Real-time crash identification using connected electric vehicle operation data

Meixin Zhu; Hao Frank Yang; Chenxi Liu; Ziyuan Pu; Yinhai Wang

doi:10.1016/j.aap.2022.106708

Real-time crash identification using connected electric vehicle operation data

Accid Anal Prev. 2022 Aug:173:106708. doi: 10.1016/j.aap.2022.106708. Epub 2022 May 26.

Authors

Meixin Zhu¹, Hao Frank Yang², Chenxi Liu³, Ziyuan Pu⁴, Yinhai Wang⁵

Affiliations

¹ Department of Civil and Environmental Engineering, University of Washington, United States. Electronic address: meixin92@uw.edu.
² Department of Civil and Environmental Engineering, University of Washington, United States. Electronic address: haoya@uw.edu.
³ Department of Civil and Environmental Engineering, University of Washington, United States. Electronic address: lcx2017@uw.edu.
⁴ School of Engineering, Monash University, Australia. Electronic address: ziyuan.pu@monash.edu.
⁵ Department of Civil and Environmental Engineering, University of Washington, United States. Electronic address: yinhai@uw.edu.

PMID: 35640365
DOI: 10.1016/j.aap.2022.106708

Abstract

As the automobile market gradually develops towards intelligence, networking, and information-orientated, intelligent identification based on connected vehicle data becomes a key technology. Specifically, real-time crash identification using vehicle operation data can enable automotive companies to obtain timely information on the safety of user vehicle usage so that timely customer service and roadside rescue can be provided. In this paper, an accurate vehicle crash identification algorithm is developed based on machine learning techniques using electric vehicles' operation data provided by SAIC-GM-Wuling. The point of battery disconnection is identified as a potential crash event. Data before and after the battery disconnection is retrieved for feature extraction. Two different feature extraction methods are used: one directly extracts the descriptive statistical features of various variables, and the other directly unfolds the multivariate time series data. The AdaBoost algorithm is used to classify whether a potential crash event is a real crash using the constructed features. Models trained with the two different features are fused for the final outputs. The results show that the final model is simple, effective, and has a fast inference speed. The model has an F1 score of 0.98 on testing data for crash classification, and the identified crash times are all within 10 s around the true crash times. All data and code are available at https://github.com/MeixinZhu/vehicle-crash-identification.

Keywords: Anomaly detection; Connected vehicle; Crash recognition; Electric vehicles; Intelligent transportation.

MeSH terms

Accidents, Traffic* / prevention & control
Algorithms
Automobile Driving*
Automobiles
Humans
Technology