A Reinforcement Learning approach for designing and optimizing interaction strategies for a Human-Machine Interface of a Partially Autonomous Driver Assistance System - CentraleSupélec Access content directly
Conference Papers Year : 2011

A Reinforcement Learning approach for designing and optimizing interaction strategies for a Human-Machine Interface of a Partially Autonomous Driver Assistance System

Fabio Tango
  • Function : Author
Maria Alonso
  • Function : Author
Raghav Aras
  • Function : Author
  • PersonId : 830439

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).

Dates and versions

hal-00618342 , version 1 (01-09-2011)

Identifiers

Cite

Fabio Tango, Maria Alonso, Henar Maria Vega, Raghav Aras, Olivier Pietquin. A Reinforcement Learning approach for designing and optimizing interaction strategies for a Human-Machine Interface of a Partially Autonomous Driver Assistance System. HMAT 2010, Jun 2010, Belgirate, Italy. pp.353-361, ⟨10.1007/978-88-470-1821-1_38⟩. ⟨hal-00618342⟩
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