Road traffic accident intelligent reproduction method based on automatic Pc-Crash simulation and MLP algorithm

Abstract

The invention belongs to the technical field of road traffic, and particularly relates to a road traffic accident intelligent reproduction method based on automatic Pc-Crash simulation and an MLP algorithm. Comprising the following steps: 1, establishing a simulation model template in Pc-Crash simulation software according to road traffic accident scene information; 2, a simulation test is designed for unconfirmed parameters on the road accident site, and a simulation data set is obtained; 3, constructing, optimizing and training a multi-layer perceptron neural network model; 4, taking the stopping position of an accident participant of the road traffic accident or the tire friction trace deviation minimization as a core, constructing a target function, predicting an accident result by adopting an MLP neural network model, and obtaining an optimal solution of an unconfirmed parameter by using a multi-starting-point search global optimization algorithm; a simulation result is compared with actual exploration data, and the accuracy of the optimal solution is verified; intelligent reproduction of the whole process of a road traffic accident is realized, and important technical support is provided for responsibility determination and compensation processing during traffic accident processing.

Description

Technical Field

[0001] The present invention belongs to the technical field of road traffic, and particularly relates to an intelligent road traffic accident reconstruction method based on automated Pc-Crash simulation and MLP algorithm. Background Art

[0002] Analysis of road traffic accidents is one of the key tasks of public security traffic management departments. Its purpose is to reconstruct the accident scene as detailed as possible and analyze the accident causes, so as to provide a scientific basis for accident liability determination and prevention of similar accidents. Traditional accident analysis methods mainly rely on on-site investigation and manual analysis, which are not only time-consuming but also limited in accuracy. As a professional traffic accident simulation analysis tool, the reliability of PC-Crash in accident reconstruction has been fully verified, and its application in road traffic accident reconstruction analysis has gradually increased in recent years.

[0003] Artur Ziola used Pc-Crash to reconstruct road traffic accidents and compared the visualized 3D simulation with the actual collision video records, fully demonstrating the reliability of the PC-Crash computer simulation software in vehicle collision simulation. The team of Jiang Gongliang from Chongqing Jiaotong University established a simulation model of the road traffic accident process for vehicle frontal collision and rear-end collision based on the Pc-Crash software. The teams of Zhang Yonggang and Huang Haibo established a simulation model of the road traffic accident process for vehicle-pedestrian collision based on the Pc-Crash software. Qin Chuang, Kong Lingshuang, and Tong Xiaobo respectively established a simulation model of the road traffic accident process for two-wheeler collision based on the Pc-Crash software. The above-mentioned existing technologies all compared the simulation results with the data of driving recorders, video surveillance, or event data recorders (EDRs), and the comparison results proved the reliability of PC-Crash in vehicle collision simulation.

[0004] However, when using the PC-Crash software for road traffic accident simulation, only forward simulation can be performed, that is, only by setting various parameters such as vehicle speed, collision angle, collision position, types of vehicles involved in the accident, load, road surface gradient, and friction coefficient, the tire friction marks, the stopping positions of accident participants, and the energy consumed in the collision after the accident can be obtained through simulation. This current situation is contrary to the needs of public security traffic management and traffic forensic identification, which more require obtaining data such as tire friction marks and the stopping positions of accident participants that can be obtained from the on-site investigation of traffic accidents, and inversely obtaining data such as the initial speed and angular velocity of accident participants before the accident. Currently, existing intelligent traffic accident reconstruction methods based on Pc-Crash all rely on manually adjusting parameters such as the initial relative positions, speeds, postures, and friction coefficients of accident participants in the model multiple times. By comparing the output results of the adjusted model with the on-site investigation data, the initial speed, angular velocity, etc. of accident participants that are optimally matched are obtained through repeated trial and error. On the one hand, this process consumes a large amount of work of accident investigators and forensic identification personnel, seriously affecting the work efficiency of accident reconstruction analysis and even the public security traffic management department. On the other hand, this process highly relies on the historical experience of software operators, posing extremely high requirements for the talent reserve of relevant departments. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent road traffic accident reconstruction method based on automated Pc-Crash simulation and MLP algorithm. This method realizes batch execution of collision simulations by constructing an automated control module of Pc-Crash based on the operating system event injection mechanism; at the same time, it innovatively establishes a multi-layer perceptron (MLP) neural network proxy model, combines intelligent optimization algorithms to achieve rapid convergence of the accident parameter space, thereby realizing the intelligent reconstruction of the entire process of road traffic accidents, providing an important technical means for the public security department to conduct more scientific and fair liability determination and compensation handling in traffic accident handling.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions:

