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. 2019 Jun 13;19(12):2670.
doi: 10.3390/s19122670.

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

Yan Li  1 , Fan Wang  2   3 , Hui Ke  4 , Li-Li Wang  5 , Cheng-Cheng Xu  6
Affiliations

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

Yan Li et al. Sensors (Basel). .

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.

Keywords: driving risk prediction; hidden Markov model; lane changing; physiology measurement sensor; vehicle dynamic data.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Selected experimental route.
Figure 2
Figure 2
Equipment installation and sensors on the equipment.
Figure 3
Figure 3
Characteristics of eye movement parameters of the lane-changing process.
Figure 4
Figure 4
Distribution of fixation duration under various conditions. LOS—level of service.
Figure 5
Figure 5
Distribution of saccade range under various conditions.
Figure 6
Figure 6
Electrocardiogram (ECG) data processing.
Figure 7
Figure 7
Characteristics of HRV parameters of lane changing process. LF/HF—ratio of absolute power of the low-frequency band to that of the high-frequency band; SDNN—standard deviation of normal to normal R–R intervals of the heart rate; CV—coefficient of variation of the R–R intervals.
Figure 7
Figure 7
Characteristics of HRV parameters of lane changing process. LF/HF—ratio of absolute power of the low-frequency band to that of the high-frequency band; SDNN—standard deviation of normal to normal R–R intervals of the heart rate; CV—coefficient of variation of the R–R intervals.
Figure 8
Figure 8
Distribution of SDNN under various conditions.
Figure 9
Figure 9
Variations of vehicle dynamic parameters of one specific lane-changing process.
Figure 10
Figure 10
Distribution of average speed under various conditions.
Figure 11
Figure 11
Structure of the state transition probability matrix.
See this image and copyright information in PMC

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