Modeling and Testing Multi-Agent Traffic Rules within Interactive Behavior Planning

TL;DR

This paper introduces a framework for modeling and testing multi-agent, time-dependent traffic rules within interactive behavior planning for autonomous vehicles, ensuring rule compliance in complex scenarios.

Contribution

It presents a novel method to incorporate history-dependent traffic rules into a dynamic game framework using Monte Carlo Tree Search, enabling rule-conformant planning.

Findings

Effective modeling of complex traffic rules in simulation.

Improved rule compliance in interactive merging scenarios.

Framework supports evaluation of rule impact on safety and progress.

Abstract

Autonomous vehicles need to abide by the same rules that humans follow. Some of these traffic rules may depend on multiple agents or time. Especially in situations with traffic participants that interact densely, the interactions with other agents need to be accounted for during planning. To study how multi-agent and time-dependent traffic rules shall be modeled, a framework is needed that restricts the behavior to rule-conformant actions during planning, and that can eventually evaluate the satisfaction of these rules. This work presents a method to model the conformance to traffic rules for interactive behavior planning and to test the ramifications of the traffic rule formulations on metrics such as collision, progress, or rule violations. The interactive behavior planning problem is formulated as a dynamic game and solved using Monte Carlo Tree Search, for which we contribute a new method to integrate history-dependent traffic rules into a decision tree. To study the effect of the rules, we treat it as a multi-objective problem and apply a relaxed lexicographical ordering to the vectorized rewards. We demonstrate our approach in a merging scenario. We evaluate the effect of modeling and combining traffic rules to the eventual compliance in simulation. We show that with our approach, interactive behavior planning while satisfying even complex traffic rules can be achieved. Moving forward, this gives us a generic framework to formalize traffic rules for autonomous vehicles.