Abstract
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.
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Markelić, I., Kulviĉius, T., Tamosiunaite, M., Wörgötter, F. (2009). Anticipatory Driving for a Robot-Car Based on Supervised Learning. In: Pezzulo, G., Butz, M.V., Sigaud, O., Baldassarre, G. (eds) Anticipatory Behavior in Adaptive Learning Systems. ABiALS 2008. Lecture Notes in Computer Science(), vol 5499. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02565-5_15
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DOI: https://doi.org/10.1007/978-3-642-02565-5_15
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