author = {Olivier Pietquin and Fabio Tango},
title = {A Reinforcement Learning Approach to Optimize the longitudinal Behavior of a Partial Autonomous Driving Assistance System },
year = {2012},
booktitle = {Proceedings of the European Conference on Artificial Intelligence (ECAI 2012) and the seventh Conference on Prestigious Applications of Intelligent Systems (PAIS 2012)},
pages = {987 - 992},
month = {August},
note = {Best Paper Award},
address = {Montpellier (France)},
url = {https://www.haiti.cs.uni-potsdam.de/proceedings/ECAI2012/content/ecai/ecai2012182.pdf},
abstract = {The Partially Autonomous Driving Assistance System (PADAS) is an artificial intelligent co-driver, able to act in critical situations, whose objective is to assist people in driving safely, by providing pertinent and accurate information in real- time about the external situation. Such a system intervenes continuously from warnings to automatic intervention in the whole longitudinal control of the vehicle. This paper illustrates the optimization process of the PADAS, following a statistical machine learning methods - Reinforcement Learning - where the action selection is derived from a set of recorded interactions with human drivers. Experimental results on a driving simulator prove this method achieves a significant reduction in the risk of collision.}