Hierarchical and game-theoretic decision-making for connected and automated vehicles in overtaking scenarios. Ji, K., Li, N., Orsag, M., & Han, K. Transportation Research Part C: Emerging Technologies, 150:104109, 2023. ![Hierarchical and game-theoretic decision-making for connected and automated vehicles in overtaking scenarios [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This paper presents a hierarchical and game-theoretic decision-making strategy for connected and automated vehicles (CAVs). A CAV can receive preview information using vehicle-to-everything (V2X) communication systems, and the optimal short- and long-term trajectory can be planned using this information. Specifically, in this study, the aggressiveness of all preceding vehicles in the car-following scenario can be estimated globally by monitoring the history of their time-series behaviors, before the CAV initiates a particular action, which is performed at the upper layer of the proposed decision-making structure. If it is determined that initiating a specific action is advantageous, the action is initiated, and the CAV then interacts with the vehicles locally to achieve its driving goal in a game-theoretical manner at the lower layer. In multiple test scenarios, we demonstrate the usefulness of our approach compared to the conventional decision-making approaches, and it shows a significant improvement in terms of success rates.
@article{Ji2023_transportation,
title = {Hierarchical and game-theoretic decision-making for connected and automated vehicles in overtaking scenarios},
journal = {Transportation Research Part C: Emerging Technologies},
volume = {150},
pages = {104109},
year = {2023},
issn = {0968-090X},
doi = {https://doi.org/10.1016/j.trc.2023.104109},
url = {https://www.sciencedirect.com/science/article/pii/S0968090X23000980},
author = {Kyoungtae Ji and Nan Li and Matko Orsag and Kyoungseok Han},
keywords = {Connected and automated vehicles, Game theory, Leader–follower game, Autonomous driving},
abstract = {This paper presents a hierarchical and game-theoretic decision-making strategy for connected and automated vehicles (CAVs). A CAV can receive preview information using vehicle-to-everything (V2X) communication systems, and the optimal short- and long-term trajectory can be planned using this information. Specifically, in this study, the aggressiveness of all preceding vehicles in the car-following scenario can be estimated globally by monitoring the history of their time-series behaviors, before the CAV initiates a particular action, which is performed at the upper layer of the proposed decision-making structure. If it is determined that initiating a specific action is advantageous, the action is initiated, and the CAV then interacts with the vehicles locally to achieve its driving goal in a game-theoretical manner at the lower layer. In multiple test scenarios, we demonstrate the usefulness of our approach compared to the conventional decision-making approaches, and it shows a significant improvement in terms of success rates.}
}
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