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Agent-Based Driver Abnormality Estimation

  • Tony Poitschke
  • Florian Laquai
  • Gerhard Rigoll
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5612)

Abstract

For enhancing current driver assistance and information systems with regard to the capability to recognize an individual driver’s needs, we conceive a system based on fuzzy logic and a multi-agent-framework. We investigate how it is possible to gain useful information about the driver from typical vehicle data and apply the knowledge on our system. In a pre-stage, the system learns the driver’s regular steering manner with the help of fuzzy inference models. By comparing his regular and current manner, the system recognizes whether the driver is possibly impaired and betakes in a risky situation. Furthermore, the steering behavior and traffical situation are continuously observed for similar pattern. According to the obtained information, the system tries to conform its assistance functionalities to the driver’s needs.

Keywords

Fuzzy Inference System Test Person Steering Behavior Steer Wheel Angle Headway Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Tony Poitschke
    • 1
  • Florian Laquai
    • 1
  • Gerhard Rigoll
    • 1
  1. 1.Institute for Human-Machine CommunicationTechnische Universität MünchenMunichGermany

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