多层车路协同框架提升复杂路口通行安全与效率
A Vehicle-Infrastructure Multi-layer Cooperative Decision-making Framework
- 基于车辆状态量化影响并聚类分组，构建动态协作网络
- 引入大模型协商机制，实现车群间通行顺序优化，减少冲突
- 仿真验证有效降低协商复杂度，符合自然驾驶逻辑
自动驾驶已进入测试阶段，但单个车辆算法决策能力有限，在复杂场景下安全性与效率问题凸显。随着车联网通信技术发展，具备连接能力的自动驾驶车辆可通过车-车（V2V）和车-基础设施（V2I）通信，缓解个体决策局限。本文提出一种面向无信号交叉口复杂冲突场景的多层级车路协同决策框架。首先，基于车辆状态定义车辆影响量化方法及其传播关系，利用累积影响通过基于基序的图聚类对车辆进行分组。其次，在组内与组间采用基于大语言模型（LLM）的通行顺序协商机制，确定车辆通行顺序并生成规划动作。消融实验的仿真结果表明，该方法显著降低协商复杂度，保障车辆在交叉口更安全、高效通行，且符合自然决策逻辑。
原文摘要 · Abstract (English)
Autonomous driving has entered the testing phase, but due to the limited decision-making capabilities of individual vehicle algorithms, safety and efficiency issues have become more apparent in complex scenarios. With the advancement of connected communication technologies, autonomous vehicles equipped with connectivity can leverage vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, offering a potential solution to the decision-making challenges from individual vehicle's perspective. We propose a multi-level vehicle-infrastructure cooperative decision-making framework for complex conflict scenarios at unsignalized intersections. First, based on vehicle states, we define a method for quantifying vehicle impacts and their propagation relationships, using accumulated impact to group vehicles through motif-based graph clustering. Next, within and between vehicle groups, a pass order negotiation process based on Large Language Models (LLM) is employed to determine the vehicle passage order, resulting in planned vehicle actions. Simulation results from ablation experiments show that our approach reduces negotiation complexity and ensures safer, more efficient vehicle passage at intersections, aligning with natural decision-making logic.
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