A Three-Level Game-Theoretic Decision-Making Framework for Autonomous Vehicles

In this paper, a three-level decision-making framework is developed to generate safe and effective decisions for autonomous vehicles (AVs). A key component in this decision framework is a normal-form game to capture the interactions between the ego vehicle and its surrounding vehicles. The payoffs in the normal-form game are designed to capture both safety reward and the reward gained by obeying (or the price paid by violating) ``soft'' traffic rules, e.g., first-come-first-go. This game formulation enables the ego to 1) make appropriate decisions considering the payoffs and possible actions of its surrounding vehicles, and 2) take intelligent actions in emergencies that may sacrifice some soft traffic rules to ensure safety. Moreover, we introduce parameters in the payoff matrix to tune the ego vehicle's behavior, e.g., aggressiveness level. A neural network is developed to learn the tuning parameters via supervised learning. In addition, to enable the ego to respond timely to different surrounding vehicles' driving styles, driving style characterization is incorporated into the payoff design for the normal-form game. Simulation studies are conducted to demonstrate the performance of the developed algorithms in two-vehicle intersection-crossing and lane-changing scenarios.