In this study, the effect of discrimination threshold on velocity variation is investigated, and a self-tuning algorithm for velocity variation is proposed to improve the accuracy of an advanced automatic crash notification (AACN) system. First, after determining the factors affecting driver injury, an injury prediction model for the driver is developed. Second, the prediction accuracy affected by the discrimination threshold in the AACN system is analyzed using a finite element model for a sled crash test. Third, the self-tuning occupant injury prediction algorithm is presented based on an association model for the velocity variation error, discrimination threshold, and acceleration peak. Fourth, a vehicle terminal is designed by embedding a self-tuning algorithm into the system. Finally, a sled test and vehicle crash test are conducted to verify the reliability of the self-tuning algorithm. The test results show that the self-tuning algorithm can increase the accuracy of the probability of driver injury.
Self-Tuning Occupant Injury Prediction Algorithm for Advanced Automatic Crash Notification System
Int.J Automot. Technol.
International Journal of Automotive Technology ; 23 , 4 ; 1035-1044
2022-08-01
10 pages
Article (Journal)
Electronic Resource
English
Injury prediction algorithm for rear‐seat occupants in advanced automatic crash notification systems
Wiley | 2022
|Injury prediction algorithm for rear‐seat occupants in advanced automatic crash notification systems
DOAJ | 2022
|Occupant Injury Response Prediction Prior to Crash Based on Pre-Crash Systems
SAE Technical Papers | 2017
|Occupant Injury Response Prediction Prior to Crash Based on Pre-Crash Systems
British Library Conference Proceedings | 2017
|Trigger algorithm of vehicle automatic crash notification system
Online Contents | 2016
|