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

Abstract

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.

Wireless Sensors System for Stress Detection by Means of ECG and EDA Acquisition

“…Just in the current year, hundreds of works have been published with the aim of investigating the stress induced by several conditions of every-day life. Some works aim at the measurement of stress in the workplace [1], some others investigate stress while people are playing videogames [2], but the majority of studies investigate the mental stress while people are driving [3][4][5][6][7][8][9][10].…”

Eye-Tracking Evaluation of Exit Advance Guide Signs in Highway Tunnels in Familiar and Unfamiliar Drivers

“…From the perspective of the driver’s fixation transfer characteristics, the position of the fixation point at the current moment was only related to that at the previous moment. Therefore, we used a Markov chain to analyze all schemes [ 46 , 47 , 48 , 49 , 50 , 51 , 52 ].…”

“…The results showed significant differences in the saccadic speed, eye–head coordination mode, and other parameters between the data for 5 s before lane change and 5 s during the lane-keeping period. Yan [ 50 ] proposed an HMM to connect driving risk, physiological information, and vehicle dynamic data. Using data from physiological measurement sensors, the model could identify dangerous and normal driving states and predict the probability of transition from dangerous to normal.…”

Application of Wireless Accelerometer Mounted on Wheel Rim for Parked Car Monitoring

“…The classic sensing systems increasing the safety of car operation include: tire-pressure monitoring system (TPMS), adaptive cruise control (ACC), blind-spot detection (BSD), lane-departure warning (LDW), traction control (TC) sometimes called rollover prevention, antilock braking system (ABS), emergency brake assist (EBA), adaptive headlights and rearview camera. All these systems are well developed, but the progress in technology and system performances are under continuous improvements [6][7][8][9][10][11].…”