用大模型协同调度自动驾驶车辆，提升城市动态交通效率
LMMCoDrive: Cooperative Driving with Large Multimodal Model
- 用大模型统一处理车辆调度与路径规划，实现端到端协同
- 基于鸟瞰图建模车与乘客关系，通过安全约束优化轨迹
- 采用分布式优化算法，适合大规模自动驾驶系统部署
为解决自动驾驶按需出行（AMoD）系统中去中心化协同调度与运动规划的复杂挑战，本文提出LMMCoDrive框架，利用大型多模态模型（LMM）提升动态城市环境中的交通效率。该框架将自动驾驶车辆与乘客请求的空间关系抽象为鸟瞰图（BEV），充分挖掘LMM潜力。同时，对每辆自动驾驶车辆的轨迹进行精细优化，并通过安全约束保障避碰。在LMM框架内引入交替方向乘子法（ADMM）实现去中心化优化，推动车辆图结构演化。仿真结果表明，LMM在优化车辆调度及增强去中心化协同优化方面起关键作用。该研究标志着迈向实用、高效、安全的AMoD系统的重要进展。代码已开源。
原文摘要 · Abstract (English)
To address the intricate challenges of decentralized cooperative scheduling and motion planning in Autonomous Mobility-on-Demand (AMoD) systems, this paper introduces LMMCoDrive, a novel cooperative driving framework that leverages a Large Multimodal Model (LMM) to enhance traffic efficiency in dynamic urban environments. This framework seamlessly integrates scheduling and motion planning processes to ensure the effective operation of Cooperative Autonomous Vehicles (CAVs). The spatial relationship between CAVs and passenger requests is abstracted into a Bird's-Eye View (BEV) to fully exploit the potential of the LMM. Besides, trajectories are cautiously refined for each CAV while ensuring collision avoidance through safety constraints. A decentralized optimization strategy, facilitated by the Alternating Direction Method of Multipliers (ADMM) within the LMM framework, is proposed to drive the graph evolution of CAVs. Simulation results demonstrate the pivotal role and significant impact of LMM in optimizing CAV scheduling and enhancing decentralized cooperative optimization process for each vehicle. This marks a substantial stride towards achieving practical, efficient, and safe AMoD systems that are poised to revolutionize urban transportation. The code is available at https://github.com/henryhcliu/LMMCoDrive.
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