[0007] An intelligent road traffic accident reconstruction method based on automated Pc-Crash simulation and MLP algorithm, comprising the following steps:

[0008] Step 1: Establish a simulation model template in the Pc-Crash simulation software according to the on-site information of the road traffic accident;

[0009] Step 2: Reasonably design simulation experiments within the parameter space for parameters that cannot be confirmed at the road accident site, and obtain a simulation data set through automated simulation by the automated simulation module according to the designed simulation experiments;

[0010] Step 3: Construct a multi-layer perceptron neural network model, optimize the multi-layer perceptron neural network model, and use the simulation dataset in Step 2 to train the optimized multi-layer perceptron neural network model to obtain the final multi-layer perceptron neural network model;

[0011] Step 4: With the minimization of the deviation of the stopping position of the accident participants or the tire friction marks in the road traffic accident as the core, construct an objective function, use the final multi-layer perceptron neural network model in Step 3 to predict the accident result, and use a global optimization algorithm with multi-start search to obtain the optimal solution of the parameters that cannot be confirmed; Use the optimal solution of the parameters that cannot be confirmed to conduct simulations in the PC-Crash simulation software, compare the simulation results with the actual survey data, and verify the accuracy of the optimal solution.

[0012] Further, establishing a simulation model template in the Pc-Crash simulation software according to the road traffic accident scene information in Step 1 means:

[0013] Determine the road conditions at the accident location based on the general view of the accident scene taken at the road traffic accident scene and the subsequent on-site surveying or electronic map, and establish a simulation background map or road model in the Pc-Crash simulation software;

[0014] Obtain the accident-involved vehicle information based on the road traffic accident scene investigation, and establish an accurate and reliable vehicle model in the Pc-Crash simulation software;

[0015] Analyze the braking marks according to the principles of traceology to obtain the mutation points of the braking mark trajectory, and then obtain the geographical location where the collision occurred; Analyze the vehicle collision relative angle by analyzing the shape of the braking marks and the vehicle deformation characteristics; Establish a suitable relative position of the vehicles in the Pc-Crash simulation software according to the geographical location where the collision occurred and the vehicle collision relative angle;

[0016] Based on video surveillance, driving recorders, and EDR devices, analyze the vehicle speeds and avoidance angular velocities of the accident-involved vehicles, and establish a vehicle motion model in the Pc-Crash simulation software according to the vehicle speeds and avoidance angular velocities.

[0017] Further, the process of the simulation experiment design in Step 2 is as follows:

[0018] For the parameters that cannot be determined at the road accident scene, use the values given at the accident scene as reference values, and set data intervals according to the reference values;

[0019] For the high-dimensional parameter space, use Latin hypercube sampling, and for the low-dimensional parameter space, use a full factorial experimental design to design experimental points; Among them, high-dimensional parameters refer to data with a dimension greater than 5, and low-dimensional data refer to data with a dimension less than or equal to 5;

[0020] According to the designed test points, the accident process under various parameters is simulated through an automated simulation module, and the tire friction marks and the stopping positions of accident participants are extracted, so as to form a simulation data set of the attributes and behaviors of accident participants and the characteristics of the vehicle and marks after the accident.

