Integrated Decision Making and Trajectory Planning for Autonomous Driving Under Multimodal Uncertainties: A Bayesian Game Approach

TL;DR

This paper presents a Bayesian game-based framework for integrated decision making and trajectory planning in autonomous driving, effectively handling multimodal uncertainties and interactions among traffic agents.

Contribution

It introduces a novel Bayesian game approach with no-regret learning to model multi-agent interactions and plans trajectories considering behavioral uncertainties.

Findings

The framework captures multimodal interactions effectively.

Simulations demonstrate improved safety and decision quality.

The approach generalizes across different traffic scenarios.

Abstract

Modeling the interaction between traffic agents is a key issue in designing safe and non-conservative maneuvers in autonomous driving. This problem can be challenging when multi-modality and behavioral uncertainties are engaged. Existing methods either fail to plan interactively or consider unimodal behaviors that could lead to catastrophic results. In this paper, we introduce an integrated decision-making and trajectory planning framework based on Bayesian game (i.e., game of incomplete information). Human decisions inherently exhibit discrete characteristics and therefore are modeled as types of players in the game. A general solver based on no-regret learning is introduced to obtain a corresponding Bayesian Coarse Correlated Equilibrium, which captures the interaction between traffic agents in the multimodal context. With the attained equilibrium, decision-making and trajectory planning are performed simultaneously, and the resulting interactive strategy is shown to be optimal over the expectation of rivals' driving intentions. Closed-loop simulations on different traffic scenarios are performed to illustrate the generalizability and the effectiveness of the proposed framework.