A detection method, system and storage medium for a driving recorder

Abstract

The present invention relates to the field of data testing, and more specifically, to a detection method, system and storage medium for a driving recorder. The method includes: constructing multiple driving environments; simulating driving states in the multiple driving environments to detect the recorded data and audio-video recording functions of the driving recorder, and obtaining the recorded data of the driving recorder; automatically parsing the recorded data of the driving recorder to obtain recorded detection data; performing communication function detection; detecting the driving behavior recognition and driving safety prompt functions of the driving recorder; detecting the safety warning response speed and accuracy of the recorder; collecting and analyzing multiple detection data based on big data technology to obtain a final detection result. The present invention can comprehensively evaluate the performance of the driving recorder. Secondly, by constructing various driving environments and simulating driving behaviors, the performance of the driving recorder can be more realistically reflected, and the obtained detection results are also more in line with the actual situation, thereby improving the accuracy of detection.

Description

Technical Field

[0001] The present invention relates to the field of data testing, and more specifically, to a detection method, system and storage medium for a driving recorder. Background Art

[0002] A driving recorder is a device widely used in real life. It accurately records the driving conditions of a vehicle and is an important basis in scenarios such as accident liability determination and driving behavior management. In recent years, with the progress of technology and the trend of intelligentization, the driving recorder no longer only has a single recording function, but also adds intelligent functions such as navigation, safety warning, audio and video recording, and data analysis. With the development of technology, the functions of the driving recorder are increasing, such as vehicle status monitoring, navigation, voice recognition, audio and video recording, vehicle safety warning, etc. Therefore, the detection of the driving recorder is particularly important to ensure its quality and accuracy to meet the daily use requirements. However, because the driving recorder involves many types of data, including video data, voice data, vehicle driving parameters, etc., problems may occur in terms of data accuracy, reliability, and stability of the driving recorder in actual operation during the design and manufacturing process. Therefore, the detection and evaluation work of the driving recorder is crucial. Traditional detection methods often detect single parameters and cannot comprehensively evaluate the performance of the driving recorder. Moreover, due to the limitations of the detection environment, it is impossible to simulate the complex and changeable driving environment in the real world.

[0003] The prior art discloses a function detection device and method for an automotive driving recorder. Among them, the device includes an industrial control computer, an electrical control cabinet, and a test fixture. An upper computer software is installed on the industrial control computer. The electrical control cabinet is used to receive the detection instructions sent by the upper computer software, perform full-function detection on the driving recorder according to the detection instructions, and receive the feedback information of the driving recorder and send it to the industrial control computer. The industrial control computer receives the feedback information and determines the detection result through the upper computer software. However, this method only detects through the detection instructions sent by the upper computer software and cannot well reflect the functions of the driving recorder in real driving situations, resulting in insufficient detection accuracy. Summary of the Invention

[0004] The object of the present invention is to disclose a detection method, system and storage medium for a driving recorder with better detection effect.

[0005] To achieve the above object, the present invention provides a detection method for a driving recorder, including:

[0006] S1: Construct multiple driving environments;

[0007] S2: Simulate the driving state in various driving environments to detect the recording data and audio-video recording function of the driving recorder, and obtain the recording data of the driving recorder; automatically analyze the recording data of the driving recorder to obtain the recorded detection data;

[0008] S3: Conduct communication function detection in various driving environments to obtain dynamic detection data of wireless communication capabilities;

[0009] S4: Simulate various driving behaviors in various driving environments, and detect the driving behavior recognition and driving safety prompt functions of the driving recorder to obtain behavior-based driving detection data;

[0010] S5: Simulate the safety warning response speed and accuracy of the recorder when an accident occurs in various driving environments to obtain intelligent safety warning detection data;

[0011] S6: Based on big data technology, collect and analyze the recorded detection data, dynamic detection data of wireless communication capabilities, driving detection data, and intelligent safety warning detection data to obtain the final detection result.

[0012] Further, the various driving environments include at least one of the following driving environments: rainy and snowy weather, night driving, and urban congestion environment.

[0013] Further, the construction of various driving environments includes:

[0014] Define parameterized driving environment characteristics, including: road type, weather conditions, lighting conditions, vehicle status, and other traffic factors;

[0015] Generate a simulation environment according to the specific conditions to be tested:

[0016] When simulating the situation of urban roads, divide the road into multiple segments, and the length, driving speed, and number of vehicles ahead of each segment are generated by a given parameter generation model;

[0017] Describe the driving environment by a parameter vector P

[0018] P = [p1, p2,..., pn]

[0019] where pi represents the i-th environmental parameter;

[0020] Generate a corresponding simulation environment according to the parameter vector P; specifically, each environmental parameter is generated by a generation function fi:

[0021] pi = fi(P')

[0022] where P' is a set of input parameters and is a random variable.

