How can CAVs cooperatively resolve conflicts with HDVs?
Recognize then Resolve: A Hybrid Framework for Understanding Interaction and Cooperative Conflict Resolution in Mixed Traffic
This paper introduces the Recognize then Resolve (RtR) framework for improving the safety and efficiency of autonomous vehicles (CAVs) interacting with human-driven vehicles (HDVs) at intersections. It addresses the challenge of CAVs understanding and reacting to unpredictable HDV behavior.
RtR uses a Bilateral Intention Progression Graph (BIPG) to model the interaction dynamics and predict HDV intentions (rush or yield) based on real-time data like time-to-collision. When the BIPG detects potential interaction breakdowns (uncertain, inefficient, or dangerous), a constrained Monte Carlo Tree Search (MCTS) algorithm determines the optimal passing order for all vehicles, respecting predicted HDV intentions. This targeted cooperative decision-making improves safety and efficiency compared to always cooperating or relying solely on single-vehicle decision-making. The focus on recognizing interaction patterns and incorporating predicted intentions is highly relevant to LLM-based multi-agent systems, where understanding and responding to complex agent interactions is crucial.