Multi-Hypothesis Interactions in Game-Theoretic Motion Planning

TL;DR

This paper introduces a new approach for autonomous vehicle motion planning that models multiple hypotheses about other agents' intentions, enabling more interactive and uncertainty-aware trajectory generation.

Contribution

It proposes a novel multi-hypothesis framework that explicitly incorporates uncertainty about other agents' objectives into game-theoretic motion planning.

Findings

Effectively models multiple agent intentions with probabilistic hypotheses.

Enables ego vehicle to adapt its behavior based on uncertainty levels.

Demonstrates improved interaction handling in dynamic scenarios.

Abstract

We present a novel method for handling uncertainty about the intentions of non-ego players in dynamic games, with application to motion planning for autonomous vehicles. Equilibria in these games explicitly account for interaction among other agents in the environment, such as drivers and pedestrians. Our method models the uncertainty about the intention of other agents by constructing multiple hypotheses about the objectives and constraints of other agents in the scene. For each candidate hypothesis, we associate a Bernoulli random variable representing the probability of that hypothesis, which may or may not be independent of the probability of other hypotheses. We leverage constraint asymmetries and feedback information patterns to incorporate the probabilities of hypotheses in a natural way. Specifically, increasing the probability associated with a given hypothesis from $0$ to $1$ shifts the responsibility of collision avoidance from the hypothesized agent to the ego agent. This method allows the generation of interactive trajectories for the ego agent, where the level of assertiveness or caution that the ego exhibits is directly related to the easy-to-model uncertainty it maintains about the scene.