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Conference Papers Year : 2011

Learning Optimal Control Strategies from Interactions with a PADAS

Fabio Tango
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Raghav Aras
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Abstract

This paper addresses the problem of finding an optimal warning and intervention strategy (WIS) for a partially autonomous driver assistance system (PADAS). An optimal WIS here is defined as the minimizing the probability of collision with a leading vehicle while keeping the number of warnings and interventions as low as possible so as to not distract the driver. A novel approach to this problem is proposed in this paper. The optimal WIS will be considered as solving a sequential decision making problem. The adopted point of view comes from machine learning where the answer to optimal sequential decision making is the Reinforcement Learning (RL) paradigm.

Dates and versions

hal-00618402 , version 1 (01-09-2011)

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Fabio Tango, Raghav Aras, Olivier Pietquin. Learning Optimal Control Strategies from Interactions with a PADAS. HMAT 2010, Jun 2010, Belgirate, Italy. pp.119-127, ⟨10.1007/978-88-470-1821-1_12⟩. ⟨hal-00618402⟩
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