The increasing demand for flexible and efficient urban transportation solutions has spotlighted the limitations of traditional Demand Responsive Transport (DRT) systems, particularly in accommodating diverse passenger needs and dynamic urban environments. Autonomous Mobility-on-Demand (AMoD) systems have emerged as a promising alternative, leveraging connected and autonomous vehicles (CAVs) to provide responsive and adaptable services. However, existing methods primarily focus on either vehicle scheduling or path planning, which often simplify complex urban layouts and neglect the necessity for simultaneous coordination and mutual avoidance among CAVs. This oversimplification poses significant challenges to the deployment of AMoD systems in real-world scenarios. To address these gaps, we propose CoDriveVLM, a novel framework that integrates high-fidelity simultaneous dispatching and cooperative motion planning for future AMoD systems. Our method harnesses Vision-Language Models (VLMs) to enhance multi-modality information processing, and this enables comprehensive dispatching and collision risk evaluation. The VLM-enhanced CAV dispatching coordinator is introduced to effectively manage complex and unforeseen AMoD conditions, thus supporting efficient scheduling decision-making. Furthermore, we propose a scalable decentralized cooperative motion planning method via consensus alternating direction method of multipliers (ADMM) focusing on collision risk evaluation and decentralized trajectory optimization. Simulation results demonstrate the feasibility and robustness of CoDriveVLM in various traffic conditions, showcasing its potential to significantly improve the fidelity and effectiveness of AMoD systems in future urban transportation networks. The code is available at https://github.com/henryhcliu/CoDriveVLM.git.

翻译：随着对灵活高效城市交通解决方案需求的日益增长，传统需求响应式公交系统的局限性日益凸显，尤其是在适应多样化乘客需求和动态城市环境方面。按需自动驾驶出行系统作为一种有前景的替代方案应运而生，它利用网联自动驾驶车辆来提供响应迅速且适应性强的服务。然而，现有方法主要侧重于车辆调度或路径规划中的单一环节，通常简化了复杂的城市布局，并忽视了CAV之间同时进行协调与相互避让的必要性。这种过度简化给AMoD系统在现实场景中的部署带来了重大挑战。为弥补这些不足，我们提出了CoDriveVLM，一个为未来AMoD系统集成高保真度同步调度与协同运动规划的新型框架。我们的方法利用视觉语言模型来增强多模态信息处理能力，从而实现全面的调度与碰撞风险评估。引入了VLM增强的CAV调度协调器，以有效管理复杂且不可预见的AMoD运行状况，从而支持高效的调度决策。此外，我们提出了一种基于共识交替方向乘子法的可扩展去中心化协同运动规划方法，该方法聚焦于碰撞风险评估与去中心化轨迹优化。仿真结果证明了CoDriveVLM在各种交通条件下的可行性与鲁棒性，展现了其在未来城市交通网络中显著提升AMoD系统保真度与有效性的潜力。代码发布于 https://github.com/henryhcliu/CoDriveVLM.git。