A Reinforcement Learning Approach for Designing and Optimizing Interaction Strategies for a Human–Machine Interface of a PADAS
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). In this paper, the design of the PADAS HMI as well as a decision-theoretic approach for deriving an optimal WIS are described.
KeywordsHuman machine interface Decision making support systems Machine learning / reinforcement learning Partially autonomous driving assistance systems Optimal strategies
The research leading to these results has received funding from the European Commission Seventh Framework Programme (FP7/2007–2013) under grant agreement no. FP7-218552, Project ISi-PADAS (Integrated Human Modelling and Simulation to support Human Error Risk Analysis of Partially Autonomous Driver Assistance Systems). The authors would like to specially thank the ISi-PADAS consortium that has supported the development of this research.
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