[0021] Furthermore, the automated simulation module refers to being embedded in the Pc-Crash simulation software to achieve automated simulation and output simulation results;

[0022] The implementation of the automated simulation module:

[0023] (1) Based on the MATLAB-Java cross-platform interface, the Java AWT Robot class API is called to simulate mouse and keyboard operations; simulating mouse input to achieve click operations on the Pc-Crash simulation software, and simulating keyboard input to achieve the input of specific parameters in the Pc-Crash simulation software; furthermore, achieving the save-as of the accident process simulation model template and the accident speed parameter adjustment process;

[0024] (2) Based on the MATLAB-Java cross-platform interface, the Java AWT Robot class API and the MATLAB screen capture and optical character recognition (OCR) functions are called to simulate click operations to achieve the simulation run of the adjusted model, and the process status is monitored by recognizing the progress bar value of the image;

[0025] (3) When it is recognized that the simulation process reaches its maximum simulation time, the simulation terminates, and the simulation result file is output by simulating mouse and keyboard operations;

[0026] (4) The output result is obtained by analyzing and reading the simulation result file through Matlab.

[0027] Furthermore, the multi-layer perceptron neural network model includes an input layer, a hidden layer, and an output layer; the ReLU activation function is used in the hidden layer, and the linear activation function is used in the output layer, and the network depth and width are dynamically adjusted according to the parameter space dimension.

[0028] Furthermore, optimizing the multi-layer perceptron neural network model means: using the Bayesian optimization algorithm to optimize the hyperparameters of the multi-layer perceptron neural network model; constructing the mapping relationship between the hyperparameters and the model performance through the Gaussian process model, aiming to minimize the mean square error of the validation set, and iteratively searching for the optimal hyperparameter combination; evaluating the generalization ability of the multi-layer perceptron neural network model through K-fold cross-validation.

[0029] Furthermore, in step 4, it is verified that the optimal solution is not the optimal solution by comparing the simulation results with the actual survey data, and the multi-layer perceptron neural network model is further trained by adding simulation test points in step 2 to improve the reliability of the multi-layer perceptron neural network model.

[0030] The beneficial effects of the present invention are as follows:

[0031] (1) The automated simulation module can replace accident investigators and forensic experts to perform software operations, forming an automated simulation system for vehicle collision processes based on the Pc-Crash simulation software, thereby reducing the workload of accident investigators and forensic experts and improving the work efficiency of accident reconstruction analysis and even the public security traffic management department.

[0032] (2) The multi-layer perceptron (MLP) neural network model realizes the intelligent optimization of model parameters and the intelligent reconstruction of the entire process of road traffic accidents, providing an important technical means for the public security department to more scientifically and fairly conduct liability determination and compensation handling in traffic accident handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flowchart of the present invention.

[0034] Figure 2 It is a diagram of the accident process in an embodiment of the present invention.

[0035] Figure 3 It is a surface diagram of the surrogate model in an embodiment of the present invention.

[0036] Figure 4 It is a regression diagnosis diagram of the surrogate model in an embodiment of the present invention.

[0037] Figure 5 It is a comparison diagram of the optimized model simulation and the vehicle stop position at the accident scene of the present invention.

[0038] Figure 6 It is a comparison diagram of the given parameter simulation and the vehicle stop position at the accident scene of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] A method for intelligent reconstruction of road traffic accidents based on automated Pc-Crash simulation and MLP algorithm provided in this embodiment depends on the Pc-Crash simulation software to implement. Before describing the method of this embodiment, an automated simulation module is embedded in the Pc-Crash simulation software, and automated simulation is realized through the automated simulation module and the simulation results are output, reducing the labor intensity of the staff and improving the work efficiency of accident analysis.

[0040] Implementation of the automated simulation module:

[0041] (1) Based on the MATLAB-Java cross-platform interface, the Java AWT Robot class API is called to simulate mouse and keyboard operations. The mouse input is simulated through the mouseMove, mousePress, and mouseRelease functions to perform click operations on the Pc-Crash simulation software, and the keyboard input is simulated through the keyPress and keyRelease functions to input specific parameters in the Pc-Crash simulation software. Furthermore, the save-as operation of the accident process simulation model template and the accident speed parameter adjustment process are realized.

[0042] (2) Based on the MATLAB-Java cross-platform interface, the Java AWT Robot class API and the MATLAB screen capture and optical character recognition (OCR) functions are called to simulate click operations, realize the simulation operation of the adjusted model, and monitor its process status through the progress bar value of image recognition.

