A Game-Theoretic Strategy-Aware Interaction Algorithm with Validation on Real Traffic Data

Interactive decision-making and motion planning are important to safety-critical autonomous agents, particularly when they interact with humans. Many different interaction strategies can be exploited by humans. For instance, they might ignore the autonomous agents, or might behave as selfish optimizers by treating the autonomous agents as opponents, or might assume themselves as leaders and the autonomous agents as followers who should take responsive actions. Different interaction strategies can lead to quite different closed-loop dynamics, and misalignment between the human's policy and the autonomous agent's belief over the policy will severely impact both safety and efficiency. Moreover, a human's interaction policy can change as interaction goes on. Hence, autonomous agents need to be aware of such uncertainties on the human policy, and integrate such information into their decision-making and motion planning algorithms. In this paper, we propose a policy-aware interaction strategy based on game theory. The goal is to allow autonomous agents to estimate humans' interactive policies and respond consequently. We validate the proposed algorithm with a roundabout scenario with real traffic data. The results show that the proposed algorithm can yield trajectories that are more similar to the ground truth than those with fixed policies. Also, we estimate how humans adjust their interaction strategies statistically based on the proposed algorithm.