Tutorial on sampling-based POMDP-planning for automated driving

Bey H, Tratz M, Sackmann M, Lange A, Thielecke J (2020)


Publication Type: Conference contribution

Publication year: 2020

Publisher: SciTePress

Pages Range: 312-321

Conference Proceedings Title: VEHITS 2020 - Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems

ISBN: 9789897584190

Abstract

Behavior planning of automated vehicles entails many uncertainties. Partially Observable Markov Decision Processes (POMDP) are a mathematical framework suited for formulating the arising sequential decision problems. Solving POMDPs used to be intractable except for overly simplified examples, especially when execution time is of importance. Recent sampling-based solvers alleviated this problem by searching not for the exact but rather an approximated solution, and made POMDPs usable for many real-world applications. One of these algorithms is the Adaptive Belief Tree (ABT) algorithm which will be analyzed in this work. The scenario under consideration is an uncertain obstacle in the way of an automated vehicle. Following this example, the setup of POMDP and ABT is derived and the impact of important parameters is assessed in simulation. As such, this work provides a hands-on tutorial, giving insights and hints on how to overcome the pitfalls in using sampling-based POMDP solvers.

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How to cite

APA:

Bey, H., Tratz, M., Sackmann, M., Lange, A., & Thielecke, J. (2020). Tutorial on sampling-based POMDP-planning for automated driving. In Karsten Berns, Markus Helfert, Oleg Gusikhin (Eds.), VEHITS 2020 - Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems (pp. 312-321). SciTePress.

MLA:

Bey, Henrik, et al. "Tutorial on sampling-based POMDP-planning for automated driving." Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2020 Ed. Karsten Berns, Markus Helfert, Oleg Gusikhin, SciTePress, 2020. 312-321.

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