@InProceedings{Supelec676,
author = {Olivier Pietquin and Fabio Tango and Raghav Aras},
title = {{Batch Reinforcement Learning for Optimizing Longitudinal Driving Assistance Strategies}},
year = {2011},
booktitle = {{Proceedings of the IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS 2011)}},
pages = {73 - 79},
month = {April},
address = {Paris (France)},
url = {http://www.metz.supelec.fr//metz/personnel/pietquin/pdf/CIVTS_2011_OPRAFT.pdf},
doi = {10.1109/CIVTS.2011.5949533},
abstract = {Partially Autonomous Driver's Assistance Systems (PADAS) are
systems aiming at providing a safer driving experience to
people. Especially, one application of such systems is to
assist the drivers in reacting optimally so as to prevent
collisions with a leading vehicle. Several means can be used by
a PADAS to reach this goal. For instance, warning signals can
be sent to the driver or the PADAS can actually modify the
speed of the car by braking automatically. An optimal
combination of different warning signals together with
assistive braking is expected to reduce the probability of
collision. How to associate the right combination of PADAS
actions to a given situation so as to achieve this aim remains
an open problem. In this paper, the use of a statistical
machine learning method, namely the reinforcement learning
paradigm, is proposed to automatically derive an optimal PADAS
action selection strategy from a database of driving
experiments. Experimental results conducted on actual car
simulators with human drivers show that this method achieves a
significant reduction of the risk of collision.}
}