feature, wind speed feature and traffic flow feature into a vehicle condition feature, and the expression is:

[0033] C(t) = [c1(t), c2(t), c3(t), c4(t)];

[0034] where t is the time node, and C(t) represents the vehicle condition feature vector at time point t.

[0035] As a preferred solution of the method for generating a test report on the vehicle-road collaborative application scenario described in the present invention, wherein: constructing a behavior prediction model using a deep Q network is specifically as follows:

[0036] Adopt a deep Q network as the basis of the behavior prediction model;

[0037] Set the state space as S, which respectively corresponds to the vehicle condition feature vector, and the expression is:

[0038] S = {s1, s2, s3, s4};

[0039] Among them, s1 corresponds to c1(t); s2 corresponds to c2(t); s3 corresponds to c3(t); s4 corresponds to c4(t);

[0040] Set the action space as A, which contains three actions: acceleration, deceleration, and steering of the vehicle. The expression is:

[0041] A = {a1, a2, a3};

[0042] Among them, a1 represents the acceleration action of the vehicle; a2 represents the deceleration action of the vehicle; a3 represents the steering action of the vehicle;

[0043] Construct a reward function R(s, a, s ′ ), and the expression is:

[0044] R(s, a, s ′ ) = β1 * exp(-α1 * d1) + β2 * exp(-α2 * d2) + β3 * exp(-α3 *

[0045] d3);

[0046] Among them, s ′ represents the new state of the vehicle; d1 represents the distance to the nearest obstacle, d2 represents the distance to the vehicle in front, and d3 represents the distance of the vehicle from the center of the lane; α1, α2, and α3 are the attenuation coefficients of d1, d2, and d3 respectively; β1, β2, and β3 are the weight coefficients corresponding to d1, d2, and d3 respectively.

[0047] As a preferred scheme of the method for generating a test report on the vehicle-road collaborative application scenario described in the present invention, wherein: inputting the vehicle condition characteristics into the behavior prediction model to obtain the behavior prediction values of the vehicle in different scenarios, and the expression is:

[0048] P(t) = argmax a Q(s, a; θ);

[0049] Among them, P(t) is the behavior prediction value, θ is the parameter vector, and argmax a represents finding the best action among all possible actions a.

[0050] As a preferred scheme of the method for generating a test report on the vehicle-road collaborative application scenario described in the present invention, wherein: collecting real-time data, inputting the real-time data into the behavior prediction model to obtain the real-time behavior prediction value, and comparing and analyzing the real-time behavior prediction value with the behavior prediction value, specifically as follows:

[0051] Collect real-time data, process the real-time data to obtain real-time vehicle condition characteristics, and input the real-time vehicle condition characteristics into the behavior prediction model to obtain the real-time behavior prediction value Ptest(t);

[0052] Create a comparative analysis function H based on the recorded data and the prediction results of the behavior prediction model. The expression is:

[0053] H = n1∑i = 1n(Ptest(t + iΔt)∣Ptest(t + iΔt)-P(t + iΔt)∣)2·

[0054] exp(-αo∣Δv(t + iΔt)∣);

[0055] Where Ptest(t) represents the real-time behavior prediction value, n represents the number of samples, Δt is the sampling interval time, αo is the attenuation coefficient, Δv represents the speed change amount, Δv(t + iΔt) is the speed change amount at time t + iΔt, i represents the index variable, and exp represents the exponential function.

