Zou Donghua, Fan Ying, Liu Ningguo, Zhang Jianhua, Liu Dikun, Liu Qingfeng, Li Zhengdong, Wang Jinming, Huang Jiang
School of Forensic Medicine, Guizhou Medical University, Guiyang, China.
Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, China.
Front Bioeng Biotechnol. 2022 Dec 5;10:1032621. doi: 10.3389/fbioe.2022.1032621. eCollection 2022.
In vehicle-pedestrian accidents, the preimpact conditions of pedestrians and vehicles are frequently uncertain. The incident data for a crash, such as vehicle deformation, injury of the victim, distance of initial position and rest position of accident participants, are useful for verification in MAthematical DYnamic MOdels (MADYMO) simulations. The purpose of this study is to explore the use of an improved optimization algorithm combined with MADYMO multibody simulations and crash data to conduct accurate reconstructions of vehicle-pedestrian accidents. The objective function of the optimization problem was defined as the Euclidean distance between the known vehicle, human and ground contact points, and multiobjective optimization algorithms were employed to obtain the local minima of the objective function. Three common multiobjective optimization algorithms-nondominated sorting genetic algorithm-II (NSGA-II), neighbourhood cultivation genetic algorithm (NCGA), and multiobjective particle swarm optimization (MOPSO)-were compared. The effect of the number of objective functions, the choice of different objective functions and the optimal number of iterations were also considered. The final reconstructed results were compared with the process of a real accident. Based on the results of the reconstruction of a real-world accident, the present study indicated that NSGA-II had better convergence and generated more noninferior solutions and better final solutions than NCGA and MOPSO. In addition, when all vehicle-pedestrian-ground contacts were considered, the results showed a better match in terms of kinematic response. NSGA-II converged within 100 generations. This study indicated that multibody simulations coupled with optimization algorithms can be used to accurately reconstruct vehicle-pedestrian collisions.
在车辆与行人碰撞事故中，行人与车辆碰撞前的状况往往难以确定。碰撞事故的事件数据，如车辆变形、受害者受伤情况、事故参与者初始位置与静止位置的距离等，对于数学动态模型（MADYMO）模拟的验证很有用。本研究的目的是探索结合MADYMO多体模拟和碰撞数据，使用改进的优化算法来准确重建车辆与行人碰撞事故。优化问题的目标函数定义为已知的车辆、人体与地面接触点之间的欧几里得距离，并采用多目标优化算法来获取目标函数的局部最小值。比较了三种常见的多目标优化算法——非支配排序遗传算法-II（NSGA-II）、邻域培育遗传算法（NCGA）和多目标粒子群优化算法（MOPSO）。还考虑了目标函数数量的影响、不同目标函数的选择以及最优迭代次数。将最终的重建结果与一起真实事故的过程进行了比较。基于一起真实事故的重建结果，本研究表明，NSGA-II比NCGA和MOPSO具有更好的收敛性，能产生更多的非劣解和更好的最终解。此外，当考虑所有车辆-行人-地面接触时，结果在运动学响应方面显示出更好的匹配度。NSGA-II在100代内收敛。本研究表明，多体模拟与优化算法相结合可用于准确重建车辆与行人碰撞事故。
