Anticipatory Driving for a Robot-Car Based on Supervised Learning

  • Irene Markelić
  • Tomas Kulviĉius
  • Minija Tamosiunaite
  • Florentin Wörgötter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5499)


Prediction and Planning are essential elements of successful human driving, making them equally important for autonomously driving systems. Many approaches achieve planning based on built-in world-knowledge. However, we show how a learning-based system can be extended to planning, needing little a priori knowledge. A car-like robot is trained by a human driver by constructing a database, where look ahead sensory information is stored together with action sequences. From that we achieve a novel form of velocity control, based only on information in image coordinates. For steering we employ a two-level approach in which database information is combined with an additional reactive controller. The result is a trajectory planning robot running at real-time, issuing steering and velocity control commands in a human manner.


anticipatory behavior example based learning robot car driving longitudinal control lateral control learning from experience 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Irene Markelić
    • 1
  • Tomas Kulviĉius
    • 1
  • Minija Tamosiunaite
    • 2
  • Florentin Wörgötter
    • 1
  1. 1.Bernstein Center for Computational NeuroscienceUniversity of GöttingenGöttingenGermany
  2. 2.Vytautas Magnus UniversityKaunasLithuania

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