TY  - JOUR
PY  - 2023//
TI  - Hierarchical and game-theoretic decision-making for connected and automated vehicles in overtaking scenarios
JO  - Transportation research part C: emerging technologies
A1  - Ji, Kyoungtae
A1  - Li, Nan
A1  - Orsag, Matko
A1  - Han, Kyoungseok
SP  - e104109
EP  - e104109
VL  - 150
IS  - 
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 0968-090X
UR  - http://dx.doi.org/10.1016/j.trc.2023.104109
ID  - ref1
ER  -