Welcome to Journal of Automotive Safety and Energy,

Journal of Automotive Safety and Energy ›› 2022, Vol. 13 ›› Issue (2): 259-268.DOI: 10.3969/j.issn.1674-8484.2022.02.005

• Automotive Safety • Previous Articles     Next Articles

Automobile crash test time-series data processing and classification method based on SSI-PSO algorithm

LI Han1(), LIU Zhao2, ZHU Ping1()   

  1. 1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    2. School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2021-12-23 Revised:2022-03-06 Online:2022-06-30 Published:2022-07-01
  • Contact: ZHU Ping E-mail:sjtulihan@sjtu.edu.cn;pzhu@sjtu.edu.cn

Abstract:

This paper investigated an optimization problem transformation and construction method for heuristic optimization algorithm to realize the category identification of dummy curve dataset from automobile crash test. A method of feature selection and classification was proposed for multi-variable time-series data in crash test based on a social spider inspired particle swarm optimization (SSI-PSO) for the feature processing and for the classification process of dummy curve data. The proposed method was tested and validated by using the dummy curve data collected from automobile crash test. The result shows that the optimal feature combination and the small-scale neural network for dummy curve classification are obtained by the proposed method. The performance of dummy curve classification model improves by 17.5% and classification accuracy reaches 96.5% based on the proposed method. Therefore, the labeling information of dummy response curve from crash test is classified effectively.

Key words: automobile crash, safety data, multi-variable time series data, social spider inspired particle swarm optimization (SSI-PSO) algorithm, feature engineering, supervised learning, heuristic optimization algorithm

CLC Number: