Estimating Driver's Lane-Change Intent Considering Driving Style and Contextual Traffic

Estimating a driver's lane-change (LC) intent is very important so as to avoid traffic accidents caused by improper LC maneuvers. This paper proposes a lane-change Bayesian network (LCBN) incorporated with a Gaussian mixture model (GMM), termed as LCBN-GMM, to estimate a driver's LC intent...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2019-09, Vol.20 (9), p.3258-3271
Main Authors: Li, Xiaohan, Wang, Wenshuo, Roetting, Matthias
Format: Article
Language:English
Subjects:
Bayes methods
Bayesian analysis
Bayesian network
Computer simulation
driving style
Estimation
Gaussian mixture model
gaze-based labeling method
Labeling
Labelling
Lane changing
Lane-change intent estimation
Lead
Maneuvers
Probabilistic models
Support vector machines
Traffic accidents
Vehicles
Windows (intervals)
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Summary:Estimating a driver's lane-change (LC) intent is very important so as to avoid traffic accidents caused by improper LC maneuvers. This paper proposes a lane-change Bayesian network (LCBN) incorporated with a Gaussian mixture model (GMM), termed as LCBN-GMM, to estimate a driver's LC intent considering a driver's driving style over varying scenarios. According to the scores made by participates with a behavioral-psychological questionnaire, three driving styles are classified. In order to get more effective labeled LC and lane-keep (LK) data for model training, we propose a gaze-based labeling (GBL) method by monitoring a drivers's gaze behavior, instead of using a time-window labeling method. The capability of LCBN-GMM to estimate a driver's lane-change intent is evaluated in different LC scenarios and driving styles, in comparison to support vector machine and Naive Bayes. Data are collected in a seat-box-based driving simulator where 32 drivers, consisting of 9 aggressive, 15 neutral, and 8 conservative drivers, participated. Experimental results demonstrate that the LCBN-GMM with GBL achieves the best performance, estimating a driver's LC intent an average of 4.5 s ahead of actual LC maneuvers with 78.2% accuracy considering both driving style and contextual traffic, compared with other approaches.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2018.2873595