[0023] Furthermore, the automatic parsing of the records of the driving recorder includes:

[0024] Using graph neural networks (GNNs) to analyze the data of the driving recorder:

[0025] Construct a graph model, regarding vehicles and roads as nodes, and the distance and direction between vehicles and roads as edges, thereby constructing a traffic network graph;

[0026] Feature extraction: For each node, define a feature vector to represent the state of the node. The state includes the speed and acceleration of the vehicle, and the length, width, and speed limit of the road. The graph neural networks (GNNs) will automatically perform information propagation and integration, so that each node contains not only its own information but also the information of adjacent nodes. Among them, feature extraction is expressed as a function H, and H(v) represents the feature vector of node v;

[0027] Training of graph neural networks (GNNs): Train according to real traffic data, including traffic flow and traffic accidents, to obtain a trained graph model;

[0028] Based on the trained graph model, predict the traffic data and conditions within a future period of time, and compare the predicted traffic data and conditions within a future period of time with the traffic data and conditions of the simulated driving state to obtain record detection data.

[0029] Furthermore, perform communication function detection in multiple driving environments to obtain dynamic detection data of wireless communication capabilities, including:

[0030] Define the set of measurement parameters Q = {moving speed V, communication distance D, network bandwidth B, network latency L}, where V, D, B, and L represent the moving speed, communication distance, network bandwidth, and network latency respectively;

[0031] Suppose the data transmission stabilities of the USB, Bluetooth, and wireless public network of the driving recorder are U, B, and W respectively, and measure them through the functions f(Q) = U, g(Q) = B, and h(Q) = W;

[0032] In a simulated mobile environment, set specific moving speed V1, communication distance D1, network bandwidth B1, and network latency L1, that is, set the parameter set Q1 = {V1, D1, B1, L1}, and then measure the data transmission stabilities U1, B1, and W1 at this time according to f(Q1), g(Q1), and h(Q1);

[0033] Then, change the mobile environment conditions, set a new parameter set Q2 = {V2, D1, B1, L1}, where V2 > V1, and measure U2, B2, and W2 at this time;

[0034] According to the above steps, multiple different parameter sets are obtained respectively. By measuring the data transmission stability under each group of conditions, a series of data transmission stability and efficiency values are obtained: (U1, B1, W1), (U2, B2, W2),..., (Un, Bn, Wn); the series of data transmission stability and efficiency values are used as dynamic detection data for wireless communication capabilities.

[0035] Furthermore, various driving behaviors are simulated in multiple driving environments, and the driving behavior recognition and driving safety prompt functions of the driving recorder are detected to obtain behavior-based driving detection data:

[0036] Define the driving behavior description set A = {a1, a2,..., an}, where a1, a2,..., an are parameters related to driving behaviors such as speed, steering angle, braking force, and vehicle relative position; at the same time, define the result set R = {r1, r2,..., rn} of driving behavior recognition, where r1, r2,..., rn represent the recognized driving behavior types, including normal driving, hard acceleration, and hard braking;

[0037] Define the driving behavior recognition function of the driving recorder as:

[0038] R = f(A),

[0039] where f is the internal algorithm of the driving recorder, which outputs the recognized driving behavior type set R by inputting the driving behavior description set A;

[0040] Similarly, define the driving safety prompt function of the driving recorder as a function g, the input is the recognized driving behavior type set R, and the output is the safety prompt set S, that is

[0041] S = g(R);

[0042] In the simulation of various driving behaviors, first, a series of driving behavior description sets A including normal driving situations and various abnormal driving behaviors are generated in the driving environment, and then input into the driving recorder. The recognized driving behavior result set R is obtained through f(A), and further the safety prompt set S is obtained through g(R);

[0043] For each driving behavior description set A, there is an expected driving behavior recognition result set R' and an expected safety prompt set S'. The actual R and S are compared with the expected R' and S' to obtain the driving behavior recognition accuracy and driving safety prompt accuracy of the driving recorder; the driving behavior recognition accuracy and driving safety prompt accuracy of the driving recorder are used as behavior-based driving detection data.

[0044] Furthermore, in various driving environments, the safety warning response speed and accuracy of the recorder are detected when simulating an accident, and intelligent safety warning detection data is obtained:

[0045] Define the set of simulated driving environment parameters E = {e1, e2,..., ei}, where ei represents the i-th driving environment parameter, including: vehicle speed, vehicle distance, road conditions;

[0046] Define the response speed of the intelligent safety warning as V and the response accuracy as A. These two parameters are calculated through functions f(E) and g(E): V = f(E), A = g(E);

[0047] During the testing process, first set the parameter set E of the simulation environment as a specific accident occurrence environment. If the vehicle distance is too close, e1 = vehicle distance, then set it as a too-close distance. If the vehicle speed is too fast, e2 = vehicle speed, then set it as a too-fast speed;

[0048] Calculate the response speed V and the response accuracy A, that is, V1 = f(E), A1 = g(E);

[0049] Then change the parameters of the simulation environment, and obtain a series of Vs and As according to the above steps;

[0050] Summarize this series of response speeds and response accuracies, denoted as (V1, A1), (V2, A2),...,(Vn, An); and conduct statistical analysis on the values of V and A, and the results obtained are used as intelligent safety warning detection data.

