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To address the coordination issue of connected automated vehicles (CAVs) aturban scenarios, a game-theoretic decision-making framework is proposed thatcan advance social benefits, including the traffic system efficiency andsafety, as well as the benefits of individual users. Under the proposeddecision-making framework, in this work, a representative urban drivingscenario, i.e. the unsignalized intersection, is investigated. Once the vehicleenters the focused zone, it will interact with other CAVs and makecollaborative decisions. To evaluate the safety risk of surrounding vehiclesand reduce the complexity of the decision-making algorithm, the driving riskassessment algorithm is designed with a Gaussian potential field approach. Thedecision-making cost function is constructed by considering the driving safetyand passing efficiency of CAVs. Additionally, decision-making constraints aredesigned and include safety, comfort, efficiency, control and stability. Basedon the cost function and constraints, the fuzzy coalitional game approach isapplied to the decision-making issue of CAVs at unsignalized intersections. Twotypes of fuzzy coalitions are constructed that reflect both individual andsocial benefits. The benefit allocation in the two types of fuzzy coalitions isassociated with the driving aggressiveness of CAVs. Finally, the effectivenessand feasibility of the proposed decision-making framework are verified withthree test cases.

Decision Making for Connected Automated Vehicles at Urban Intersections Considering Social and Individual Benefits