Driver fatigue transition prediction in highly automated driving using physiological features
作者：
Highlights：
• We predict the transition from non-fatigue to fatigue using physiological features.
• We capitalize on PERCLOS as the ground truth of driver fatigue.
• We select most critical physiological features to predict driver fatigue proactively.
• We predict the fatigue transition using nonlinear autoregressive exogenous network.
• The accuracy of fatigue transition prediction evidences the potential of our method.
摘要
•We predict the transition from non-fatigue to fatigue using physiological features.•We capitalize on PERCLOS as the ground truth of driver fatigue.•We select most critical physiological features to predict driver fatigue proactively.•We predict the fatigue transition using nonlinear autoregressive exogenous network.•The accuracy of fatigue transition prediction evidences the potential of our method.
论文关键词：Driver fatigue,PERCLOS,Fatigue transition prediction,Highly automated driving
论文评审过程：Received 13 August 2019, Revised 24 November 2019, Accepted 13 January 2020, Available online 15 January 2020, Version of Record 23 January 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113204
