Reasoning Multi-Agent Behavioral Topology for Interactive Autonomous Driving

TL;DR

This paper introduces BeTop, a topological framework based on braid theory, to improve multi-agent behavioral modeling for autonomous driving, enhancing prediction and planning consistency amid interaction uncertainties.

Contribution

It proposes a novel Behavioral Topology (BeTop) derived from braid theory and a synergistic learning framework (BeTopNet) to better model multi-agent interactions in autonomous driving.

Findings

Achieves state-of-the-art results on nuPlan and WOMD datasets.

Demonstrates improved behavioral consistency and planning compliance.

Effectively manages behavioral uncertainty in multi-agent scenarios.

Abstract

Autonomous driving system aims for safe and social-consistent driving through the behavioral integration among interactive agents. However, challenges remain due to multi-agent scene uncertainty and heterogeneous interaction. Current dense and sparse behavioral representations struggle with inefficiency and inconsistency in multi-agent modeling, leading to instability of collective behavioral patterns when integrating prediction and planning (IPP). To address this, we initiate a topological formation that serves as a compliant behavioral foreground to guide downstream trajectory generations. Specifically, we introduce Behavioral Topology (BeTop), a pivotal topological formulation that explicitly represents the consensual behavioral pattern among multi-agent future. BeTop is derived from braid theory to distill compliant interactive topology from multi-agent future trajectories. A synergistic learning framework (BeTopNet) supervised by BeTop facilitates the consistency of behavior prediction and planning within the predicted topology priors. Through imitative contingency learning, BeTop also effectively manages behavioral uncertainty for prediction and planning. Extensive verification on large-scale real-world datasets, including nuPlan and WOMD, demonstrates that BeTop achieves state-of-the-art performance in both prediction and planning tasks. Further validations on the proposed interactive scenario benchmark showcase planning compliance in interactive cases.