Encoding Defensive Driving as a Dynamic Nash Game

TL;DR

This paper introduces a novel dynamic game-theoretic approach to encoding safety in autonomous driving by modeling an adversarial distraction phase, improving robustness against dangerous agent behaviors.

Contribution

It proposes a new formulation of robustness using a dynamic game with an adversarial distraction phase, addressing limitations of existing methods.

Findings

Effective safety encoding in traffic scenarios

Robust trajectories against dangerous behaviors

Improved safety in autonomous navigation

Abstract

Robots deployed in real-world environments should operate safely in a robust manner. In scenarios where an "ego" agent navigates in an environment with multiple other "non-ego" agents, two modes of safety are commonly proposed -- adversarial robustness and probabilistic constraint satisfaction. However, while the former is generally computationally intractable and leads to overconservative solutions, the latter typically relies on strong distributional assumptions and ignores strategic coupling between agents. To avoid these drawbacks, we present a novel formulation of robustness within the framework of general-sum dynamic game theory, modeled on defensive driving. More precisely, we prepend an adversarial phase to the ego agent's cost function. That is, we prepend a time interval during which other agents are assumed to be temporarily distracted, in order to render the ego agent's equilibrium trajectory robust against other agents' potentially dangerous behavior during this time. We demonstrate the effectiveness of our new formulation in encoding safety via multiple traffic scenarios.