@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). }
}