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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alsen J, et al (2010). Integrated human modelling and simulation to support human error risk analysis of partially autonomous driver assistance systems: the ISI-PADAS project. Transport Research Arena Europe, Brussels
Puterman ML (1994) Markov decision processes: discrete stochastic dynamic programming. Wiley, New York
Bradtke SJ, Barto AG (1996). Linear least-squares algorithms for temporal difference learning. J Mach Learn Res 22(2):33–57
Sutton R, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge
Muhrer E, Vollrath M (2009) Final report for task 1.22–results from accident analysis. Technical report, ISI-PADAS project
Vollrath M et al (2006) Ableitung von anforderungen an ein fahrerassistenzsystem aus sicht der verkehrssicherheit, berichte der bundesanstalt für strassenwesen, fahrzeugtechnik, heft F 60. Wirtschaftsverlag NW, Bremerhaven, in German language
Briest S, Vollrath M (2006) In welchen situationen machen fahrer welche fehler? Ableitung von anforderungen an fahrerassistenzsysteme durch, in-depth-unfallanalysen. In VDI (ed), Integrierte sicherheit und fahrerassistenzsysteme. VDI, Wolfsburg, pp 449–463, in German language
Kiefer RJ, Salinger J, Ference JJ (2005) Status of NHTSA’s rear-end crash prevention research program, National Highway Traffic and Safety Administration, paper number 05-0282
Lee JD et al (2002) Collision warning timing, driver distraction and driver response to imminent rear-end collisions in a high-fidelity driving simulator. Hum Factors J 44:314–334
Boyan J (1999) Technical update: LSTD. J Mach Learn Res 49(2-3):233–246
Atkeson CG, Santamaria JC (1997) A comparison of direct and model-based reinforcement learning. In the proceedings of the international conference on robotics and automation (ICRA)
Lagoudakis M, Parr R (2003) Least squares policy iteration. J AI Res 4:1107–1149
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Italia Srl
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-88-470-1821-1_12
Published:
Publisher Name: Springer, Milano
Print ISBN: 978-88-470-1820-4
Online ISBN: 978-88-470-1821-1
eBook Packages: EngineeringEngineering (R0)