Abstract
Driver Systems for autonomous vehicles are the nucleus of many studies done so far. In this light, they mainly consist of two major parts: the recognition of the environment (usually based on image processing) as well as any learning aspects for the driving behaviour. The latter is the nucleus of this research whereby learning aspects are understood that way that the driving behaviour should be optimised over time, therefore the most appropriate actions for each possible situation should be self-created and lastly offered for selection. The current research bases the learning aspects on means of Reinforcement Learning which is in sharp contrast to other research studies done before being mainly based on explicit modelling or neural nets.
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Kuhnert, KD., Krödel, M. (2003). A Learning Autonomous Driver System on the Basis of Image Classification and Evolutional Learning. In: Perner, P., Rosenfeld, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2003. Lecture Notes in Computer Science, vol 2734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45065-3_35
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DOI: https://doi.org/10.1007/3-540-45065-3_35
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