AI 中文总结
本文提出VeloCity框架，通过去中心化多智能体时空出行剖面规划，在四张真实城市地图上实现CAVs的高效无冲突交通管理，显著降低通行时间、约束延迟方差并避免拥堵死锁。
AI 中文摘要
随着现代城市面临日益严重的交通拥堵，网联自动驾驶汽车（CAVs）已成为下一代智能交通管理的关键支撑技术。然而，现有范式的局限阻碍了这一潜力的充分发挥：现有方法通常仅优化局部交互而非系统级效率，会产生严重的通信开销，或缺乏安全运动执行所需的确定性保证；此外，当前多智能体适配方案常局限于小型预设场景，无法扩展至大型复杂城市网络。为弥合这一差距，本文提出VeloCity，一种面向任意城市区域CAVs的去中心化多智能体时空出行剖面规划框架。为最小化车辆通行时间，VeloCity将出行剖面优化直接分配给单个CAVs；车辆向局部交通协调器查询预留表，独立计算自身最快无冲突出行剖面，并向协调器预留所请求的时空时隙。该框架可天然适配任意道路拓扑，无需场景特定调参即可管理高度不规则的城市区域，同时保证车辆轨迹无碰撞且符合物理可执行性。在东京、曼哈顿、罗马、博洛尼亚四张大规模真实城市地图上开展的大量仿真验证了该框架的可扩展性：与现有最先进模型相比，VeloCity的通行时间大幅降低，延迟方差被严格约束，即使在极高车辆密度下也能成功避免拥堵死锁。
英文摘要
As modern cities face increasingly severe traffic congestion, connected and autonomous vehicles (CAVs) have emerged as a crucial enabling technology for next-generation intelligent traffic management. However, fully realizing this potential is hindered by the limitations of current paradigms. Existing approaches typically optimize localized interactions rather than system-wide efficiency, incur severe communication overhead, or lack the deterministic guarantees required for safe kinematic execution. Furthermore, current multi-agent adaptations are frequently restricted to small predefined scenarios, failing to scale across large and complex urban networks. To bridge this gap, this paper introduces VeloCity, a decentralized multi-agent spatio-temporal mobility profile planning framework designed for CAVs operating in arbitrary urban areas. To minimize vehicles' travel times, VeloCity distributes mobility profile optimization directly to individual CAVs. Vehicles query a localized traffic coordinator for a reservation table, independently compute their fastest conflict-free mobility profile, and reserve their requested space-time slots back with the coordinator. By natively adapting to any arbitrary road topology, the framework manages highly irregular urban areas without requiring scenario-specific tuning, all while guaranteeing collision-free and physically executable vehicle trajectories. Extensive simulations across four large-scale real-world urban maps (Tokyo, Manhattan, Rome, and Bologna) demonstrate the framework's scalability. Compared to established state-of-the-art models, VeloCity yields drastically lower travel times, tightly bounds delay variance, and successfully prevents congestion gridlocks even under extremely high vehicular densities.
