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 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, the authors 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.

• Record URL:
  • https://doi.org/10.1109/TITS.2018.2873595
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2019, IEEE.
• Authors:
  • Li, Xiaohan
  • Wang, Wenshuo
  • Roetting, Matthias
• Publication Date: 2019-9

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 3258-3271
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 20
  • Issue Number: 9
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Algorithms; Bayes' theorem; Behavior; Driver monitoring; Driving; Eye movements; Lane changing; Mathematical models
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01718380
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Sep 27 2019 12:07PM