Citation

Deng C, Wu C, Lyu N, Huang Z. PLoS One 2017; 12(8): e0182419.

Affiliation

School of Automation, Institution Name, Wuhan University of Technology, Wuhan, Hubei, China.

Copyright

(Copyright © 2017, Public Library of Science)

DOI

10.1371/journal.pone.0182419

PMID

28837580

Abstract

Since the advantage of hidden Markov model in dealing with time series data and for the sake of identifying driving style, three driving style (aggressive, moderate and mild) are modeled reasonably through hidden Markov model based on driver braking characteristics to achieve efficient driving style. Firstly, braking impulse and the maximum braking unit area of vacuum booster within a certain time are collected from braking operation, and then general braking and emergency braking characteristics are extracted to code the braking characteristics. Secondly, the braking behavior observation sequence is used to describe the initial parameters of hidden Markov model, and the generation of the hidden Markov model for differentiating and an observation sequence which is trained and judged by the driving style is introduced. Thirdly, the maximum likelihood logarithm could be implied from the observable parameters. The recognition accuracy of algorithm is verified through experiments and two common pattern recognition algorithms. The results showed that the driving style discrimination based on hidden Markov model algorithm could realize effective discriminant of driving style.

Language: en