A data-driven, kinematic feature-based, near real-time algorithm for injury severity prediction of vehicle occupants

• Published in: Accident analysis and prevention Vol. 156; p. 106149
• Main Authors: Wang, Qingfan, Gan, Shun, Chen, Wentao, Li, Quan, Nie, Bingbing
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.06.2021
• Subjects: Injury risk; Machine-learning algorithms; Motor vehicle crashes; Occupant protection; Prediction models; Motor vehicle crashes; Occupant protection; Prediction models; Injury risk; Machine-learning algorithms
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2021.106149

•Accurate prediction on injury severity is a prerequisite for enhancing road traffic safety.•We designed a two-phase framework consisting of CNN-based model construction and kinematic feature extraction.•We built an SVM-based algorithm, obtaining 85.4 % prediction accuracy in 1.2 ms.•The proposed algorithm provides a decision reference for integrated vehicular safety. Accurate real-time prediction of occupant injury severity in unavoidable collision scenarios is a prerequisite for enhancing road traffic safety with the development of highly automated vehicles. Specifically, a safety prediction model provides a decision reference for the trajectory planning system in the pre-crash phase and the adaptive restraint system in the in-crash phase. The main goal of the current study is to construct a data-driven, vehicle kinematic feature-based model to realize accurate and near real-time prediction of in-vehicle occupant injury severity. A large-scale numerical database was established focusing on occupant kinetics. A first-step deep-learning model was established to predict occupant kinetics and injury severity using a convolutional neural network (CNN). To reduce the computational time for real-time application, the second step was to extract simplified kinematic features from vehicle crash pulses via a feature extraction method, which was inspired by a visualization approach applied to the CNN-based model. The features were incorporated with a low-complexity machine-learning algorithm and achieved satisfactory accuracy (85.4 % on the numerical database, 78.7 % on a 192-case real-world dataset) and decreased computational time (1.2 ± 0.4 ms) on the prediction tasks. This study demonstrated the feasibility of using data-driven and feature-based approaches to achieve accurate injury risk estimation prior to collision. The proposed model is expected to provide a decision reference for integrated safety systems in the next generation of automated vehicles.