[0051] Furthermore, based on big data technology, collect and analyze the recorded detection data, wireless communication ability dynamic detection data, driving detection data, and intelligent safety warning detection data to obtain the final detection results, including:

[0052] Define all the data collected during the testing process as the data set D, which includes driving environment parameters, driving recorder parameters, and various performance test results, that is

[0053] D = {E, A, R, S, U, B, W, V1, A1, V2, A2,..., Vn, An};

[0054] Among them, E = {e1, e2,..., en} represents the parameters of various driving environments; A = {a1, a2,..., an} represents the parameters of driving actions; R = {r1, r2,..., rn} represents the recognized types of driving behaviors; S = {s1, s2,..., sn} represents the safety prompts of the driving recorder; U, B, and W respectively represent the data transmission stability and efficiency of USB, Bluetooth, and wireless public networks; V1, A1, V2, A2,..., Vn, An respectively represent the safety warning response speed and accuracy of the intelligent warning system in various driving environments.

[0055] Perform data preprocessing through big data technology, remove outliers and invalid data, and perform data cleaning:

[0056] Outliers Z and invalid data I are defined by the following formulas;

[0057] Z = x|x ∈ D, and the value of x is outside the range of its maximum and minimum values,

[0058] I = x|x ∈ D, and the value of x is missing or unreasonable,

[0059] The excluded dataset D' is defined as:

[0060] D' = D - Z - I;

[0061] Obtain the cleaned dataset D',

[0062] D' = {E', A', R', S', U', B', W', V1', A1', V2', A2',..., Vn', An'};

[0063] For the dataset D', use a variety of statistical analysis and machine learning methods for in-depth analysis or use descriptive statistical analysis methods to analyze it, including calculating the mean μ, median θ, and standard deviation σ of each parameter. The formulas are as follows:

[0064]

[0065] (if N is odd); or θ = (xi + xi+1) / 2 (if N is even);

[0066]

[0067] Where N is the sample size and xi is the value of the i-th sample;

[0068] Use correlation analysis to describe the correlation between each parameter. Use the Pearson correlation coefficient r for correlation analysis. The calculation method is as follows:

[0069]

[0070] Among them, xi and yi represent the x and y values of the i-th sample, μx and μy are the average values of x and y, and are the standard deviations of x and y;

[0071] For key performance indicators such as recognition accuracy and warning accuracy, the machine learning model SVM is applied for prediction and optimization; the driving behavior recognition accuracy P is provided, and the prediction function p(X) = a*X + b is defined, where X is a set of parameters, and a and b are coefficients to be solved; the parameters of the prediction function are optimized by minimizing the error between the predicted value and the actual value, and the error function E(a,b) is defined as:

[0072] E(a,b) = Σ[p(Xi) - Pi] 2

[0073] Among them, Pi is the true driving behavior recognition accuracy, and p(Xi) is the predicted value;

[0074] The clustering analysis method is used for data mining to find the potential laws of the data; among them, a commonly used index for evaluating the clustering result is the silhouette coefficient S, and the calculation formula is as follows:

[0075] S = (b - a) / max(a,b)

[0076] Among them, a represents the average distance of the sample from the samples of the same category, and b represents the minimum average distance of the sample from the samples of other categories.

[0077] In addition, the present invention also provides a tachograph detection system, including:

[0078] Environment construction module: constructing various driving environments;

[0079] Recording detection module: simulating the driving state in various driving environments to detect the recording data and audio-visual recording function of the tachograph, obtaining the recording data of the tachograph; automatically parsing the recording data of the tachograph to obtain recording detection data;

[0080] Wireless communication ability dynamic detection module: performing communication function detection in various driving environments to obtain wireless communication ability dynamic detection data;

[0081] Driving detection module: simulating various driving behaviors in various driving environments to detect the driving behavior recognition and driving safety prompt functions of the tachograph to obtain behavior-based driving detection data;

[0082] Intelligent safety warning detection module: Detect the safety warning response speed and accuracy of the recorder when simulating an accident in various driving environments to obtain intelligent safety warning detection data;

[0083] Final detection module: Based on big data technology, collect and analyze the recorded detection data, dynamic detection data of wireless communication capabilities, driving detection data, and intelligent safety warning detection data to obtain the final detection result.

[0084] In addition, the present invention also provides a driving recorder detection storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned detection method of a driving recorder is implemented.

[0085] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0086] The present invention conducts a comprehensive test on various functions of the driving recorder by constructing various driving environments, and obtains recorded detection data, dynamic detection data of wireless communication capabilities, driving detection data, and intelligent safety warning detection data, which can comprehensively evaluate the performance of the driving recorder. Secondly, by constructing various driving environments and simulating driving behaviors, the performance of the driving recorder can be more realistically reflected, and the obtained detection results are also more in line with the actual situation, thereby improving the detection effect. Description of the Drawings

[0087] Figure 1 It is a flowchart of a detection method for a driving recorder described in Embodiment 1;

[0088] Figure 2 It is a final detection flowchart described in Embodiment 2;

[0089] Figure 3 It is a block diagram of a driving recorder detection system described in Embodiment 3; Detailed Embodiments

[0090] The drawings are only for illustrative purposes and should not be construed as a limitation of this patent;

[0091] The technical solution of the present invention will be further described below with reference to the drawings and embodiments.

