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Open AccessArticle

A Driver’s Physiology Sensor-Based Driving Risk Prediction Method for Lane-Changing Process Using Hidden Markov Model

by Yan Li 1, Fan Wang 1,2, Hui Ke 1, Li-li Wang 1 and Cheng-cheng Xu 3,*
1
School of Highway, Chang’an University, Xi’an 710064, China
2
State Key Laboratory of Road Engineering Safety and Health in Cold and High-altitude Regions, CCCC First Highway Consultants Co., LTD, Xi’an 710075, China
3
School of Transportation, Southeast University, Nanjing 211189, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(12), 2670; https://doi.org/10.3390/s19122670
Received: 5 May 2019 / Revised: 8 June 2019 / Accepted: 11 June 2019 / Published: 13 June 2019
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
Lane changing is considered as one of the most dangerous driving behaviors because drivers have to deal with the traffic conflicts on both the current and target lanes. This study aimed to propose a method of predicting the driving risks during the lane-changing process using drivers’ physiology measurement data and vehicle dynamic data. All the data used in the proposed model were obtained by portable sensors with the capability of recording data in the actual driving process. A hidden Markov model (HMM) was proposed to link driving risk with drivers’ physiology information and vehicle dynamic data. The two-factor indicators were established to evaluate the performances of eye movement, heart rate variability, and vehicle dynamic parameters on driving risk. The standard deviation of normal to normal R–R intervals of the heart rate (SDNN), fixation duration, saccade range, and average speed were then selected as the input of the HMM. The HMM was trained and tested using field-observed data collected in Xi’an City. The proposed model using the data from the physiology measurement sensor can identify dangerous driving state from normal driving state and predict the transition probability between these two states. The results match the perceptions of the tested drivers with an accuracy rate of 90.67%. The proposed model can be used to develop proactive crash prevention strategies. View Full-Text
Keywords: driving risk prediction; hidden Markov model; physiology measurement sensor; vehicle dynamic data; lane changing driving risk prediction; hidden Markov model; physiology measurement sensor; vehicle dynamic data; lane changing
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MDPI and ACS Style

Li, Y.; Wang, F.; Ke, H.; Wang, L.-L.; Xu, C.-C. A Driver’s Physiology Sensor-Based Driving Risk Prediction Method for Lane-Changing Process Using Hidden Markov Model. Sensors 2019, 19, 2670.

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