[0043] (3) When it is recognized that the simulation process reaches its maximum simulation time, the simulation terminates, and the output of the simulation result file is realized through simulating mouse and keyboard operations.

[0044] (4) The output result is obtained by analyzing and reading the simulation result file through Matlab; the output result includes but is not limited to the stop position of accident participants, the speed, acceleration, and displacement history of accident participants.

[0045] The automated simulation module of this embodiment can replace accident investigators and forensic experts to perform software operations, forming an automated simulation system for vehicle collision processes based on the Pc-Crash simulation software, thereby reducing the workload of accident investigators and forensic experts and improving the work efficiency of accident reconstruction analysis and even the public security traffic management department.

[0046] Taking the road traffic accident of "a car hitting a trailer head-on at an intersection" as an example, as Figure 2 shown, the intelligent reconstruction method of road traffic accidents based on automated Pc-Crash simulation and MLP algorithm described in this embodiment is introduced in detail. As Figure 1 shown, the method includes the following steps:

[0047] Step 1: Establish a simulation model template in the Pc-Crash simulation software according to the road traffic accident scene information.

[0048] Determine the road conditions at the accident site based on the overview map of the accident scene taken at the road traffic accident site and the subsequent on-site surveying and mapping or electronic map, and establish a simulation background map or road model in the Pc-Crash simulation software. In this embodiment, the road conditions at the accident site were determined based on the overview map of the accident scene of "a frontal collision between a car and a semi-trailer at an intersection" taken by a drone; the taken overview map was used as the background map for the accident process simulation, which facilitated the subsequent comparison of the simulation results with the accident scene.

[0049] Obtain the information of the accident-involved vehicles through on-site investigation of the road traffic accident, and establish an accurate and reliable vehicle model in the Pc-Crash simulation software; the vehicle information includes but is not limited to dimensions, shape, wheelbase, tire parameters, and total weight. In this embodiment, the car model is the 2015 Chevrolet Spark M300, the tractor is the 1994 Peterbilt 397, and the trailer is the 2017 Travis S / 96 rear-dump body semi-trailer. The total weight of the semi-trailer body and the load is 37 tons; the vehicle information was obtained from the website according to the vehicle model, and then the vehicle model was established.

[0050] Analyze the braking marks (morphology, direction, length, etc.) according to the principles of traceology to obtain the mutation points of the braking mark trajectory, and then obtain the geographical location where the collision occurred; analyze the morphology of the braking marks and the deformation characteristics of the vehicle to obtain the relative collision angle of the vehicle; establish a suitable relative position of the vehicle in the Pc-Crash simulation software according to the geographical location where the collision occurred and the relative collision angle of the vehicle.

[0051] If the braking marks are not obvious or the vehicle deformation characteristics are complex, determine the geographical location where the collision occurred and the relative collision angle of the vehicle in combination with the statements of the accident-involved personnel, eyewitnesses, or other debris characteristics.

[0052] In the accident scene investigation of this embodiment, the mutation points of the braking marks were analyzed according to the principles of traceology to determine the geographical location where the collision occurred; the relative collision angle of the vehicle was determined based on the morphology of the braking marks and the vehicle deformation characteristics; the relative position of the vehicle in the Pc-Crash simulation software was set according to the obtained parameters, such as Figure 2 The figure shows the simulation model with the relative position set in the Pc-Crash simulation software, and the background picture in the simulation model is the overview map of the accident scene collected by the drone in the embodiment.

[0053] Based on detection devices such as video surveillance, driving recorders, and EDR, analyze the vehicle speeds and avoidance angular velocities of the accident-involved vehicles, and establish a vehicle motion model in the Pc-Crash simulation software according to the vehicle speeds and avoidance angular velocities.

[0054] If the above-mentioned device does not exist or is damaged, a rough vehicle speed and avoidance angular velocity are given by combining the transcripts of accident participants, eyewitness transcripts, the length of tire friction marks, etc.

