Collision accident rapid reconstruction platform and reconstruction method

Abstract

The invention relates to a collision accident rapid reconstruction platform and reconstruction method, and the platform comprises an accident information collection module which is used for collecting to-be-reconstructed real traffic accident information; the multi-rigid-body modeling module is used for performing multi-rigid-body modeling on a first collision vehicle, a second collision vehicle and traffic participants in the accident in MADYMO software; the simulation analysis module is used for performing simulation calculation on the simulation matrix generated by the optimization algorithm module; the parameter conversion module is used for converting parameters extracted from the traffic accident video into modeling parameters which can be identified in the simulation model; and the optimization algorithm module is used for determining a multi-target optimization algorithm, a design variable, a constraint condition and a reconstruction target. According to the method, the collision accident reconstruction efficiency is remarkably improved, the objectivity and accuracy of a reconstruction result are ensured, and the method is suitable for reconstruction of different complex collision scenes.

Description

Technical Field

[0001] The invention belongs to the technical field of road safety and relates to a collision accident rapid reconstruction platform and a reconstruction method. Background Art

[0002] The current mainstream method of road safety analysis is to study the injuries of people in traffic accidents based on simulation. However, the traditional trial-and-error method of reconstructing collision accident scenes is highly dependent on the experience and intuition of accident analysts, and it is difficult to guarantee the objectivity of the results. In addition, there are many factors that affect traffic participants in traffic accidents. If the traditional trial-and-error method is used, it will be inefficient and the accuracy of the reconstruction results will be difficult to guarantee. Summary of the invention

[0003] The purpose of the present invention is to provide a collision accident rapid reconstruction platform and reconstruction method, which realizes rapid, automatic and accurate reconstruction of traffic accident scenes by integrating system modules.

[0004] According to the purpose of the present invention, the present invention provides a collision accident rapid reconstruction platform, comprising:

[0005] An accident information collection module, used to collect real traffic accident information to be reconstructed;

[0006] Multi-rigid body modeling module, used to perform multi-rigid body modeling of the first collision vehicle, the second collision vehicle, and traffic participants in the accident in MADYMO software;

[0007] A simulation analysis module is used to perform simulation calculations on the simulation matrix generated by the optimization algorithm module;

[0008] A parameter conversion module is used to convert the parameters extracted from the traffic accident video into modeling parameters that can be identified in the simulation model;

[0009] The optimization algorithm module is used to determine the multi-objective optimization algorithm, design variables, constraints, and reconstruction objectives.

[0010] Furthermore, the traffic accident information is video surveillance of the location where the collision accident occurred. Using the surveillance video, the collision speed of the two colliding vehicles is first calculated based on the frame difference method, and then the relative positions of the first collision vehicle and the second collision vehicle before the collision, as well as the relative positions of the two vehicles after the collision are extracted; finally, the injury position and injury level of the traffic participants in the accident are extracted.

[0011] Furthermore, if the first collision vehicle and the second collision vehicle are motor vehicles, the modeling parameters are ground clearance, hood front edge height, bumper upper end depth, hood length, hood angle, and windshield angle; if they are two-wheeled vehicles, the modeling parameters are pedal height, handlebar height, and seat height; and the modeling parameters of traffic participants are height, arm length, leg length, arm bending angle, and leg bending angle.

[0012] Furthermore, the multi-objective optimization algorithm includes a multi-objective genetic algorithm or a particle swarm optimization algorithm, and the design variables include modeling parameters of the first collision vehicle, the second collision vehicle, and the traffic participants in the multi-rigid body modeling module, including ground clearance, hood front edge height, bumper upper end depth, hood length, hood angle, windshield angle, pedal height, handle height, seat cushion height, body height, arm length, leg length, arm bending angle or leg bending angle; wherein the constraints are the lateral movement direction of the first collision vehicle after the collision, the lateral movement direction of the second collision vehicle after the collision, and the collision area between the traffic participant and the vehicle; wherein the reconstruction target is the relative position of the two vehicles after the collision, the damage position and damage level of the traffic participant.

[0013] Furthermore, the simulation analysis module includes an ISIGHT multi-objective optimization calling program and a MADYMO solver, wherein the ISIGHT multi-objective optimization calling program is used to call the MADYMO solver for solving based on the simulation matrix.

