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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Markelić, I., Kulviĉius, T., Tamosiunaite, M., & Wörgötter, F. (2009). Anticipatory driving for a robot-car based on supervised learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5499 LNAI, pp. 267–282). https://doi.org/10.1007/978-3-642-02565-5_15
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