[0055] In the accident scene of this embodiment, there is no video surveillance device. The tow truck is not equipped with a driving recorder and an EDR device. The car is equipped with an EDR device, and the vehicle speed of the car at the time of the accident is recorded. The vehicle speed of the car is the data recorded by the EDR, that is, the initial speed of the car is 33.8 km / h, and the angular velocity is 0 rad / s. Considering that both the driver and the passengers of the car suffered fatal injuries and died, the initial speed of the tow truck in the accident process simulation model can only be initially set to 83.6 km / h according to the transcript of the tow truck driver, and the angular velocity is set to 0.2 rad / s.

[0056] Step 2: Reasonably design simulation experiments within the parameter space for the parameters that cannot be confirmed at the road accident scene, and obtain a simulation data set through automated simulation by the automated simulation module according to the designed simulation experiments;

[0057] Due to various reasons such as incomplete or damaged detection equipment, it is difficult to determine multiple parameters such as the vehicle speed and avoidance angular velocity of accident participants before a large number of traffic accidents occur. For the above-mentioned difficult-to-determine parameters, it is necessary to reasonably design simulation experiments within the parameter space in order to find the global optimal matching parameter values subsequently.

[0058] The process of designing the simulation experiment is as follows:

[0059] For the parameters that cannot be determined at the road accident scene, use the values given at the accident scene in Step 1 as reference values, and set data intervals according to the reference values;

[0060] For high-dimensional parameter spaces (dimensions greater than 5 dimensions), use Latin hypercube sampling, and for low-dimensional parameter spaces (≤5 dimensions), use full factorial experimental design to design test points;

[0061] According to the designed test points, simulate the accident process for each parameter through the automated simulation module, and extract result data such as tire friction marks and the stopping positions of accident participants, so as to form a simulation data set of accident participant attributes, behaviors, and vehicle and trace characteristics after the accident.

[0062] In this embodiment, the speed and angular velocity of the trailer are parameters that cannot be determined; taking 83.6 km / h and 0.2 rad / s in Step 1 as reference values, the initial speed range of the trailer is set to 60 to 120 km / h, and the angular velocity range is set to -0.9 to 0.9 rad / s. The full factorial experimental design is used to set the test points, with each parameter taking 13 levels, so a total of 169 groups of simulation test points are determined; the accident process under each parameter is simulated through the automated simulation module, and the result data of the stop positions of the accident participants are extracted, thus forming a simulation data set of the trailer speed, angular velocity, and the vehicle stop position after the accident.

[0063] Step 3: Construct a multi-layer perceptron (MLP) neural network model, optimize the MLP neural network model, and use the simulation data set in Step 2 to train the optimized MLP neural network model to obtain the final MLP neural network model.

[0064] The multi-layer perceptron (MLP) neural network model includes an input layer, a hidden layer, and an output layer; the ReLU activation function is used in the hidden layer to avoid the problem of gradient disappearance, and the linear activation function is used in the output layer to adapt to continuous response variables. The depth and width of the network are dynamically adjusted according to the parameter space dimension, initially set to 1 hidden layer, and the number of neurons in each layer is 4 times the number of input layer variables. The input variables of the input layer are parameters that cannot be confirmed, such as the initial linear speed of collision, angular velocity, and vehicle mass distribution; the output response of the output layer is result data such as tire friction marks, the stop positions of accident participants, and vehicle termination states. The input layer variables in this embodiment are from the simulation data set generated in Step 2, and the output layer response is the simulation result of the PC-Crash simulation software.

[0065] Optimizing the MLP neural network model specifically means: using the Bayesian optimization algorithm to optimize the MLP network hyperparameters (such as the number of hidden layers, the number of neurons, etc.); constructing the mapping relationship between the hyperparameters and the model performance through the Gaussian process model, aiming to minimize the mean square error (MSE) of the validation set, and iteratively searching for the optimal hyperparameter combination. The generalization ability of the surrogate model is evaluated through K-fold cross-validation; the simulation data set is randomly divided into K subsets, and one of the subsets is used as the validation set in turn, and the remaining subsets are used as the training set, and the average performance indicators (such as R 2 , MSE) in the K validations are calculated.

