Intention Communication and Hypothesis Likelihood in Game-Theoretic Motion Planning

TL;DR

This paper introduces a fault-tolerant game-theoretic motion planner for multi-robot systems that uses inter-agent communication and Bayesian filtering to infer intentions and ensure safety despite communication faults.

Contribution

It proposes a novel receding horizon planner that incorporates intention hypothesis likelihood and real-time Bayesian inference to handle communication faults in multi-agent motion planning.

Findings

Successfully demonstrated safe trajectory generation in autonomous driving scenarios

Effectively infers agent objectives despite communication faults

Enhances safety and robustness of multi-robot systems

Abstract

Game-theoretic motion planners are a potent solution for controlling systems of multiple highly interactive robots. Most existing game-theoretic planners unrealistically assume a priori objective function knowledge is available to all agents. To address this, we propose a fault-tolerant receding horizon game-theoretic motion planner that leverages inter-agent communication with intention hypothesis likelihood. Specifically, robots communicate their objective function incorporating their intentions. A discrete Bayesian filter is designed to infer the objectives in real-time based on the discrepancy between observed trajectories and the ones from communicated intentions. In simulation, we consider three safety-critical autonomous driving scenarios of overtaking, lane-merging and intersection crossing, to demonstrate our planner's ability to capitalize on alternative intention hypotheses to generate safe trajectories in the presence of faulty transmissions in the communication network.