A Game Theoretic Decision-Making Framework With Conflict-Aware Nash Equilibrium Selection for Autonomous Vehicles at Uncontrolled Intersections

Uncontrolled intersections, as a typical urban traffic environment, are challenging scenarios for autonomous driving due to the potential conflicts and lack of co-ordination between traffic participants. Even if both vehicles are rational players, the existence of multiple Nash equilibira in this dynamic game may result in no consensus between them during the interaction. This may lead to unsmooth rides with late emergency brakes. This paper proposes a conflict-aware game theoretic decision-making framework for autonomous vehicles at uncontrolled intersections considering the Nash equilibrium selection. Combining dynamic programming and iterative best response, a dynamic closed-loop Nash equilibrium calculating method is proposed. The issue of multiple equilibria is analyzed. An equilibrium selection mechanism is designed based on sequentially-constructed Conflict-Aware Bimatrix Games (CAB-Games) and the idea of risk dominant equilibria. The proposed policy provides a relatively smooth drive without being too aggressive or conservative. It is demonstrated in simulations and driving simulator experiments that the policy can interact well with other autonomous driving policies as well as human drivers.