[0066] The MLP neural network model is trained using the simulation data set to form a high-precision surrogate model that can replace physical simulation; this model can predict accident results with a response time of milliseconds, significantly reducing the computational cost and providing efficient support for subsequent parameter optimization.

[0067] The optimized MLP neural network model in this embodiment contains 1 hidden layer with 12 neurons. The MLP neural network model is trained using a simulation dataset to obtain the final MLP neural network model, and its surface plot is as shown in Figure 3 , and the regression diagnostic plot is as shown in Figure 4 .

[0068] Step 4: Taking the minimization of the deviation of the stopping position of the accident participants or the tire friction marks in a road traffic accident as the core, construct an objective function, use the final MLP neural network model in Step 3 to predict the accident result, and obtain the optimal solution of the undetermined parameters using a global optimization algorithm with multi-start search; use the optimal solution of the undetermined parameters to perform simulations in the PC-Crash simulation software, compare the simulation results with the actual survey data, and verify the accuracy of the optimal solution.

[0069] When the optimal solution is accurate enough, the undetermined parameters of this accident case are optimally matched. If there is a large error between the simulation result of the optimal solution and the actual survey result, it indicates that the final MLP neural network model in Step 3 needs to be further optimized, and the MLP neural network model is further optimized by adding simulation test points in Step 2.

[0070] This embodiment takes the minimization of the absolute deviation between the simulation result and the stopping position of the accident participants obtained from the on-site survey as the objective function; with the minimization of this objective function as the optimization goal and the parameter space of the trailer speed and angular velocity as the boundary constraints, the optimal speed and angular velocity are obtained as 91.5 km / h and 0.16 rad / s respectively.

[0071] Feed the optimal solution back to the PC-Crash simulation software for verification simulation; as shown in Figure 5 is the comparison diagram of the simulation of the optimized model and the stopping position of the vehicle at the accident scene. The average relative position error between the simulation and the survey results of the stopping positions of the car, trailer, and semi-trailer is less than 5%, which proves the reliability of this method.

[0072] Figure 6 is the comparison diagram of the stopping positions of the vehicles at the accident scene simulated with the speed and angular velocity given in Step 1; comparing Figure 5 and Figure 6 , it can be seen that this method can reduce the deviation of the stopping position of the accident participants obtained from the simulation and the survey without manual intervention, proving that this method realizes the intelligent reproduction of road traffic accidents.

[0073] The above is only the preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any modification and replacement based on the technical solutions and inventive concepts provided by the present invention should be covered within the protection scope of the present invention.

Claims

1. A road traffic accident intelligent reconstruction method based on automated Pc-Crash simulation and MLP algorithm, characterized in that: The steps include: Step 1: Establish a simulation model template in the Pc-Crash simulation software based on the road traffic accident scene information; Step 2: Rationally design simulation tests in the parameter space for parameters that cannot be confirmed at the road accident scene, and perform automated simulation through the automated simulation module to obtain simulation data sets based on the designed simulation tests; Step 3: construct a multi-layer perceptron neural network model, optimize the multi-layer perceptron neural network model, and use the simulation data set of step 2 to train the optimized multi-layer perceptron neural network model to obtain the final multi-layer perceptron neural network model; Step 4: With the minimization of the deviation of the stopping position or tire friction mark of the accident participants in the road traffic accident as the core, construct the objective function, use the final multi-layer perceptron neural network model in step 3 to predict the accident result, and use the global optimization algorithm of multi-starting point search to obtain the optimal solution of the parameters that cannot be confirmed; use the optimal solution of the parameters that cannot be confirmed to simulate in the PC-Crash simulation software, compare the simulation results with the actual survey data, and verify the accuracy of the optimal solution.

