Stochastic Modeling and Optimal Control for Automated Overtaking
2019 (English)In: Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 1273-1278Conference paper, Published paper (Refereed)
Abstract [en]
This paper proposes a solution to the overtaking problem where an automated vehicle tries to overtake a human-driven vehicle, which may not be moving at a constant velocity. Using reachability theory, we first provide a robust time-optimal control algorithm to guarantee that there is no collision throughout the overtaking process. Following the robust formulation, we provide a stochastic reachability formulation that allows a trade-off between the conservative overtaking time and the allowance of a small collision probability. To capture the stochasticity of a human driver's behavior, we propose a new martingale-based model where we classify the human driver as aggressive or nonaggressive. We show that if the human driver is nonaggressive, our stochastic time-optimal control algorithm can provide a shorter overtaking time than our robust algorithm, whereas if the human driver is aggressive, the stochastic algorithm will act on a collision probability of zero, which will match the robust algorithm. Finally, we detail a simulated example that illustrates the effectiveness of the proposed algorithms.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2019. p. 1273-1278
Keywords [en]
Behavioral research, Economic and social effects, Stochastic control systems, Transport properties, Automated vehicles, Collision probability, Constant velocities, Optimal controls, Reachability theory, Robust formulations, Stochastic algorithms, Time optimal control, Stochastic systems
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-274082DOI: 10.1109/CDC40024.2019.9029505ISI: 000560779001039Scopus ID: 2-s2.0-85082496462OAI: oai:DiVA.org:kth-274082DiVA, id: diva2:1451199
Conference
58th IEEE Conference on Decision and Control, CDC 2019, 11 December 2019 through 13 December 2019
Note
QC 20200702
Part of ISBN 9781728113982
2020-07-022020-07-022024-10-23Bibliographically approved