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Title of the item:

Driver Intention Recognition Method Using Continuous Hidden Markov Model

Title :
Driver Intention Recognition Method Using Continuous Hidden Markov Model
Authors :
Haijing Hou
Lisheng Jin
Qingning Niu
Yuqin Sun
Meng Lu
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Subject Terms :
Electronic computers. Computer science
ComputingMethodologies_PATTERNRECOGNITION
ComputerApplications_COMPUTERSINOTHERSYSTEMS
QA75.5-76.95
Source :
International Journal of Computational Intelligence Systems, Vol 4, Iss 3, Pp 386-393 (2011)
International Journal of Computational Intelligence Systems, Vol 4, Iss 3 (2011)
Publisher :
Atlantis Press, 2011.
Publication Year :
2011
Language :
English
ISSN :
1875-6883
DOI :
10.2991/ijcis.2011.4.3.13
Accession Number :
edsair.dedup.wf.001..4624422da0615608f03499dfe409e1cc
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.

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