Lane-Change Intention Estimation for Car-Following Control in Autonomous Driving

Car-following is the most general behavior in highway driving. It is crucial to recognize the cut-in intention of vehicles from an adjacent lane for safe and cooperative driving. In this paper, a method of behavior estimation is proposed to recognize and predict the lane change intentions based on the contextual traffic information. A model predictive controller is designed to optimize the acceleration sequences by incorporating the lane-change intentions of other vehicles. The public data set of next generation simulation is labeled and then published as a benchmarking platform for the research community. Experimental results demonstrate that the proposed method can accurately estimate vehicle behavior and therefore outperform the traditional car-following control.

• Record URL:
  https://doi.org/10.1109/TIV.2018.2843178
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
• Supplemental Notes:
  • Copyright © 2018, IEEE.
• Authors:
  • Zhang, Yigong
  • Lin, Qin
  • Wang, Jun
  • Verwer, Sicco
  • Dolan, John M
• Publication Date: 2018-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 276-286
• Serial:
  • IEEE Transactions on Intelligent Vehicles
  • Volume: 3
  • Issue Number: 3
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2379-8858
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7274857

Subject/Index Terms

• TRT Terms: Autonomous vehicle guidance; Car following; Connected vehicles; Lane changing; Process control
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01681420
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 21 2018 9:31AM