Abstract
This paper addresses the problem to find an optimal warning and intervention strategy for a partially autonomous driver’s assistance system. Here, an optimal strategy is regarded as the one minimizing the risk of collision with an obstacle ahead, while keeping the number of warnings and interventions as low as possible, in order to support the driver and avoid distraction or annoyance. A novel approach to this problem is proposed, based on the solution of a sequential decision making problem.
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Acknowledgment
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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© 2011 Springer-Verlag Italia Srl
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Tango, F., Aras, R., Pietquin, O. (2011). Learning Optimal Control Strategies from Interactions with a PADAS. In: Cacciabue, P., Hjälmdahl, M., Luedtke, A., Riccioli, C. (eds) Human Modelling in Assisted Transportation. Springer, Milano. https://doi.org/10.1007/978-88-470-1821-1_12
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DOI: https://doi.org/10.1007/978-88-470-1821-1_12
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