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To improve the safety and efficiency of the intelligent transportationsystem, particularly in complex urban scenarios, in this paper a game theoreticdecision-making framework is designed for connected automated vehicles (CAVs)at unsignalized roundabouts considering their personalized driving behaviours.Within the decision-making framework, a motion prediction module is designedand optimized using model predictive control (MPC) to enhance the effectivenessand accuracy of the decision-making algorithm. Besides, the payoff function ofdecision making is defined with the consideration of vehicle safety, ridecomfort and travel efficiency. Additionally, the constraints of thedecision-making problem are constructed. Based on the establisheddecision-making model, Stackelberg game and grand coalition game approaches areadopted to address the decision making of CAVs at an unsignalized roundabout.Three testing cases considering personalized driving behaviours are carried outto verify the performance of the developed decision-making algorithms. Thetesting results show that the proposed game theoretic decision-making frameworkis able to make safe and reasonable decisions for CAVs in the complex urbanscenarios, validating its feasibility and effectiveness. Stackelberg gameapproach shows its advantage in guaranteeing personalized driving objectives ofindividuals, while the grand coalition game approach is advantageous regardingthe efficiency improvement of the transportation system.

Decision Making of Connected Automated Vehicles at An Unsignalized Roundabout Considering Personalized Driving Behaviours