Driver Intention Recognition Method Using Continuous Hidden Markov Model

DOI

10.2991/ijcis.2011.4.3.13How to use a DOI?

Abstract

In order to make Intelligent Transportation System (ITS) work effectively, a driver intention recognition method is proposed. In this research, three different recognition models were developed based on Continuous Hidden Markov Model (CHMM), and could distinguish left and right lane change intention from normal lane keeping intention. Subjects performed lane change maneuvers and lane keeping maneuvers with driving simulator which simulated highway scenes, parameters that highly correlated with lane change behavior were collected and analyzed. A series of testings and comparisons were done to obtain the optimal model structure and feature set. Results show that, taking the steering wheel angel, steering wheel angle velocity and lateral acceleration as the optimal observation signals, the accuracy can achieve up 95%, and it proved very effective in terms of early intention recognition.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Haijing Hou
AU  - Lisheng Jin
AU  - Qingning Niu
AU  - Yuqin Sun
AU  - Meng Lu
PY  - 2011
DA  - 2011/05/01
TI  - Driver Intention Recognition Method Using Continuous Hidden Markov Model
JO  - International Journal of Computational Intelligence Systems
SP  - 386
EP  - 393
VL  - 4
IS  - 3
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2011.4.3.13
DO  - 10.2991/ijcis.2011.4.3.13
ID  - Hou2011
ER  -