[0092] Embodiment 1:

[0093] This embodiment provides a detection method for a driving recorder as shown in Figure 1 and includes:

[0094] Construct various driving environments;

[0095] Simulate the driving state in various driving environments to detect the recording data and audio-video recording function of the driving recorder, and obtain the recording data of the driving recorder; automatically analyze the recording data of the driving recorder to obtain the recorded detection data;

[0096] Conduct communication function detection in various driving environments to obtain dynamic detection data of wireless communication capabilities;

[0097] Simulate various driving behaviors in various driving environments, and detect the driving behavior recognition and driving safety prompt functions of the driving recorder to obtain behavior-based driving detection data;

[0098] Simulate the safety warning response speed and accuracy of the recorder when an accident occurs in various driving environments to obtain intelligent safety warning detection data;

[0099] Based on big data technology, collect and analyze the recorded detection data, dynamic detection data of wireless communication capabilities, driving detection data, and intelligent safety warning detection data to obtain the final detection result.

[0100] In this embodiment, by constructing various driving environments, all functions of the driving recorder are comprehensively tested, and the recorded detection data, dynamic detection data of wireless communication capabilities, driving detection data, and intelligent safety warning detection data can be used to comprehensively evaluate the performance of the driving recorder. Secondly, by constructing various driving environments and simulating driving behaviors, the performance of the driving recorder can be more realistically reflected, and the obtained detection results are also more in line with the actual situation, thus improving the detection effect.

[0101] Embodiment 2:

[0102] This embodiment makes a further disclosure on the basis of Embodiment 1:

[0103] The various driving environments include at least one of the following driving environments: rainy and snowy weather, night driving, and urban congestion environment.

[0104] The construction of various driving environments includes:

[0105] Define parameterized driving environment characteristics, including: road type, weather conditions, lighting conditions, vehicle status, and other traffic factors;

[0106] Generate a simulation environment according to the specific conditions to be tested:

[0107] When simulating the situation of urban roads, divide the road into multiple sections, and the length, driving speed, and number of vehicles ahead of each section are generated by a given parameter generation model;

[0108] Describe the driving environment by a parameter vector P

[0109] P = [p1, p2,..., pn]

[0110] where pi represents the i-th environmental parameter;

[0111] According to the parameter vector P, a corresponding simulation environment is generated; specifically, each environmental parameter is generated by a generation function fi:

[0112] pi = fi(P')

[0113] where P' is a set of input parameters and is a random variable.

[0114] The automatic parsing of the records of the driving recorder includes:

[0115] Using graph neural networks GNNs to analyze the data of the driving recorder:

[0116] Construct a graph model, regarding vehicles and roads as nodes, and regarding the distance and direction between vehicles and roads as edges, thereby constructing a traffic network graph;

[0117] Feature extraction. For each node, a feature vector is defined to represent the state of the node. The state includes the speed and acceleration of the vehicle, and the length, width, and speed limit of the road; the graph neural networks GNNs will automatically perform information propagation and integration, so that each node contains not only its own information but also the information of adjacent nodes; where feature extraction is expressed as a function H, and H(v) represents the feature vector of node v;

[0118] Training of graph neural networks GNNs. According to real traffic data, including traffic flow and traffic accidents, training is carried out to obtain a trained graph model;

[0119] Based on the trained graph model, predict the traffic data and conditions within a future period of time, and compare the predicted traffic data and conditions within a future period of time with the traffic data and conditions of the simulated driving state to obtain record detection data.

[0120] Carry out communication function detection in multiple driving environments to obtain dynamic detection data of wireless communication capabilities, including:

[0121] Define a set of measurement parameters Q = {moving speed V, communication distance D, network bandwidth B, network latency L}, where V, D, B, and L represent moving speed, communication distance, network bandwidth, and network latency respectively;

[0122] Let the data transmission stabilities of the USB, Bluetooth, and wireless public network of the driving recorder be U, B, and W respectively, and measure them through the functions f(Q) = U, g(Q) = B, and h(Q) = W;

[0123] In a simulated mobile environment, set specific mobile speed V1, communication distance D1, network bandwidth B1, and network latency L1, that is, set the parameter set Q1 = {V1, D1, B1, L1}, and then measure the data transmission stability U1, B1, W1 at this time according to f(Q1), g(Q1), h(Q1);

[0124] Then, change the mobile environment conditions, set a new parameter set Q2 = {V2, D1, B1, L1}, where V2 > V1, and then measure U2, B2, W2 at this time;

[0125] According to the above steps, obtain multiple different parameter sets respectively. By measuring the data transmission stability under each group of conditions, obtain a series of data transmission stability and efficiency values: (U1, B1, W1), (U2, B2, W2),..., (Un, Bn, Wn); regard the series of data transmission stability and efficiency values as dynamic detection data of wireless communication capabilities.