[0056] As a preferred solution of the method for generating a test report for the vehicle-road collaborative application scenario described in the present invention, wherein: calculating a comprehensive evaluation index based on the comparative analysis result, evaluating the performance of the behavior prediction model, and generating a test report, specifically as follows:

[0057] Create a comprehensive evaluation index E(t) based on the result of the comparative analysis. The expression is:

[0058]

[0059] Where j represents the index of each time point in the time series; j iterates from 1 to m, m is the total number of sampling points, and Δt is the sampling interval time;

[0060] The value of E(t) ranges from 0 to positive infinity;

[0061] Set a threshold value TT;

[0062] When 0 < E(t) ≤ TT, it indicates that the prediction ability of the behavior prediction model is strong;

[0063] When E(t) > threshold value TT, it indicates that the prediction ability of the behavior prediction model is poor. Re-input the data into the behavior prediction model for training until 0 < E(t) ≤ TT;

[0064] Based on the result of E(t), generate a test report. The test report includes the specific value of E(t), the basic information of the model, the time period and conditions of data collection, the type of sensors used and their accuracy, the description of the experimental environment, the detailed result of the comparative analysis, the model optimization suggestions, and the future improvement direction.

[0065] In a second aspect, the present invention provides a vehicle-road collaborative application scenario test report generation system, including:

[0066] A data acquisition module, responsible for collecting vehicle driving speed, temperature, wind speed, and traffic flow data using sensors. This module also includes a preprocessing process for data cleaning, missing value handling, and outlier detection.

[0067] A feature extraction module, responsible for extracting key features from the preprocessed dataset and extracting vehicle condition features from the key features.

[0068] A prediction module, responsible for constructing a behavior prediction model using a deep Q-network and inputting the vehicle condition features into the behavior prediction model to obtain behavior prediction values of the vehicle under different scenarios.

[0069] An analysis module, responsible for inputting real-time data into the behavior prediction model to obtain real-time behavior prediction values, and comparing and analyzing the real-time behavior prediction values with the behavior prediction values.

[0070] A performance evaluation module, responsible for calculating comprehensive evaluation indicators using the comparative analysis results, evaluating the performance of the behavior prediction model, and organizing the test data to generate an easy-to-understand test report.

[0071] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor. The memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the vehicle-road collaborative application scenario test report generation method described in the first aspect of the present invention is implemented.

[0072] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein: when the computer program is executed by the processor, any step of the vehicle-road collaborative application scenario test report generation method described in the first aspect of the present invention is implemented.

[0073] The beneficial effects of the present invention are as follows: The present invention can form a closed-loop test process from data acquisition to model prediction and then to actual testing. This closed-loop mechanism can not only improve the quality of data and the accuracy of the model, but also simulate the real traffic environment through mixed reality technology, thereby more accurately evaluating the performance of the vehicle-road collaborative system; in addition, by comparing and analyzing the prediction results with the actual test data, the performance of the behavior prediction model can be effectively evaluated, further optimizing the model and system design, significantly improving the efficiency and accuracy of vehicle-road collaborative scenario testing, and promoting the development and improvement of intelligent transportation systems. Description of the Drawings

[0074] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0075] Figure 1 It is a flowchart of the method for generating a test report of the vehicle-road collaborative application scenario in Embodiment 1.

[0076] Figure 2 It is a flowchart of the system for generating a test report of the vehicle-road collaborative application scenario in Embodiment 1. Specific Embodiments

[0077] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification.

[0078] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0079] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments.

[0080] Embodiment 1, referring to Figure 1 and Figure 2 , is the first embodiment of the present invention. This embodiment provides a method for generating a test report of a vehicle-road collaborative application scenario, including the following steps:

[0081] S1 Use sensors to collect vehicle driving speed, temperature, wind speed, and traffic flow data and perform preprocessing work;

[0082] Install a speed sensor, a temperature sensor, a wind speed sensor, and a flow sensor on the vehicle to collect vehicle driving speed, temperature, wind speed, and traffic flow data;

[0083] The accurate capture of multi-dimensional data during vehicle driving provides a high-quality data basis for subsequent data analysis.

[0084] Use a data preprocessing algorithm to preprocess the collected data. The preprocessing work includes:

[0085] Data cleaning: Remove invalid data points and noise data to ensure data validity;

[0086] Missing value handling: Use interpolation methods to fill in missing values. Here, the linear interpolation method is adopted, that is, for missing data points, the nearest valid data points before and after are used for linear interpolation to estimate the missing values;

[0087] Outlier detection: Use statistical methods to identify and handle outliers. Specifically, the Z-score method is adopted, that is, any value below the mean minus 3 standard deviations or above the mean plus 3 standard deviations is considered an outlier, and the mean of the variable is used to replace the outlier.

