AI 中文总结
针对当前联网自动驾驶车辆缺乏全局一致、意图感知协调决策机制的问题，提出MIND-CAVs框架，车辆将传感器观测抽象为意图表示交换，边缘智能体协商冲突意图，实验表明该框架在多车道场景中相比基线有改善。
AI 中文摘要
现代自动驾驶车辆大多作为孤立的智能体运行，依赖车载感知和决策模块，仅广播低级运动状态的基本安全消息。现有合作驾驶框架虽能实现有限的传感器共享，但很少传达高级机动意图，边缘计算主要用于内容交付而非决策仲裁。因此，当前的联网自主缺乏一种机制来做出跨车辆的全局一致、意图感知的协调决策。为填补这一空白，我们提出了MIND-CAVs，一种基于意图驱动自主的联网自动驾驶车辆（CAV）多智能体协商与决策框架。每辆车将原始传感器观测抽象为结构化意图表示，通过V2X链路进行交换，并从路边边缘服务器接收全局一致的协调计划。边缘智能体结合学习和基于规则的仲裁机制来协商车辆间冲突的意图，而云平台记录决策以供审计和持续再训练。我们在基于CARLA的AI在环平台上实现了MIND-CAVs，并在涉及冲突机动和路线受限出口的多车道高速公路场景中对其进行评估。实验结果表明，与孤立自主、先到先服务仲裁和多智能体强化学习基线相比，MIND-CAVs在机动完成时间、不安全接近度和不必要制动方面都有改善。
英文摘要
Modern autonomous vehicles largely operate as isolated agents: they rely on on-board perception and decision modules and broadcast Basic Safety Messages (BSMs) that expose only low-level kinematic state. While existing cooperative driving frameworks enable limited sensor sharing, they rarely communicate high-level maneuver intentions, and edge computing is primarily used for content delivery rather than decision arbitration. As a result, current connected autonomy lacks a principled mechanism for making globally consistent, intent-aware coordination decisions across vehicles. To address this gap, we propose MIND-CAVs, a Multi-Intelligence Negotiation and Decision framework for connected autonomous vehicles (CAVs) based on intent-driven autonomy. Each vehicle abstracts raw sensor observations into structured intent representations, exchanges them over V2X links, and receives globally consistent coordination plans from roadside edge servers. Edge agents combine learned and rule-based arbitration mechanisms to negotiate conflicting intents among vehicles, while a cloud platform records decisions for auditing and continual retraining. We implement MIND-CAVs in a CARLA-based AI-in-the-loop platform and evaluate it in multi-lane highway scenarios involving conflicting maneuvers and route-constrained exits. Experimental results show improved maneuver completion time and reduced unsafe proximity and unnecessary braking compared with isolated autonomy, first-come-first-served arbitration, and multi-agent reinforcement learning baselines.
Comments8 pages, 5 figures, 1 table
