Open AccessInjury prediction algorithm for rear‐seat occupants in advanced automatic crash notification systems
Author(s) -
Lu Ying,
Liu Yufa,
Shu Yu,
Yin Yuezhou
Publication year - 2022
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/itr2.12153
Subject(s) - crash , seat belt , collision , aeronautics , computer science , simulation , algorithm , engineering , automotive engineering , computer security , programming language
An advanced automatic crash notification (AACN) system can predict the injuries of a vehicle's occupants when the vehicle is involved in a collision accident. The rear‐seat occupants are at significant risk in collision accidents owing to an inferior protection system and the absence of mandatory seat belt laws. However, only a few studies have focused on the injury prediction for rear‐seat occupants, which can be used in AACN system. Therefore, an injury prediction algorithm is proposed for rear‐seat occupants. Initially, a rear‐seat occupant simulation model is established and verified and the relationship between the head, neck, and chest injury levels and velocity variation is examined. Then, a rear‐seat occupant injury prediction algorithm is developed and AACN terminal is constructed to analyse the effects of the algorithm. The comparison of the obtained results with actual accident data validated that the proposed algorithm can effectively evaluate the injury levels of rear‐seat occupants. The study findings can enhance the prediction accuracy of all occupants’ injury levels based on their positions inside the vehicle and improve the efficiency of rescue operations, particularly in the case of rear‐seat occupants.