[0088] Integrate the preprocessed vehicle driving speed, temperature, wind speed, and traffic flow data into a dataset, and the expression is:

[0089] x(t) = (v(t), T(t), W(t), F(t));

[0090] Among them, x(t) is the dataset at time t, t represents the time node, and v(t), T(t), W(t), and F(t) are the vehicle driving speed, ambient temperature, wind speed, and traffic flow at time t, respectively.

[0091] The preprocessing work ensures the consistency and integrity of the data, thereby improving the reliability and effectiveness of the entire system.

[0092] S2 Extract key features from the preprocessed dataset to obtain vehicle condition features;

[0093] Based on the vehicle driving speed data, use the standard deviation calculation method in statistics to generate speed features, and the expression is:

[0094]

[0095] Among them, c1(t) represents the speed feature within time t; v(t + iΔt) represents the vehicle driving speed at time t + iΔt; is the average speed within the time window, n is the number of samples within the time window, and Δt is the sampling interval time;

[0096] Based on the temperature data, use the normalization processing method to generate temperature features, and the expression is:

[0097]

[0098] Among them, c2(t) is the temperature feature of the vehicle; T(t) represents the ambient temperature at time t; T ref is the reference temperature, T max and T minThey are the maximum and minimum temperature thresholds respectively;

[0099] Based on the wind speed data, combining the methods of linear normalization and exponential decay to generate the wind speed feature, the expression is:

[0100]

[0101] where c3(t) is the wind speed feature of the vehicle; c3(t) represents the wind speed at time t; W max is the maximum wind speed threshold, and α is the decay coefficient;

[0102] Based on the traffic flow data, combining the methods of linear normalization and periodic function modulation to generate the traffic flow feature, the expression is:

[0103]

[0104] where c4(t) represents the traffic flow feature of the vehicle; F(t) represents the traffic flow at time t; F max is the maximum traffic flow threshold; β and γ are adjustment coefficients;

[0105] Integrate the speed feature, temperature feature, wind speed feature and traffic flow feature into the vehicle condition feature, the expression is:

[0106] C(t) = [c1(t), c2(t), c3(t), c4(t)];

[0107] where t is the time node, and C(t) represents the vehicle condition feature vector at time point t;

[0108] Specifically, the speed feature refers to the degree of speed fluctuation within time t, the temperature feature refers to the influence degree factor of the environmental temperature on the vehicle performance, the wind speed feature refers to the influence degree factor of measuring the wind speed on the vehicle driving, and the traffic flow feature refers to the influence degree of the traffic flow on the vehicle driving.

[0109] By extracting key features, not only the data structure is simplified, but also the correlation between features is enhanced, enabling the behavior prediction model to more effectively learn and understand the performance of the vehicle under different environmental conditions, and further improving the model's understanding ability and prediction accuracy for complex scenarios.

[0110] S3 uses a deep Q-network to construct a behavior prediction model, inputs the vehicle condition feature into the behavior prediction model, and obtains the behavior prediction values of the vehicle under different scenarios;

[0111] Adopt a deep Q-network as the basis of the behavior prediction model;

[0112] Set the state space as S, corresponding to the vehicle condition feature vector respectively, the expression is:

[0113] S = {s1, s2, s3, s4};

[0114] Among them, s1 corresponds to c1(t); s2 corresponds to c2(t); s3 corresponds to c3(t); s4 corresponds to c4(t);

[0115] Set the action space as A, which contains three actions: vehicle acceleration, deceleration, and steering. The expression is:

[0116] A = {a1, a2, a3};

