Welcome to Journal of Automotive Safety and Energy,

Journal of Automotive Safety and Energy ›› 2023, Vol. 14 ›› Issue (5): 536-543.DOI: 10.3969/j.issn.1674-8484.2023.05.002

• Automotive Safety • Previous Articles     Next Articles

Prediction of pedestrian head injury in vehicle-pedestrian collisions based on a CART decision tree

HAN Yong1(), LUO Jinrong1(), HE Yong2, WU He3, LIN Xujie1, CAI Hongyu1   

  1. 1. School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China
    2. School of Information and Intelligent Mechatronics, Xiamen Huaxia University, Xiamen 361024, China
    3. School of Aeronautics and Astronautics, Xiamen University, Xiamen 361102, China
  • Received:2023-03-04 Revised:2023-07-17 Online:2023-10-31 Published:2023-10-31

Abstract:

A prediction model based on a multi-rigid-body system dynamics simulation method and a Classification and Regression Tree (CART) were established to rapidly predict the risk of head injury in vehicle to pedestrian collision. A multi-body model of the vehicle front structure with refined stiffness characteristics was developed with reference to the European New Car Assessment Program (Euro-NCAP). About 4 500 sets of multi-body simulations were established by the full factor design test method with the pedestrian model, initial vehicle speed and pedestrian speed, pedestrian-vehicle collision position, and relative angle as simulation variables, and the CART model was used to explore the correlation between the variables and the kinetic response parameters. The results show that the initial vehicle speed is a key factor affecting the dynamic response of the pedestrian head. The prediction accuracy of the model for the collision speed and the value of the head injury criterion (HIC15) is 87.5% and 86.8%, respectively, and the average prediction time is 42.7 ms, which have high prediction accuracy and decision-making ability. The results can provide a theoretical reference basis for developing pedestrian head injury risk assessment experiments and injury protection research.

Key words: vehicle safety, collision accident, head injury criterion (HIC), vehicle front structure, decision tree prediction model, head dynamics response, classification and regression tree (CART)

CLC Number: