Modelling driving behaviour using hybrid automata

This study presents a new approach to the modelling of human driving behaviour. It describes driving behaviour as the result of an optimisation process within the formal framework of hybrid automata. In contrast to most approaches, the aim is not to construct a (cognitive) model of a human driver, but to directly model driving behaviour. The authors assume human driving to be controlled by the anticipated outcomes of possible behaviours. These positive and negative outcomes are mapped onto a single theoretical variable - the so called reinforcement value. Behaviour is assumed to be chosen in such a way that the reinforcement value is optimised in any given situation. To formalise their models the authors use hybrid automata, which allow for both continuous variables and discrete states. The models are evaluated using simulations of the optimised driving behaviours. A car entering a freeway served as the scenario to demonstrate the approach. First results yield plausible predictions for car trajectories and the chronological sequence of speed, depending on the surrounding traffic, indicating the feasibility of the approach.

• Record URL:
  • https://doi.org/10.1049/iet-its.2012.0150
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/1751956X
• Authors:
  • Schwarze, Anke
  • Buntins, Matthias
  • Schicke-Uffmann, Jens
  • Goltz, Ursula
  • Eggert, Frank
• Publication Date: 2013-6

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 251-256
• Serial:
  • IET Intelligent Transport Systems
  • Volume: 7
  • Issue Number: 2
  • Publisher: Institution of Engineering and Technology (IET)
  • ISSN: 1751-956X
  • EISSN: 1751-9578
  • Serial URL: https://ietresearch.onlinelibrary.wiley.com/journal/17519578
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Automata theory; Behavior; Driver information systems; Drivers; Human factors engineering; Optimization
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Safety and Human Factors; I72: Traffic and Transport Planning; I83: Accidents and the Human Factor;

Filing Info

• Accession Number: 01489538
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 12 2013 4:45PM