[0117] Among them, a1 represents the acceleration action of the vehicle; a2 represents the deceleration action of the vehicle; a3 represents the steering action of the vehicle;

[0118] Construct a reward function R(s, a, s ′ ), and the expression is:

[0119] R(s, a, s ′ ) = β1 * exp(-α1 * d1) + β2 * exp(-α2 * d2) + β3 * exp(-α3 *

[0120] d3);

[0121] Among them, s ′ represents the new state of the vehicle after executing a; d1 represents the distance to the nearest obstacle, d2 represents the distance to the vehicle in front, and d3 represents the distance of the vehicle from the center of the lane; α1, α2, and α3 are the attenuation coefficients of d1, d2, and d3 respectively; β1, β2, and β3 are the weight coefficients corresponding to d1, d2, and d3 respectively.

[0122] The functions of the reward function are as follows:

[0123] Guiding behavior: The reward function guides the vehicle's behavior by giving positive or negative reward values. Positive rewards encourage the vehicle to take similar actions, while negative rewards make the vehicle avoid repeating the same actions.

[0124] Optimizing strategies: By maximizing the cumulative reward, the reinforcement learning algorithm can learn the optimal behavior strategy. The design of the reward function directly affects what kind of strategy the agent learns.

[0125] Safety considerations: The reward function can be designed to include safety considerations. For example, reducing the distance to an obstacle will result in a negative reward, while maintaining an appropriate safety distance will result in a positive reward.

[0126] Efficiency and comfort: In addition to safety, the reward function can also consider driving efficiency (such as reducing unnecessary acceleration and deceleration) and passenger comfort (such as smooth acceleration and deceleration).

[0127] Multi-objective optimization: In a complex driving environment, the reward function may need to consider multiple objectives simultaneously, such as the time to reach the destination, fuel consumption, passenger satisfaction, etc. The design of the reward function requires balancing these different objectives.

[0128] S4 collects real-time data, inputs the real-time data into the behavior prediction model to obtain real-time behavior prediction values, and conducts a comparative analysis of the real-time behavior prediction values and the behavior prediction values.

[0129] The expression of the prediction model is:

[0130] P(t) = argmax a Q(s,a;θ);

[0131] where P(t) is the behavior prediction value, θ is the parameter vector, and argmax a represents finding the best action among all possible actions a;

[0132] Collect real-time data, process the real-time data to obtain real-time vehicle condition characteristics, input the real-time vehicle condition characteristics into the behavior prediction model to obtain real-time behavior prediction value Ptest(t);

[0133] Based on the recorded data and the prediction results of the behavior prediction model, create a comparative analysis function H, and the expression is:

[0134] H = n1∑i = 1n(Ptest(t + iΔt)∣Ptest(t + iΔt) - P(t + iΔt)∣)2·

[0135] exp(-αo∣Δv(t + iΔt)∣);

[0136] where Ptest(t) represents the real-time behavior prediction value, n represents the number of samples, Δt is the sampling interval time, αo is the attenuation coefficient, Δv represents the speed change amount, Δv(t + iΔt) is the speed change amount at time t + iΔt, i represents the index variable, and exp represents the exponential function.

[0137] S5 calculates a comprehensive evaluation index based on the comparative analysis results, evaluates the performance of the behavior prediction model, and generates a test report.

[0138] Based on the results of the comparative analysis, create a comprehensive evaluation index E(t), and the expression is:

[0139]

[0140] where j represents the index of each time point in the time series; j iterates from 1 to m, m is the total number of sampling points, and Δt is the sampling interval time;

[0141] The value range of E(t) is from 0 to positive infinity;

[0142] Set a threshold TT, and the value of TT can be flexibly adjusted according to specific test requirements;

[0143] When 0 < E(t) ≤ TT, it indicates that the prediction ability of the behavior prediction model is strong;

[0144] When E(t) > the threshold TT, it indicates that the prediction ability of the behavior prediction model is poor. The data will be re - input into the behavior prediction model for training until 0 < E(t) ≤ TT.

