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/.

翻译：对交互行为理解的缺失以及有效解决冲突能力的不足，持续阻碍着网联自动驾驶车辆（CAV）在与人类驾驶车辆（HDV）交互过程中的发展。为应对这一挑战，我们提出了“识别后解决”（RtR）框架。首先，基于CAV-HDV交互数据构建双边意图演进图（BIPG），以建模交互的演化过程并识别潜在的HDV意图。随后，对三种典型的交互失效场景进行分类，并定义了触发协同冲突解决的关键时刻。在此基础上，引入一种带约束的蒙特卡洛树搜索（MCTS）算法，在适应HDV意图的同时确定最优通行顺序。实验结果表明，所提出的RtR框架在不同渗透率下，在安全性和效率方面均优于其他协同方法，在显著减少计算资源的同时，取得了接近持续协同的效果。我们的代码与数据公开于：https://github.com/FanGShiYuu/RtR-Recognize-then-Resolve/。