[0014] According to another object of the present invention, the present invention provides a method for rapid reconstruction of a collision accident, comprising the following steps:

[0015] S1, establishing a multi-rigid body simulation basic model including a first collision vehicle, a second collision vehicle, and traffic participants in a multi-rigid body modeling module;

[0016] S2, based on the accident information collection module, obtains the traffic accident video surveillance to be reconstructed, calculates the collision speed between the first collision vehicle and the second collision vehicle, the relative position of the first collision vehicle and the second collision vehicle before the collision, the relative position of the two vehicles after the collision, the lateral movement direction of the first collision vehicle after the collision, the lateral movement direction of the second collision vehicle after the collision, the collision area between the traffic participant and the vehicle, and the injury position and injury level of the traffic participant in the accident;

[0017] S3, based on the optimization algorithm module, determines the multi-objective optimization algorithm, design variables, constraints, and reconstruction objectives;

[0018] S4, based on the determined multi-objective optimization algorithm, DOE design is performed on the design variables to obtain a simulation matrix;

[0019] S5, based on the parameter conversion module, converting the parameters extracted from the traffic accident video into modeling parameters that can be recognized in the simulation model;

[0020] S6, inputting the simulation matrix into the simulation analysis module, calling the MADYMO solver based on the ISIGHT multi-objective optimization calling program to solve the simulation matrix, and obtaining the simulation target value corresponding to the simulation matrix;

[0021] S7, determining whether the simulation calculation has reached the maximum number of iterations, if so, extracting the simulation matrix and the corresponding simulation target value, if not, repeating step 6;

[0022] S8, comparing all the obtained simulation target values ​​with the reconstruction target obtained in step 3 in turn, and determining whether the error is less than 5%. If so, outputting the design variables corresponding to the simulation target values ​​that meet the requirements; if so, repeating steps 3-7;

[0023] S9, inputting the design variables corresponding to the simulation target values ​​that meet the requirements into the multi-rigid body modeling module to establish a collision accident reconstruction model that meets the requirements.

[0024] Beneficial effects of the present invention:

[0025] (1) The rapid reconstruction platform for collision accidents proposed in the present invention solves the problem that traditional trial-and-error-based methods are difficult to integrate multiple reconstruction objectives and carry out reconstruction efficiently and accurately.

[0026] (2) The collision accident rapid reconstruction platform proposed in the present invention realizes the whole process of multi-rigid body model from parameter selection, simulation calculation, result extraction to target optimization, and is carried out uninterruptedly and automatically.

[0027] (3) The collision accident rapid reconstruction platform proposed in the present invention is based on automated simulation and multi-objective optimization theory, which significantly improves the efficiency of collision accident reconstruction, ensures the objectivity and accuracy of the reconstruction results, and is suitable for the reconstruction of different complex collision scenes. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The following is a flowchart of an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The specific embodiments of the present invention are further described below. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in each embodiment of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0030] Example 1

[0031] A rapid reconstruction platform for a collision accident includes an accident information collection module, a multi-rigid body modeling module, a simulation analysis module, a parameter conversion module and an optimization algorithm module, wherein the accident information collection module is used to collect real traffic accident information to be reconstructed. The traffic accident information is video surveillance of the place where the collision accident occurred. Using the surveillance video, the collision speed of the two vehicles (the first collision vehicle and the second collision vehicle) that collided is first calculated based on the "frame difference method". Then, the relative position of the first collision vehicle and the second collision vehicle before the collision, as well as the relative position of the two vehicles after the collision, are extracted. Finally, the damage position and damage level of the traffic participants in the accident are extracted.

[0032] The multi-rigid body modeling module is used to perform multi-rigid body modeling of the first collision vehicle, the second collision vehicle, and the traffic participants in the accident in the MADYMO software. Among them, if the first collision vehicle and the second collision vehicle are motor vehicles, the modeling parameters are ground clearance, hood front edge height, bumper top depth, hood length, hood angle, and windshield angle. If it is a two-wheeled vehicle, the modeling parameters are pedal height, handle height, and seat height. The modeling parameters of traffic participants are height, arm length, leg length, arm bending angle, and leg bending angle.

[0033] The parameter conversion module is used to convert the parameters extracted from the traffic accident video into modeling parameters that can be recognized in the simulation model.