[0145] The smaller the evaluation index E(t), the higher the prediction accuracy of the model. In this way, we can quantitatively understand the performance of the model at different times and in different scenarios, and further optimize the model accordingly;

[0146] Based on the results of E(t), a test report is generated. The test report includes the specific value of E(t), the basic information of the model, the time period and conditions of data collection, the types of sensors used and their accuracies, the description of the experimental environment, the detailed results of comparative analysis, suggestions for model optimization, and future improvement directions.

[0147] This embodiment also provides a test report generation system for vehicle - road collaborative application scenarios, including:

[0148] A data acquisition module, responsible for collecting vehicle driving speed, temperature, wind speed, and traffic flow data using sensors. This module also includes pre - processing processes such as data cleaning, missing value handling, and outlier detection;

[0149] A feature extraction module, responsible for extracting key features from the pre - processed dataset and extracting vehicle condition features from the key features;

[0150] A prediction module, responsible for constructing a behavior prediction model using a deep Q - network and inputting the vehicle condition features into the behavior prediction model to obtain the behavior prediction values of the vehicle in different scenarios;

[0151] An analysis module, responsible for inputting real - time data into the behavior prediction model to obtain real - time behavior prediction values, and comparing and analyzing the real - time behavior prediction values with the behavior prediction values;

[0152] Responsible for calculating a comprehensive evaluation index using the results of comparative analysis, evaluating the performance of the behavior prediction model, and organizing the test data to generate an easy - to - understand test report.

[0153] This embodiment also provides a computer device, which is applicable to the situation of the method for generating a test report for a vehicle-road collaborative application scenario, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for generating a test report for a vehicle-road collaborative application scenario as proposed in the above embodiment.

[0154] The computer device can be a terminal. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0155] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for generating a test report for a vehicle-road collaborative application scenario as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM for short), Electrically Erasable Programmable Read-Only Memory (EEPROM for short), Erasable Programmable Read-Only Memory (EPROM for short), Programmable Read-Only Memory (PROM for short), Read-Only Memory (ROM for short), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0156] In summary, the present invention forms a closed-loop test process from data collection to model prediction and then to actual testing. This closed-loop mechanism can not only improve the quality of data and the accuracy of the model, but also simulate the real traffic environment through mixed reality technology, thereby more accurately evaluating the performance of the vehicle-road collaborative system. In addition, by comparing and analyzing the prediction results with the actual test data, the performance of the behavior prediction model can be effectively evaluated, further optimizing the model and system design, significantly improving the efficiency and accuracy of vehicle-road collaborative scenario testing, and promoting the development and improvement of intelligent transportation systems.

[0157] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A method for generating a test report on a vehicle-road collaborative application scenario, characterized in that: including, S1: Using sensors to collect vehicle driving speed, temperature, wind speed, and traffic flow data and perform preprocessing work; S2: Extracting key features from the preprocessed dataset to obtain vehicle condition features; S3: Using a deep Q-network to construct a behavior prediction model, inputting the vehicle condition features into the behavior prediction model to obtain behavior prediction values of the vehicle under different scenarios; S4: Collecting real-time data, inputting the real-time data into the behavior prediction model to obtain real-time behavior prediction values, and comparing and analyzing the real-time behavior prediction values with the behavior prediction values; S5: Calculating a comprehensive evaluation index based on the comparison and analysis results, evaluating the performance of the behavior prediction model, and generating a test report.

2. The method for generating a test report on a vehicle-road collaborative application scenario according to claim 1, wherein: The step of using sensors to collect vehicle driving speed, temperature, wind speed, and traffic flow data and perform preprocessing work is as follows: Installing a speed sensor, a temperature sensor, a wind speed sensor, and a traffic flow sensor on the vehicle to collect vehicle driving speed, temperature, wind speed, and traffic flow data; Preprocessing the collected data; Integrating the preprocessed vehicle driving speed, temperature, wind speed, and traffic flow data into a dataset.

