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

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 MA...

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Veröffentlicht in:Frontiers in bioengineering and biotechnology Jg. 10; S. 1032621
Hauptverfasser: Zou, Donghua, Fan, Ying, Liu, Ningguo, Zhang, Jianhua, Liu, Dikun, Liu, Qingfeng, Li, Zhengdong, Wang, Jinming, Huang, Jiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media SA 05.12.2022
Frontiers Media S.A
Schlagworte:
accident reconstruction
Accidents
Aerial photography
Algorithms
Bioengineering and Biotechnology
Forensic sciences
Genetic algorithms
Injuries
Investigations
Kinematics
Mathematical models
Methods
multibody simulation
multiobjective optimization algorithm
Optimization
pedestrian injury
Simulation
Software
traffic accident
Traffic accidents & safety
Unmanned aerial vehicles
China
multibody simulation
accident reconstruction
multiobjective optimization algorithm
traffic accident
pedestrian injury
ISSN:2296-4185, 2296-4185
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Zusammenfassung: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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Fang Wang, Changsha University of Science and Technology, China
Edited by: Ajay Seth, Delft University of Technology, Netherlands
Reviewed by: Riender Happee, Delft University of Technology, Netherlands
These authors have contributed equally to this work and share first authorship
This article was submitted to Biomechanics, a section of the journal Frontiers in Bioengineering and Biotechnology
ISSN:2296-4185
2296-4185
DOI:10.3389/fbioe.2022.1032621