Game-theoretic Objective Space Planning

Abstract

Autonomous Racing awards agents that react to opponents' behaviors with agile maneuvers towards progressing along the track while penalizing both over-aggressive and overconservative agents. Understanding the intent of other agents is crucial to deploying autonomous systems in adversarial multiagent environments. Current approaches either oversimplify the discretization of the action space of agents or fail to recognize the long-term effect of actions and become myopic. Our work focuses on addressing these two challenges. First, we propose a novel dimension reduction method that encapsulates diverse agent behaviors while conserving the continuity of agent actions. Second, we formulate the two-agent racing game as a regret minimization problem and provide a solution for tractable counterfactual regret minimization with a regret prediction model. Finally, we validate our findings experimentally on scaled autonomous vehicles. We demonstrate that using the proposed game-theoretic planner using agent characterization with the objective space significantly improves the win rate against different opponents, and the improvement is transferable to unseen opponents in an unseen environment.