Learning Optimal Control Strategies from Interactions with a PADAS

Fabio Tango (1) , Raghav Aras (2) , Olivier Pietquin (2, 3)

1 CRF - Centro Ricerche Fiat
2 SUPELEC-Campus Metz
3 Georgia Tech Lorraine [Metz]

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.

Domaines: Apprentissage [cs.LG]

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⟩