Probabilistic MDP-behavior planning for cars

This paper presents a method for high-level decision making in traffic environments. In contrast to the usual approach of modeling decision policies by hand, a Markov Decision Process (MDP) is employed to plan the optimal policy by assessing the outcomes of actions. Using probability theory, decisions are deduced automatically from the knowledge about how road users behave over time. This approach does neither depend on an explicit situation recognition nor is it limited to only a variety of situations or types of descriptions. Hence it is versatile and powerful. The contribution of this paper is a mathematical framework to derive abstract symbolic states from complex continuous temporal models encoded as Dynamic Bayesian Networks (DBN). For this purpose discrete MDP states are interpreted by random variables. To make computation feasible this space grows adaptively during planning and according to the problem to be solved.

• Record URL:
  https://doi.org/10.1109/ITSC.2011.6082928
• Availability:
  • Find a library where document is available. Order URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6069628
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Corporate Authors:

  Institute of Electrical and Electronics Engineers (IEEE)

  3 Park Avenue, 17th Floor
  New York, NY  United States  10016-5997
• Authors:
  • Brechtel, S
  • Gindele, T
  • Dillmann, R
• Conference:
  • 14th International IEEE Conference on Intelligent Transportation Systems (ITSC 2011)
  • Location: Washington DC, United States
  • Date: 2011-10-5 to 2011-10-7
• Publication Date: 2011-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1537-1542
• Monograph Title: 14th International IEEE Conference on Intelligent Transportation Systems (ITSC 2011)

Subject/Index Terms

• TRT Terms: Behavior; Decision making; Drivers; Intelligent vehicles; Markov processes; Probability theory
• Uncontrolled Terms: Bayesian networks
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment; I91: Vehicle Design and Safety;

Filing Info

• Accession Number: 01565395
• Record Type: Publication
• ISBN: 9781457721984
• Files: TRIS
• Created Date: May 30 2015 6:00PM