Clustering driver behavior using dynamic time warping and hidden Markov model Academic Article uri icon

abstract

  • © 2019, © 2019 Taylor & Francis Group, LLC. Based on on-board diagnostics and Global Position System installed in taxicabs, driver behavior data is collected. Left turn data on six similar curves are extracted, and speed, acceleration, yaw rate, and sideslip angle of drivers in time series are selected as clustering indexes. Initial clustering is implemented by Dynamic Time Warping (DTW) and Hierarchical Clustering, and the clustering results are put into the Hidden Markov Model (HMM) to iteratively optimize the results for achieving convergence. Driver behavior patterns over time while driving on the curves and the statistical characteristics of different groups are examined. All indexes including lateral vehicle control and longitudinal vehicle control have a significant difference in different groups, indicating that the clustering method of DTW and HMM can effectively classify driver behavior. Finally, the driving behavior in different groups is further investigated and classified based on characteristics related to safe and ecological driving. This method can be applied by automobile insurance companies, and for the development of specific training courses for drivers to optimize their driving behavior.

author list (cited authors)

  • Yao, Y., Zhao, X., Wu, Y., Zhang, Y., & Rong, J.

citation count

  • 7

publication date

  • August 2019