Double Deep Q-Networks Based Game-Theoretic Equilibrium Control of Automated Vehicles at Autonomous Intersection

Optimizing the efficiency of traffic flow while minimizing fuel consumption is of significant importance in the context of resource scarcity and environmental preservation. Currently, the two-layer optimization strategy has been employed in autonomous intersection cooperation problems. The traffic efficiency is optimized on the first layer and energy consumption is optimized on the second layer based on an optimal timetable gained in the first layer. This operation prioritizes traffic efficiency over energy consumption, which may present a limitation in terms of equilibrating them. This paper develops an equilibrium control strategy for autonomous intersection. This control strategy includes vehicle schedule and equilibrium control. A schedule algorithm is initially proposed for platoons, in which the most passing sequence is gained when considering platoon formation. Then, a game deep reinforcement learning model is designed, and an equilibrating control algorithm is proposed, in which equilibrium state can be gained between traffic efficiency and energy consumption. Simulation results demonstrate that the proposed method can equilibrate traffic efficiency and energy consumption to an equilibrium state, as well as reducing trip cost compared with existing methods.