[0126] Simulate various driving behaviors in multiple driving environments, and detect the driving behavior recognition and driving safety prompt functions of the driving recorder to obtain behavior-based driving detection data:

[0127] Define the driving behavior description set A = {a1, a2,..., an}, where a1, a2,..., an are parameters related to driving behaviors such as speed, steering angle, braking force, and vehicle relative position; at the same time, define the result set R = {r1, r2,..., rn} of driving behavior recognition, where r1, r2,..., rn represent the recognized driving behavior types, including normal driving, sudden acceleration, and sudden braking;

[0128] Define the driving behavior recognition function of the driving recorder as:

[0129] R = f(A),

[0130] where f is the internal algorithm of the driving recorder. By inputting the driving behavior description set A, output the recognized driving behavior type set R;

[0131] Similarly, define the driving safety prompt function of the driving recorder as a function g. The input is the recognized driving behavior type set R, and the output is the safety prompt set S, that is

[0132] S = g(R);

[0133] In the simulation of various driving behaviors, first generate a series of driving behavior description sets A in the driving environment, including normal driving situations and various abnormal driving behaviors, and then input them into the driving recorder. Obtain the driving behavior recognition result set R through f(A), and further obtain the safety prompt set S through g(R);

[0134] For each set A of driving behavior descriptions, there is a set R' of expected driving behavior recognition results and a set S' of expected safety prompts. By comparing the actual R and S with the expected R' and S', the driving behavior recognition accuracy and driving safety prompt accuracy of the driving recorder are obtained. The driving behavior recognition accuracy and driving safety prompt accuracy of the driving recorder are used as behavior-based driving detection data.

[0135] During accidents simulated in various driving environments, the safety warning response speed and accuracy of the recorder are detected to obtain intelligent safety warning detection data:

[0136] Define the set E of simulated driving environment parameters as E = {e1, e2,..., ei}, where ei represents the i-th driving environment parameter, including: vehicle speed, vehicle distance, and road conditions;

[0137] Define the response speed of the intelligent safety warning as V and the response accuracy as A. These two parameters are calculated through functions f(E) and g(E): V = f(E), A = g(E);

[0138] During the testing process, first set the set E of parameters of the simulated environment to a specific accident-occurring environment. If the vehicle distance is too close, e1 = vehicle distance, then set it to a too-close distance. If the vehicle speed is too fast, e2 = vehicle speed, then set it to a too-fast speed;

[0139] Calculate the response speed V and the response accuracy A, that is, V1 = f(E), A1 = g(E);

[0140] Then change the parameters of the simulated environment, and obtain a series of Vs and As according to the above steps;

[0141] Summarize this series of response speeds and response accuracies, denoted as (V1, A1), (V2, A2),..., (Vn, An); and conduct statistical analysis on the values of V and A, and the results obtained are used as intelligent safety warning detection data.

[0142] Based on big data technology, collect and analyze the recorded detection data, wireless communication ability dynamic detection data, driving detection data, and intelligent safety warning detection data to obtain the final detection result as Figure 2 shown including:

[0143] Define all the data collected during the testing process as the data set D, which contains driving environment parameters, driving recorder parameters, and various performance test results, that is

[0144] D = {E, A, R, S, U, B, W, V1, A1, V2, A2,..., Vn, An};

[0145] Among them, E = {e1, e2,..., en} represents the parameters of various driving environments; A = {a1, a2,..., an} represents the parameters of driving actions; R = {r1, r2,..., rn} represents the recognized types of driving behaviors; S = {s1, s2,..., sn} represents the safety tips of the driving recorder; U, B, and W respectively represent the data transmission stability and efficiency of USB, Bluetooth, and wireless public networks; V1, A1, V2, A2,..., Vn, An respectively represent the safety warning response speed and accuracy of the intelligent warning system in various driving environments;

[0146] Perform data preprocessing through big data technology to remove outliers and invalid data, and perform data cleaning:

[0147] The outlier Z and invalid data I are defined by the following formulas;

[0148] Z = x|x ∈ D, and the value of x is outside the range of its maximum and minimum values,

[0149] I = x|x ∈ D, and the value of x is missing or unreasonable,

[0150] The excluded dataset D' is defined as:

[0151] D' = D - Z - I;

[0152] Obtain the cleaned dataset D',

[0153] D' = {E', A', R', S', U', B', W', V1', A1', V2', A2',..., Vn', An'};

[0154] For the dataset D', use a variety of statistical analysis and machine learning methods for in-depth analysis or use descriptive statistical analysis methods to analyze it, including calculating the mean μ, median θ, and standard deviation σ of each parameter. The formulas are as follows:

[0155]

[0156] (if N is odd); or θ = (xi + xi+1) / 2 (if N is even);

[0157]

[0158] where N is the sample size and xi is the value of the i-th sample;

[0159] Use correlation analysis to describe the correlation between each parameter. Use the Pearson correlation coefficient r for correlation analysis. The calculation method is as follows:

[0160]

[0161] Among them, xi and yi represent the x and y values of the i-th sample, and μx, μy are the average values of x and y. are the standard deviations of x and y;

[0162] For key performance indicators such as recognition accuracy and warning accuracy, the machine learning model SVM is applied for prediction and optimization; the driving behavior recognition accuracy P is provided, and the prediction function p(X) = a*X + b is defined, where X is a set of parameters, and a and b are coefficients to be solved; the parameters of the prediction function are optimized by minimizing the error between the predicted value and the actual value, and the error function E(a, b) is defined as:

[0163] E(a, b) = Σ[p(Xi) - Pi] 2

[0164] Among them, Pi is the true driving behavior recognition accuracy, and p(Xi) is the predicted value;

[0165] The clustering analysis method is used for data mining to find the potential laws of the data; among them, a commonly used index for evaluating the clustering result is the silhouette coefficient S, and the calculation formula is as follows:

[0166] S = (b - a) / max(a, b)

[0167] Among them, a represents the average distance of the sample from the samples in the same category, and b represents the minimum average distance of the sample from the samples in other categories.

