Autonomous Planning and Control for Intelligent Vehicles in Traffic

This paper addresses the trajectory planning problem for autonomous vehicles in traffic. The authors build a stochastic Markov decision process (MDP) model to represent the behaviors of the vehicles. This MDP model takes into account the road geometry and is able to reproduce more diverse driving styles. They introduce a new concept, namely, the “dynamic cell,” to dynamically modify the state of the traffic according to different vehicle velocities, driver intents (signals), and the sizes of the surrounding vehicles (i.e., truck, sedan, and so on). The authors then use Bézier curves to plan smooth paths for lane switching. The maximum curvature of the path is enforced via certain design parameters. By designing suitable reward functions, different desired driving styles of the intelligent vehicle can be achieved by solving a reinforcement learning problem. The desired driving behaviors (i.e., autonomous highway overtaking) are demonstrated with an in-house developed traffic simulator.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2019.2918071
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2020, IEEE.
• Authors:
  • You, Changxi
  • Lu, Jianbo
  • 0000-0001-9088-5663
  • Filev, Dimitar
  • Tsiotras, Panagiotis
  • 0000-0001-7563-4129
• Publication Date: 2020-6

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 2339-2349
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 21
  • Issue Number: 6
  • 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; Autonomous vehicles; Cellular automata; Decision trees; Highways; Machine learning; Markov processes; Traffic models; Trajectory control
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01749240
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Aug 27 2020 10:21AM