3. The method for generating a test report of a vehicle-road collaborative application scenario according to claim 2, wherein: The step of extracting key features from the preprocessed dataset to obtain vehicle condition features is as follows: Generating a speed feature based on the vehicle driving speed data; Generating a temperature feature based on the temperature data; Generating a wind speed feature based on the wind speed data; Generating a traffic flow feature based on the traffic flow data; Integrating the speed feature, the temperature feature, the wind speed feature, and the traffic flow feature into vehicle condition features.

4. The method for generating a test report on vehicle-road collaborative application scenarios according to claim 3, wherein: The step of using a deep Q-network to construct a behavior prediction model is as follows: Adopting a deep Q-network as the basis of the behavior prediction model; Setting the state space as S, corresponding to the vehicle condition feature vector respectively; Setting the action space as A, including three actions: accelerating, decelerating, and turning of the vehicle; Construct the reward function R(s, a, s based on the state space and the action space ′ ).

5. The method for generating a test report on a vehicle-road collaborative application scenario according to claim 4, wherein: Inputting the vehicle condition features into the behavior prediction model to obtain behavior prediction values of the vehicle under different scenarios.

6. The method for generating a test report of a vehicle-road collaborative application scenario according to claim 5, wherein: The step of collecting real-time data, inputting the real-time data into the behavior prediction model to obtain real-time behavior prediction values, and comparing and analyzing the real-time behavior prediction values with the behavior prediction values is as follows: Collecting real-time data, processing the real-time data to obtain real-time vehicle condition features, inputting the real-time vehicle condition features into the behavior prediction model to obtain real-time behavior prediction value Ptest(t); Creating a comparison and analysis function H based on the recorded data and the prediction results of the behavior prediction model.

7. The method for generating a test report on a vehicle-road collaborative application scenario according to claim 6, characterized in that: The step of calculating a comprehensive evaluation index based on the comparison and analysis results, evaluating the performance of the behavior prediction model, and generating a test report is as follows: Creating a comprehensive evaluation index E(t) based on the comparison and analysis results; The value range of E(t) is from 0 to positive infinity; Setting a threshold TT; When 0 < E(t) ≤ TT, it indicates that the prediction ability of the behavior prediction model is strong; When E(t) > threshold TT, it indicates that the prediction ability of the behavior prediction model is poor, and the data is re-input into the behavior prediction model for training until 0 < E(t) ≤ TT; Based on the results of E(t), a test report is generated. The test report includes the specific value of E(t), the basic information of the model, the time period and conditions of data collection, the type of sensors used and their accuracy, the description of the experimental environment, the detailed results of comparative analysis, suggestions for model optimization, and future improvement directions.

8. A vehicle-road collaborative application scenario test report generation system, based on the vehicle-road collaborative application scenario test report generation method according to any one of claims 1 to 7, characterized in that, Including: A data acquisition module, which is responsible for collecting vehicle driving speed, temperature, wind speed, and traffic flow data using sensors. This module also includes a preprocessing process for data cleaning, missing value handling, and outlier detection; A feature extraction module, which is responsible for extracting key features from the preprocessed dataset and extracting vehicle condition features from the key features; A prediction module, which is responsible for constructing a behavior prediction model using a deep Q-network and inputting the vehicle condition features into the behavior prediction model to obtain the behavior prediction values of the vehicle under different scenarios; An analysis module, which is responsible for inputting real-time data into the behavior prediction model to obtain real-time behavior prediction values and comparing and analyzing the real-time behavior prediction values with the behavior prediction values; A performance evaluation module, which is responsible for calculating comprehensive evaluation indicators using the results of comparative analysis, evaluating the performance of the behavior prediction model, and organizing the test data to generate an easy-to-understand test report.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for generating a test report for vehicle-road collaborative application scenarios described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for generating a test report for vehicle-road collaborative application scenarios described in any one of claims 1 to 7.

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