[0168] In this embodiment, various driving environments are constructed to comprehensively test the various functions of the driving recorder. The recorded detection data, dynamic detection data of wireless communication capabilities, driving detection data, and intelligent safety warning detection data can comprehensively evaluate the performance of the driving recorder. Secondly, by constructing various driving environments, simulating driving behaviors can more realistically reflect the performance of the driving recorder, and the obtained detection results are also more in line with the actual situation, thereby improving the detection effect.

[0169] Embodiment 3:

[0170] This embodiment provides a driving recorder detection system as Figure 3 shown, including:

[0171] Environment construction module: Construct various driving environments;

[0172] Record detection module: Simulate the driving state in various driving environments to detect the recording data and audio-video recording function of the driving recorder, and obtain the recording data of the driving recorder; automatically analyze the recording data of the driving recorder to obtain the recorded detection data;

[0173] Wireless communication capability dynamic detection module: Detect the communication function in various driving environments to obtain wireless communication capability dynamic detection data;

[0174] Driving detection module: Simulate various driving behaviors in various driving environments to detect the driving behavior recognition and driving safety prompt functions of the driving recorder to obtain behavior-based driving detection data;

[0175] Intelligent safety warning detection module: Simulate the safety warning response speed and accuracy of the recorder when an accident occurs in various driving environments to obtain intelligent safety warning detection data;

[0176] Final detection module: Based on big data technology, collect and analyze the record detection data, wireless communication capability dynamic detection data, driving detection data, and intelligent safety warning detection data to obtain the final detection result.

[0177] In this embodiment, by constructing various driving environments, all functions of the driving recorder are comprehensively tested to obtain record detection data, wireless communication capability dynamic detection data, driving detection data, and intelligent safety warning detection data, which can comprehensively evaluate the performance of the driving recorder. Secondly, by constructing various driving environments and simulating driving behaviors, the performance of the driving recorder can be more realistically reflected, and the obtained detection results are also more in line with the actual situation, thereby improving the detection effect.

[0178] Embodiment 4:

[0179] This embodiment provides a storage medium for detecting a driving recorder, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned detection method of a driving recorder is implemented.

[0180] In this embodiment, by constructing various driving environments, all functions of the driving recorder are comprehensively tested to obtain record detection data, wireless communication capability dynamic detection data, driving detection data, and intelligent safety warning detection data, which can comprehensively evaluate the performance of the driving recorder. Secondly, by constructing various driving environments and simulating driving behaviors, the performance of the driving recorder can be more realistically reflected, and the obtained detection results are also more in line with the actual situation, thereby improving the detection effect.

[0181] Obviously, the above-mentioned embodiments of the present invention are merely examples for clearly illustrating the present invention, rather than limiting the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A detection method for a driving recorder, characterized in that: include: Build multiple driving environments; Simulate driving conditions in various driving environments to detect the recorded data and audio and video recording functions of the driving recorder to obtain the recorded data of the driving recorder; automatically analyze the recorded data of the driving recorder to obtain the recorded detection data; Conduct communication function tests in various driving environments to obtain dynamic test data of wireless communication capabilities; Simulate various driving behaviors in various driving environments, test the driving behavior recognition and driving safety prompt functions of the driving recorder to obtain behavior-based driving test data; Simulate various driving behaviors in various driving environments, test the driving behavior recognition and driving safety prompt functions of the driving recorder, and obtain behavior-based driving test data including: Define a driving behavior description set A = {a1, a2, ..., an}, where a1, a2, ..., an are parameters related to driving behaviors such as speed, steering angle, braking force, and relative position of the vehicle; meanwhile, define a driving behavior recognition result set R = {r1, r2, ..., rn}, where r1, r2, ..., rn represent the recognized driving behavior types, including normal driving, sudden acceleration, and sudden braking; where n is a positive integer variable, indicating that there are n parameters in total; The driving behavior recognition function of the driving recorder is defined as: R = f(A), Where f is the internal algorithm of the driving recorder, which inputs the driving behavior description set A and outputs the identified driving behavior type set R; Similarly, the driving safety reminder function of the driving recorder is defined as a function g, the input is the identified driving behavior type set R, and the output is the safety reminder set S, that is, S = g(R); In simulating various driving behaviors, a series of driving behavior description sets A are first generated in the driving environment, including normal driving situations and various abnormal driving behaviors, and then input into the driving recorder, and the driving behavior recognition result set R is obtained through f(A), and the safety prompt set S is further obtained through g(R); For each driving behavior description set A, there is an expected driving behavior recognition result set R' and an expected safety prompt set S'. The actual R and S are compared with the expected R' and S' to obtain the driving behavior recognition accuracy and driving safety prompt accuracy of the driving recorder; the driving behavior recognition accuracy and driving safety prompt accuracy of the driving recorder are used as behavior-based driving detection data; Test the safety warning response speed and accuracy of the recorder when simulating accidents in various driving environments to obtain intelligent safety warning detection data; Based on big data technology, the final test results are obtained by collecting and analyzing recorded test data, dynamic test data on wireless communication capabilities, driving test data, and intelligent safety warning test data.

