Application of Hidden Markov Model on Car Sensors for Detecting Drunk Drivers

The ability to detect drunk driving behavior on roadways enhances road safety by significantly reducing the risk of fatal accidents. In this paper, a set of measurements, readily available via on-board vehicle sensors, was selected to detect drunk driving behaviors based on learning in accordance with certain drunk driving cues. A Hidden Markov Model (HMM) method was applied for each of the collected time series data, which correspond to the selected measurements. The prediction accuracy attained using each measured variable was derived and analyzed. The longitudinal acceleration achieved the best average prediction accuracy, for detecting both drunk and normal driving behaviors, with an accuracy that is equal to about 79%.

[1]  Jr. G. Forney,et al.  The viterbi algorithm , 1973 .

[2]  Rajesh Rajamani,et al.  Vehicle dynamics and control , 2005 .

[3]  Dong Xuan,et al.  Mobile phone based drunk driving detection , 2010, 2010 4th International Conference on Pervasive Computing Technologies for Healthcare.

[4]  Naseem Daher,et al.  Combined fuzzy and nonlinear dynamic observer for vehicle longitudinal velocity and side-slip angle , 2018, 2018 11th International Symposium on Mechatronics and its Applications (ISMA).

[5]  Uwe Kiencke,et al.  Automotive Control Systems , 2005 .

[6]  Haitham Akkary,et al.  Toward self-policing: Detecting drunk driving behaviors through sampling CAN bus data , 2017, 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA).

[7]  E. B. Andersen,et al.  Asymptotic Properties of Conditional Maximum‐Likelihood Estimators , 1970 .

[8]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[9]  Li Zhenlong,et al.  Drunk driving detection based on classification of multivariate time series , 2015 .

[10]  Xiaohua Zhao,et al.  Drunk driving detection based on classification of multivariate time series. , 2015, Journal of safety research.

[11]  Eamonn J. Keogh,et al.  An online algorithm for segmenting time series , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[12]  Didier Puzenat,et al.  Multi-user Blood Alcohol Content estimation in a realistic simulator using Artificial Neural Networks and Support Vector Machines , 2013, ESANN.