[0034] The optimization algorithm module is used to determine the multi-objective optimization algorithm, design variables, constraints, and reconstruction targets. The multi-objective optimization algorithm includes but is not limited to the multi-objective genetic algorithm, particle swarm optimization algorithm, etc. The design variables include the modeling parameters of the first collision vehicle, the second collision vehicle, and the traffic participants in the multi-rigid body modeling module, including ground clearance, hood front edge height, bumper upper end depth, hood length, hood angle, windshield angle, pedal height, handle height, seat height, height, arm length, leg length, arm bending angle, leg bending angle, etc. The constraints are the lateral movement direction of the first collision vehicle after the collision, the lateral movement direction of the second collision vehicle after the collision, and the collision area between the traffic participants and the vehicles. The reconstruction targets are the relative positions of the two vehicles after the collision, the damage positions and damage levels of the traffic participants.

[0035] The simulation analysis module is used to simulate the simulation matrix generated by the optimization algorithm module. It includes the ISIGHT multi-objective optimization calling program and the MADYMO solver. The ISIGHT multi-objective optimization calling program is used to call the MADYMO solver based on the simulation matrix for solution.

[0036] like Figure 1 As shown, this embodiment also proposes a method for rapid reconstruction of a collision accident, comprising the following steps:

[0037] Step 1, establishing a multi-rigid body simulation basic model including a first collision vehicle, a second collision vehicle, and traffic participants in a multi-rigid body modeling module;

[0038] Step 2, based on the accident information collection module, obtain the traffic accident video surveillance to be reconstructed, and calculate the collision speed between the first collision vehicle and the second collision vehicle, the relative position of the first collision vehicle and the second collision vehicle before the collision, the relative position of the two vehicles after the collision, the lateral movement direction of the first collision vehicle after the collision, the lateral movement direction of the second collision vehicle after the collision, the collision area between the traffic participants and the vehicles, and the injury position and injury level of the traffic participants in the accident based on the "frame difference method";

[0039] Step 3, based on the optimization algorithm module, determine the multi-objective optimization algorithm, design variables (modeling parameters of the first collision vehicle, the second collision vehicle, and traffic participants), constraints (lateral movement direction of the first collision vehicle after collision, lateral movement direction of the second collision vehicle after collision, collision area between traffic participants and vehicles), and reconstruct the objectives (relative positions of the two vehicles after the collision, damage positions and damage levels of traffic participants).

[0040] Step 4: Based on the determined multi-objective optimization algorithm, DOE design is performed on the design variables to obtain a simulation matrix;

[0041] Step 5, based on the parameter conversion module, the parameters extracted from the traffic accident video are converted into modeling parameters that can be recognized in the simulation model;

[0042] Step 6, input the simulation matrix into the simulation analysis module, call the MADYMO solver based on the ISIGHT multi-objective optimization calling program to solve the simulation matrix, and obtain the simulation target values ​​corresponding to the simulation matrix (the relative positions of the two vehicles after the collision, the damage positions and damage levels of the traffic participants);

[0043] Step 7, determine whether the simulation calculation reaches the maximum number of iterations, if so, extract the simulation matrix and the corresponding simulation target value, if not, repeat step 6;

[0044] Step 8, compare all the obtained simulation target values ​​with the reconstruction target obtained in step 3 in turn to determine whether the error is less than 5%. If so, output the design variable corresponding to the simulation target value that meets the requirements. If so, repeat steps 3-7.

[0045] Step 9: Input the design variables corresponding to the simulation target values ​​that meet the requirements into the multi-rigid body modeling module to establish a collision accident reconstruction model that meets the requirements.

[0046] The rapid reconstruction platform for collision accidents proposed in the present invention solves the problem that traditional trial-and-error-based methods are difficult to integrate multiple reconstruction targets and carry out reconstruction efficiently and accurately. The rapid reconstruction platform for collision accidents proposed in the present invention realizes the whole process of multi-rigid body model from parameter selection, simulation calculation, result extraction, to target optimization, and is carried out automatically and uninterruptedly. The rapid reconstruction platform for collision accidents proposed in the present invention is based on automated simulation and multi-objective optimization theory, which significantly improves the efficiency of collision accident reconstruction, ensures the objectivity and accuracy of reconstruction results, and is suitable for the reconstruction of different complex collision scenes.