2. A detection method for a driving recorder according to claim 1, characterized in that: The multiple driving environments include: at least one driving environment in rainy and snowy weather, driving at night, and a congested city environment.

3. The detection method of a driving recorder according to claim 1, characterized in that: The construction of various driving environments includes: Define parameterized driving environment characteristics, including: road type, weather conditions, lighting conditions, vehicle status and other traffic factors; Generate a simulation environment based on the specific conditions to be tested: When simulating the situation of urban roads, the road is divided into multiple sections, and the length, driving speed, and number of vehicles ahead of each section are generated by the given parameter generation model; The driving environment is described by the parameter vector P P=[p1,p2,...,pn] Wherein, pi represents the i-th environmental parameter; i is a positive integer between 1 and n; According to the parameter vector P, the corresponding simulation environment is generated; specifically, each environment parameter is generated by a generating function fi: pi=fi(P') Among them, P' is a set of input parameters, which are random variables.

4. The detection method of a driving recorder according to claim 3, characterized in that: The automatic analysis of the records of the driving recorder includes: Using graph neural networks (GNNs) to analyze driving recorder data: Build a graph model, consider vehicles and roads as nodes, and the distance and direction between vehicles and roads as edges, so as to construct a traffic network graph; Feature extraction: For each node, a feature vector is defined to express the node status, including the speed and acceleration of the vehicle, the length, width and speed limit of the road; Graph Neural Networks (GNNs) automatically propagate and integrate information, so that each node contains not only its own information, but also the information of adjacent nodes; feature extraction is expressed as a function H, where H(v) represents the feature vector of node v; Graph neural network GNNs training, based on real traffic data, including traffic flow and traffic accidents, to obtain a trained graph model; Based on the trained graph model, the traffic data and conditions in the future are predicted, and the predicted traffic data and conditions in the future are compared with the traffic data and conditions of the simulated driving state to obtain the recorded detection data.

5. The detection method of a driving recorder according to claim 1, characterized in that: The communication function is tested in various driving environments, and the dynamic detection data of wireless communication capability is obtained, including: Define the measurement parameter set Q = {moving speed V, communication distance D, network bandwidth B, network delay L}, where V, D, B, L represent moving speed, communication distance, network bandwidth and network delay respectively; Assume that the data transmission stability of the USB, Bluetooth, and wireless public network of the driving recorder is U, B, and W respectively, and is measured by the functions f(Q)=U, g(Q)=B, and h(Q=W; In the simulated mobile environment, set a specific mobile speed V1, communication distance D1, network bandwidth B1, and network delay L1, that is, set the parameter set Q1 = {V1, D1, B1, L1}, and then measure the data transmission stability U1, B1, W1 at this time according to f(Q1), g(Q1), and h(Q1); Then, change the mobile environment conditions, set a new parameter set Q2 = {V2, D1, B1, L1}, V2> V1, and measure U2, B2, and W2 at this time; According to the above steps, multiple groups of different parameter sets are obtained respectively, and a series of data transmission stability and efficiency values ​​are obtained by measuring the data transmission stability under each group of conditions; the series of data transmission stability and efficiency values ​​are used as dynamic detection data of wireless communication capability.

6. The detection method of a driving recorder according to claim 1, characterized in that: The safety warning reaction speed and accuracy of the recorder are tested in various driving environments when simulating accidents, and intelligent safety warning detection data is obtained: Define a set of simulated driving environment parameters E = {e1, e2, ..., ei}, where ei represents the i-th driving environment parameter, including: vehicle distance, vehicle speed, road condition; i is a positive integer variable; Define the reaction speed of intelligent safety warning as V, and the reaction accuracy as A. These two parameters are calculated by functions f(E) and g(E): V = f(E), A = g(E); During the test, the parameter set E of the simulation environment is first set as a specific accident environment. If the distance between vehicles is too close, e1 = vehicle distance, then it is set as too close distance. If the speed is too fast, e2 = vehicle speed, then it is set as too fast speed. Calculate the reaction speed V and reaction accuracy A, that is, V1 = f(E), A1 = g(E); Then change the parameters of the simulation environment and obtain a series of V and A according to the above steps; This series of reaction speeds and reaction accuracies are summarized and recorded as (V1, A1), (V2, A2), ..., (Vn, An); A1, V2, A2, ..., Vn, An represent the safety warning reaction speed and accuracy of the intelligent warning system under various driving environments respectively; and the values ​​of V and A are statistically analyzed, and the results are used as intelligent safety warning detection data.

