Recognize then Resolve: A Hybrid Framework for Understanding Interaction and Cooperative Conflict Resolution in Mixed Traffic

2025-01-31

AI Summary

In mixed traffic involving connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs), weak intent understanding and difficulty in resolving interactive conflicts hinder safe and efficient coordination. Method: This paper proposes a two-stage “identification–resolution” framework: (1) a novel Bilateral Intent Progression Graph (BIPG) to model interactive dynamics and precisely identify three types of interaction failure scenarios along with their critical triggering moments; (2) a constraint-aware Monte Carlo Tree Search (MCTS) algorithm for lightweight, intent-aware cooperative decision-making. Contribution/Results: The method significantly improves safety and throughput across diverse CAV penetration rates, approaching the performance of ideal cooperative control while drastically reducing computational overhead. The source code and benchmark dataset are publicly released.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Coordination and Collaboration

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

A lack of understanding of interactions and the inability to effectively resolve conflicts continue to impede the progress of Connected Autonomous Vehicles (CAVs) in their interactions with Human-Driven Vehicles (HDVs). To address this challenge, we propose the Recognize then Resolve (RtR) framework. First, a Bilateral Intention Progression Graph (BIPG) is constructed based on CAV-HDV interaction data to model the evolution of interactions and identify potential HDV intentions. Three typical interaction breakdown scenarios are then categorized, and key moments are defined for triggering cooperative conflict resolution. On this basis, a constrained Monte Carlo Tree Search (MCTS) algorithm is introduced to determine the optimal passage order while accommodating HDV intentions. Experimental results demonstrate that the proposed RtR framework outperforms other cooperative approaches in terms of safety and efficiency across various penetration rates, achieving results close to consistent cooperation while significantly reducing computational resources. Our code and data are available at: https://github.com/FanGShiYuu/RtR-Recognize-then-Resolve/.

Problem

• Autonomous Vehicles
• Human-Driven Vehicles
• Interaction Challenges

Innovation

• RtR Framework
• Bilateral Intent Progression Graph (BIPG)
• Constrained Monte Carlo Tree Search (MCTS)