@InCollection{Supelec606,
author = {Fabio Tango and Maria Alonso and Maria Henar Vega and Raghav Aras and Olivier Pietquin},
title = {{A Reinforcement Learning approach for designing and optimizing interaction strategies for a Human-Machine Interface of 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 = {353-362},
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 = {The FP7 EU project ISi-PADAS (Integrated Human Modelling and
Simulation to support Human Error Risk Analysis of Partially
Autonomous Driver Assistance Systems) endeavours to conceive an
intelligent system called PADAS (Partially Autonomous Driver
Assistance System) for aiding human drivers in driving safely
by providing them with pertinent and accurate information in
real time about the external situation and by acting as a co-
pilot in emergency conditions. The system interacts with the
driver through a Human-Machine Interface (HMI) installed on the
vehicle using an adequate Warning and Intervention Strategy
(WIS). }
}