7. The detection method of a driving recorder according to claim 1, characterized in that: Based on big data technology, the final test results obtained by collecting and analyzing the recorded test data, wireless communication capability dynamic test data, driving test data and intelligent safety warning test data include: The data collected during all tests are defined as data set D, which includes driving environment parameters, driving recorder parameters, and various performance test results, namely D={E,A,R,S,U,B,W,V1,A1,V2,A2,...,Vn,An}; Wherein, E = {e1, e2, ..., en} represents the parameters of various driving environments; A = {a1, a2, ..., an} represents the parameters of driving actions; R = {r1, r2, ..., rn} represents the type of driving behavior identified; S = {s1, s2, ..., sn} represents the safety prompts of the driving recorder; U, B, W represent the data transmission stability and efficiency of USB, Bluetooth, and wireless public networks respectively; V1, A1, V2, A2, ..., Vn, An represent the safety warning response speed and accuracy of the intelligent warning system in various driving environments respectively; Use big data technology to preprocess data, remove outliers and invalid data, and perform data cleaning: The outliers Z and invalid data I are defined by the following formulas; Z = x|x∈D, and the value of x is outside its maximum and minimum range, I=x|x∈D, and the value of x is missing or unreasonable, The excluded data set D' is defined as: D' = DZI; Get the cleaned data set D', D'={E',A',R',S',U',B',W',V1',A1',V2',A2',...,Vn',An'}; For the data set D', a variety of statistical analysis and machine learning methods are used for in-depth analysis or descriptive statistical analysis is used to analyze it, including calculating the mean μ, median θ and standard deviation σ of each parameter. The formula is as follows: (if N is an odd number); or θ=(xi+xi+1) / 2 (if N is an even number); Where N is the number of samples, xi is the i-th sample value; Correlation analysis is used to describe the correlation between various parameters, and Pearson correlation coefficient r is used for correlation analysis. The calculation method is as follows: Among them, xi and yi represent the x and y values ​​of the i-th sample, μx, μy are the average values ​​of x and y, is the standard deviation of x and y; For recognition accuracy and warning accuracy, the machine learning model SVM is used for prediction and optimization; the driving behavior recognition accuracy P is provided, and the prediction function p(X) = a*X+b is defined, where X is the parameter set, and a and b are the coefficients to be solved; the parameters of the prediction function are optimized by minimizing the error between the predicted value and the actual value, and the error function E(a,b) is defined as: E(a,b)=Σ[p(Xi)-Pi] 2 Among them, Pi is the actual driving behavior recognition accuracy, and p(Xi) is the predicted value; Cluster analysis is used to perform data mining to find the underlying patterns of data. A commonly used indicator for evaluating clustering results is the silhouette coefficient S, which is calculated as follows: S=(ba) / max(a,b) Among them, a represents the average distance between the sample and samples of the same category, and b represents the minimum average distance between the sample and samples of other categories.

8. A driving recorder detection system, characterized in that: include: Environment construction module: build various driving environments; Recording detection module: simulates driving status in various driving environments to detect the recording data and audio and video recording functions of the driving recorder, and obtains the recording data of the driving recorder; automatically analyzes the recording data of the driving recorder to obtain the recording detection data; Wireless communication capability dynamic detection module: performs communication function detection in various driving environments to obtain dynamic detection data of wireless communication capability; Driving detection module: simulates various driving behaviors in various driving environments, detects the driving behavior recognition and driving safety prompt functions of the driving recorder, and obtains behavior-based driving detection data; Simulate various driving behaviors in various driving environments, test the driving behavior recognition and driving safety prompt functions of the driving recorder, and obtain behavior-based driving test data including: Define a driving behavior description set A = {a1, a2, ..., an}, where a1, a2, ..., an are parameters related to driving behaviors such as speed, steering angle, braking force, and relative position of the vehicle; meanwhile, define a driving behavior recognition result set R = {r1, r2, ..., rn}, where r1, r2, ..., rn represent the recognized driving behavior types, including normal driving, sudden acceleration, and sudden braking; where n is a positive integer variable, indicating that there are n parameters in total; The driving behavior recognition function of the driving recorder is defined as: R = f(A), Where f is the internal algorithm of the driving recorder, which inputs the driving behavior description set A and outputs the identified driving behavior type set R; Similarly, the driving safety reminder function of the driving recorder is defined as a function g, the input is the identified driving behavior type set R, and the output is the safety reminder set S, that is, S = g(R); In simulating various driving behaviors, a series of driving behavior description sets A are first generated in the driving environment, including normal driving situations and various abnormal driving behaviors, and then input into the driving recorder, and the driving behavior recognition result set R is obtained through f(A), and the safety prompt set S is further obtained through g(R); For each driving behavior description set A, there is an expected driving behavior recognition result set R' and an expected safety prompt set S'. The actual R and S are compared with the expected R' and S' to obtain the driving behavior recognition accuracy and driving safety prompt accuracy of the driving recorder; the driving behavior recognition accuracy and driving safety prompt accuracy of the driving recorder are used as behavior-based driving detection data; Intelligent safety warning detection module: Test the safety warning response speed and accuracy of the recorder when simulating an accident in a variety of driving environments to obtain intelligent safety warning detection data; Final detection module: Based on big data technology, it collects and analyzes recorded detection data, wireless communication capability dynamic detection data, driving detection data, and intelligent safety warning detection data to obtain the final detection results.

9. A driving recorder detection storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, a detection method for a driving recorder as claimed in any one of claims 1 to 7 is implemented.

Images