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International Journal of Automotive Technology > Volume 23(4); 2022 > Article
International Journal of Automotive Technology 2022;23(4): 1035-1044.
doi: https://doi.org/10.1007/s12239-022-0090-2
Ying Lu 1, Yuezhou Yin 1, Heng Wei 2, Na Chen 3
1School of Automotive and Traffic Engineering, Jiangsu University
2Department of Civil & Architectural Engineering, University of Cincinnati
3School of Planning, College of Design, Architecture
PDF Links Corresponding Author.  Ying Lu  , Email. luying@ujs.edu.cn
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.
Key Words: Advanced automatic crash notification system, Discrimination threshold, Velocity variation, Self-tuning algorithm, Vehicle terminal
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