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A Reinforcement Learning Approach to Optimize the longitudinal Behavior of a Partial Autonomous Driving Assistance System

Olivier Pietquin 1 Fabio Tango 2
1 IMS - Equipe Information, Multimodalité et Signal
UMI2958 - Georgia Tech - CNRS [Metz], SUPELEC-Campus Metz
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.
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https://hal-supelec.archives-ouvertes.fr/hal-00749436
Contributor : Sébastien van Luchene <>
Submitted on : Wednesday, November 7, 2012 - 3:23:46 PM
Last modification on : Wednesday, September 16, 2020 - 10:43:37 AM

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Olivier Pietquin, Fabio Tango. A Reinforcement Learning Approach to Optimize the longitudinal Behavior of a Partial Autonomous Driving Assistance System. ECAI 2012, Aug 2012, Montpellier, France. pp.987-992, ⟨10.3233/978-1-61499-098-7-987⟩. ⟨hal-00749436⟩

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