Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 23, Issue 11, Pages 22549-22562Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2022.3209607
Keywords
Decision making; Games; Safety; Risk management; Roads; Computational modeling; Q-learning; Decision making; connected automated vehicles; fuzzy coalitional game; driving risk assessment; social and individual benefits; unsignalized intersection
Categories
Funding
- A*STAR [1922500046]
- Nanyang Technological University, Singapore, through the Start-Up Grant (SUG)-Nanyang Assistant Professorship (NAP) Grant [M4082268.050]
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This paper proposes a game-theoretic decision-making framework to address the coordination issue of connected automated vehicles (CAVs) in urban scenarios. By designing algorithms for driving risk assessment, decision-making cost function, and decision-making constraints, collaborative decisions of vehicles at unsignalized intersections are achieved.
To address the coordination issue of connected automated vehicles (CAVs) at urban scenarios, a game-theoretic decision-making framework is proposed that can advance social benefits, including the traffic system efficiency and safety, as well as the benefits of individual users. Under the proposed decision-making framework, in this work, a representative urban driving scenario, i.e. the unsignalized intersection, is investigated. Once the vehicle enters the focused zone, it will interact with other CAVs and make collaborative decisions. To evaluate the safety risk of surrounding vehicles and reduce the complexity of the decision-making algorithm, the driving risk assessment algorithm is designed with a Gaussian potential field approach. The decision-making cost function is constructed by considering the driving safety and passing efficiency of CAVs. Additionally, decision-making constraints are designed and include safety, comfort, efficiency, control and stability. Based on the cost function and constraints, the fuzzy coalitional game approach is applied to the decision-making issue of CAVs at unsignalized intersections. Two types of fuzzy coalitions are constructed that reflect both individual and social benefits. The benefit allocation in the two types of fuzzy coalitions is associated with the driving aggressiveness of CAVs. Finally, the effectiveness and feasibility of the proposed decision-making framework are verified with three test cases.
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