@InCollection{Supelec605,
author = {Fabio Tango and Raghav Aras and Olivier Pietquin},
title = {Learning Optimal Control Strategies from Interactions for a Partially Autonomous Driver Assistance System},
year = {2011},
booktitle = {Human Modelling in Assisted Transportation: Models, Tools and Risk Methods},
publisher = {Springer Verlag, Heidelberg - Berlin},
pages = {119-128},
month = {June},
note = {Proceedings of the Workshop on Human Modelling in Assisted Transportation (HMAT 2010)},
editor = {P.C Cacciabue and M. Hjälmdahl and A. Luedtke and C. Riccioli},
address = {Belgirate (Italy)},
url = {http://www.springer.com/engineering/mechanical+eng/book/978-88-470-1820-4\'echangeHeader},
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.}
}