Handling Prediction Model Errors in Planning for Automated Driving Using POMDPs

Bey H, Sackmann M, Lange A, Thielecke J (2021)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2021

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2021-September

Pages Range: 439--446

Conference Proceedings Title: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

Event location: Hybrid / Indianapolis US

ISBN: 9781728191423

URI: https://ieeexplore.ieee.org/abstract/document/9564408

DOI: 10.1109/ITSC48978.2021.9564408

Abstract

Decision making under uncertainty is a key challenge in automated driving. A source of uncertainty is the unknown driving behavior of other traffic participants surrounding the automated vehicle. To take this uncertainty into account, we formulate the planning problem as a Partially Observable Markov Decision Process (POMDP) and solve it with the state-of-the-art sampling-based POMCP(OW) solver. Within planning, a derivative of the Intelligent Driver Model (IDM) is used to predict other traffic participants. For evaluation, the POMDP planner runs in a closed-loop simulation environment where the other traffic participants are controlled by machine-learned driver models. The different models used in planning and simulation constitute a true prediction model error. Compared to a classical search-based planner (and reactive driver models), the POMDP leads to less critical situations while retaining the comfort and efficiency of foresighted planning. Both planning approaches apply a particle filter to estimate others' driving characteristics-but only the POMDP approach is able to consider the whole range of possible characteristics. Several variants of the POMDP solver were evaluated, allowing for different weighting of safety vs. comfort/efficiency.

Authors with CRIS profile

Henrik Bey Professur für Informationstechnik (Schwerpunkt Ortsbestimmung und Navigation) Moritz Sackmann Professur für Informationstechnik (Schwerpunkt Ortsbestimmung und Navigation) Jörn Thielecke Professur für Informationstechnik (Schwerpunkt Ortsbestimmung und Navigation)

Involved external institutions

Audi AG DE Germany (DE)

How to cite

APA:

Bey, H., Sackmann, M., Lange, A., & Thielecke, J. (2021). Handling Prediction Model Errors in Planning for Automated Driving Using POMDPs. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) (pp. 439--446). Hybrid / Indianapolis, US: Institute of Electrical and Electronics Engineers Inc..

MLA:

Bey, Henrik, et al. "Handling Prediction Model Errors in Planning for Automated Driving Using POMDPs." Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference, ITSC 2021, Hybrid / Indianapolis Institute of Electrical and Electronics Engineers Inc., 2021. 439--446.

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