2. The method for intelligently reproducing road traffic accidents based on automated Pc-Crash simulation and MLP algorithm according to claim 1 is characterized in that: In step 1, establishing a simulation model template in the Pc-Crash simulation software based on the road traffic accident scene information means: Determine the road conditions at the accident site based on the accident site overview map taken at the road traffic accident site and the subsequent on-site mapping or electronic map, and establish a simulation background map or road model in the Pc-Crash simulation software; Obtain information about vehicles involved in the accident based on on-site investigation of the road traffic accident, and establish an accurate and reliable vehicle model in the Pc-Crash simulation software; According to the principle of traceology, the brake traces are analyzed to obtain the mutation point of the brake trace trajectory, and then the geographical location of the collision is obtained; the relative angle of vehicle collision is obtained by analyzing the brake trace morphology and vehicle deformation characteristics; according to the geographical location of the collision and the relative angle of vehicle collision, the appropriate relative position of the vehicle is established in the Pc-Crash simulation software; Based on video surveillance, driving recorders, and EDR equipment, the speed and avoidance angular velocity of the vehicles involved in the accident are analyzed, and the vehicle motion model is established in the Pc-Crash simulation software based on the vehicle speed and avoidance angular velocity.

3. The intelligent reconstruction method of road traffic accidents based on automated Pc-Crash simulation and MLP algorithm according to claim 1 is characterized in that: The simulation experiment design process described in step 2 is as follows: If the parameters cannot be determined at the road accident scene, the values ​​given at the accident scene are used as reference values, and the data interval is set according to the reference values; Latin hypercube sampling is used for high-dimensional parameter space, and full factorial experimental design is used for low-dimensional parameter space to design experimental points; high-dimensional parameters refer to data with a dimension greater than 5, and low-dimensional data refer to data with a dimension less than or equal to 5; According to the designed test points, the accident process under various parameters is simulated through the automated simulation module, and the tire friction marks and the stopping positions of the accident participants are extracted, thereby forming a simulation data set of accident participant attributes, behaviors, and post-accident vehicle and trace characteristics.

4. The method for intelligently reproducing road traffic accidents based on automated Pc-Crash simulation and MLP algorithm according to claim 3 is characterized in that: The automated simulation module is embedded in the Pc-Crash simulation software to realize automated simulation and output simulation results; Implementation of the automation simulation module: (1) Based on the MATLAB-Java cross-platform interface, the Java AWT Robot class API is called to simulate mouse and keyboard operations; the mouse input is simulated to realize the point-selection operation of the Pc-Crash simulation software, and the keyboard input is simulated to realize the input of specific parameters in the Pc-Crash simulation software; and then the accident process simulation model template is saved as and the accident speed parameter adjustment process is realized; (2) Based on the MATLAB-Java cross-platform interface, the Java AWT Robot class API and the MATLAB screen capture and optical character recognition (OCR) functions are called to simulate the point-selection operation, realize the simulation operation of the adjusted model, and monitor its process status through the image recognition progress bar value; (3) When it is recognized that the simulation process has reached its maximum simulation time, the simulation is terminated, and the simulation result file is output by simulating mouse and keyboard operations; (4) The simulation result file is read through Matlab analysis to obtain the output result.

5. The intelligent reconstruction method of road traffic accidents based on automated Pc-Crash simulation and MLP algorithm according to claim 1 is characterized in that: The multi-layer perceptron neural network model includes an input layer, a hidden layer and an output layer; the hidden layer adopts a ReLU activation function, the output layer adopts a linear activation function, and the network depth and width are dynamically adjusted according to the parameter space dimension.

6. The method for intelligently reproducing road traffic accidents based on automated Pc-Crash simulation and MLP algorithm according to claim 5 is characterized in that: Optimizing the multilayer perceptron neural network model means: using the Bayesian optimization algorithm to tune the hyperparameters of the multilayer perceptron neural network model; constructing the mapping relationship between hyperparameters and model performance through the Gaussian process model, iteratively searching for the optimal hyperparameter combination with the goal of minimizing the mean square error of the validation set; and evaluating the generalization ability of the multilayer perceptron neural network model through K-fold cross validation.

7. The method for intelligently reproducing road traffic accidents based on automated Pc-Crash simulation and MLP algorithm according to claim 1 is characterized in that: In step 4, the simulation results are compared with the actual exploration data to verify that the optimal solution is not the optimal solution. By adding simulation test points in step 2, the multilayer perceptron neural network model is further trained to improve the reliability of the multilayer perceptron neural network model.

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