[0047] Although the embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A collision accident rapid reconstruction platform, characterized in that: include: An accident information collection module, used to collect real traffic accident information to be reconstructed; Multi-rigid body modeling module, used to perform multi-rigid body modeling of the first collision vehicle, the second collision vehicle, and traffic participants in the accident in MADYMO software; A simulation analysis module is used to perform simulation calculations on the simulation matrix generated by the optimization algorithm module; A parameter conversion module is used to convert the parameters extracted from the traffic accident video into modeling parameters that can be identified in the simulation model; The optimization algorithm module is used to determine the multi-objective optimization algorithm, design variables, constraints, and reconstruction objectives.

2. The collision accident rapid reconstruction platform according to claim 1, characterized in that: The traffic accident information is video surveillance of the place where the collision accident occurred. Using the surveillance video, the collision speed of the two vehicles that collided is first calculated based on the frame difference method, and then the relative positions of the first collision vehicle and the second collision vehicle before the collision, as well as the relative positions of the two vehicles after the collision are extracted; finally, the injury position and injury level of the traffic participants in the accident are extracted.

3. The collision accident rapid reconstruction platform according to claim 1, characterized in that: If the first collision vehicle and the second collision vehicle are motor vehicles, the modeling parameters are ground clearance, hood front edge height, bumper upper end depth, hood length, hood angle, and windshield angle; if they are two-wheeled vehicles, the modeling parameters are pedal height, handlebar height, and seat cushion height; the modeling parameters of traffic participants are height, arm length, leg length, arm bending angle, and leg bending angle.

4. The collision accident rapid reconstruction platform according to claim 1, characterized in that: The multi-objective optimization algorithm includes a multi-objective genetic algorithm or a particle swarm optimization algorithm, and the design variables include the modeling parameters of the first collision vehicle, the second collision vehicle, and the traffic participants in the multi-rigid body modeling module, including ground clearance, engine hood front edge height, bumper upper end depth, engine hood length, engine hood angle, windshield angle, pedal height, handle height, seat height, body height, arm length, leg length, arm bending angle or leg bending angle; wherein the constraints are the lateral movement direction of the first collision vehicle after the collision, the lateral movement direction of the second collision vehicle after the collision, and the collision area between the traffic participant and the vehicle; wherein the reconstruction target is the relative position of the two vehicles after the collision, the damage position and damage level of the traffic participant.

5. The collision accident rapid reconstruction platform according to claim 1, characterized in that: The simulation analysis module includes an ISIGHT multi-objective optimization calling program and a MADYMO solver, wherein the ISIGHT multi-objective optimization calling program is used to call the MADYMO solver for solving based on a simulation matrix.

6. A reconstruction method for a collision accident rapid reconstruction platform based on any one of claims 1 to 5, characterized in that: The steps include: S1, establishing a multi-rigid body simulation basic model including a first collision vehicle, a second collision vehicle, and traffic participants in a multi-rigid body modeling module; S2, based on the accident information collection module, obtains the traffic accident video surveillance to be reconstructed, calculates the collision speed between the first collision vehicle and the second collision vehicle, the relative position of the first collision vehicle and the second collision vehicle before the collision, the relative position of the two vehicles after the collision, the lateral movement direction of the first collision vehicle after the collision, the lateral movement direction of the second collision vehicle after the collision, the collision area between the traffic participant and the vehicle, and the injury position and injury level of the traffic participant in the accident; S3, based on the optimization algorithm module, determines the multi-objective optimization algorithm, design variables, constraints, and reconstruction objectives; S4, based on the determined multi-objective optimization algorithm, DOE design is performed on the design variables to obtain a simulation matrix; S5, based on the parameter conversion module, converting the parameters extracted from the traffic accident video into modeling parameters that can be recognized in the simulation model; S6, inputting the simulation matrix into the simulation analysis module, calling the MADYMO solver based on the ISIGHT multi-objective optimization calling program to solve the simulation matrix, and obtaining the simulation target value corresponding to the simulation matrix; S7, determining whether the simulation calculation has reached the maximum number of iterations, if so, extracting the simulation matrix and the corresponding simulation target value, if not, repeating step 6; S8, comparing all the obtained simulation target values ​​with the reconstruction target obtained in step 3 in turn, and determining whether the error is less than 5%. If so, outputting the design variables corresponding to the simulation target values ​​that meet the requirements; if so, repeating steps 3-7; S9, inputting the design variables corresponding to the simulation target values ​​that meet the requirements into the multi-rigid body modeling module to establish a collision accident reconstruction model that meets the requirements.

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