Identification of highway dangerous driving behavior based on Hidden Markov Model

2014 ◽  
pp. 209-212
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Driving Behavior ◽  
Dangerous Driving ◽  
Behavior Based

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

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2670 ◽  
Author(s):  
Yan Li ◽  
Fan Wang ◽  
Hui Ke ◽  
Li-li Wang ◽  
Cheng-cheng Xu
Keyword(s):  
Heart Rate ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Vehicle Dynamic ◽  
Dynamic Data ◽  
Lane Changing ◽  
Dangerous Driving ◽  
Proposed Model ◽  
Driving Risk

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.

scholarly journals A two-state mixed hidden Markov model for risky teenage driving behavior

2015 ◽  
Vol 9 (2) ◽  
pp. 849-865 ◽  
Author(s):  
John C. Jackson ◽  
Paul S. Albert ◽  
Zhiwei Zhang
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Driving Behavior

scholarly journals Research on students’ classroom behavior based on big data analysis and hidden Markov model

2021 ◽  
Vol 1873 (1) ◽  
pp. 012084
Author(s):  
Mingwei Tang ◽  
Xiaoliang Chen ◽  
Hongyun Mao ◽  
Tian Yang ◽  
Jianhua Xie ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Classroom Behavior ◽  
Hidden Markov ◽  
Big Data Analysis ◽  
Behavior Based
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