4.7 Article

Multiobjective optimization algorithm for accurate MADYMO reconstruction of vehicle-pedestrian accidents

Journal

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fbioe.2022.1032621

Keywords

traffic accident; accident reconstruction; multibody simulation; pedestrian injury; multiobjective optimization algorithm

Funding

  1. National Key Research and Development Plan [2022YFC3302002]
  2. National Natural Science Foundation of China [82171872]
  3. Natural Science Foundation of Shanghai [1ZR1464600]
  4. Shanghai Key Laboratory of Forensic Medicine [21DZ2270800]
  5. Shanghai Forensic Service Platform [19DZ2290900]
  6. Central Research Institute Public Project [GY2020G-4, GY2021G-5]
  7. Guizhou Provincial College Students' Innovation and Entrepreneurship Training Plan Project [S202110660012]

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This study aims to accurately reconstruct vehicle-pedestrian accidents by combining an improved optimization algorithm with MADYMO multibody simulations and crash data. The study found that NSGA-II algorithm outperformed other algorithms in terms of convergence, generating more noninferior solutions, and better final solutions. The results suggest that multibody simulations coupled with optimization algorithms can be used for accurate reconstruction of vehicle-pedestrian collisions.
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.

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