Achieving both safety guarantees and real-time performance in cooperative vehicle coordination remains a fundamental challenge, particularly in dynamic and uncertain environments. Existing methods often suffer from insufficient uncertainty treatment in safety modeling, which intertwines with the heavy computational burden under complex multi-vehicle coupling. This paper presents a novel coordination framework that resolves this challenge through three key innovations: 1) direct control of vehicles' trajectory distributions during coordination, formulated as a robust cooperative planning problem with adaptive enhanced safety constraints, ensuring a specified level of safety regarding the uncertainty of the interactive trajectory, 2) a fully parallel ADMM-based distributed trajectory negotiation (ADMM-DTN) algorithm that efficiently solves the optimization problem while allowing configurable negotiation rounds to balance solution quality and computational resources, and 3) an interactive attention mechanism that selectively focuses on critical interactive participants to further enhance computational efficiency. Simulation results demonstrate that our framework achieves significant advantages in safety (reducing collision rates by up to 40.79\% in various scenarios) and real-time performance compared to representative benchmarks, while maintaining strong scalability with increasing vehicle numbers. The proposed interactive attention mechanism further reduces the computational demand by 15.4\%. Real-world experiments further validate robustness and real-time feasibility with unexpected dynamic obstacles, demonstrating reliable coordination in complex traffic scenes. The experiment demo could be found at https://youtu.be/4PZwBnCsb6Q.

翻译：同时实现网联自动驾驶车辆协同中的安全保障与实时性能仍是根本性挑战，尤其在动态不确定环境下。现有方法在安全建模中对不确定性处理不足，且复杂多车耦合导致计算负担沉重。本文提出一种新型协同框架，通过三项关键创新解决该问题：1) 直接控制车辆协同过程中的轨迹分布，将其构建为具有自适应增强安全约束的鲁棒协同规划问题，确保交互轨迹不确定性下的指定安全等级；2) 基于全并行ADMM的分布式轨迹协商算法(ADMM-DTN)，可高效求解优化问题，并支持通过可配置的协商轮次平衡解质量与计算资源；3) 交互注意力机制，通过选择性聚焦关键交互参与者进一步提升计算效率。仿真结果表明，与代表性基准方法相比，本框架在各类场景下碰撞率降低达40.79%，并显著提升实时性能，且随车辆数量增加保持强可扩展性。所提交互注意力机制可进一步降低15.4%的计算需求。真实世界实验验证了面对突发动态障碍物时的鲁棒性与实时可行性，展示了复杂交通场景下的可靠协同。实验演示视频见https://youtu.be/4PZwBnCsb6Q。