Paper Info
Title
A Reinforcement Learning Approach to Optimize the longitudinal Behavior of a Partial Autonomous Driving Assistance System.
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.
Year
DOI
Venue
2012
10.3233/978-1-61499-098-7-987
FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS
Field
DocType
Volume
Driving simulator,Computer science,Simulation,Collision,Artificial intelligence,Action selection,Machine learning,Reinforcement learning
Conference
242
ISSN
Citations 
PageRank 
0922-6389
2
0.44
References 
Authors
2
2
Name
Order
Citations
PageRank
Olivier Pietquin166468.60
Fabio Tango2547.74