Autonomous mobility systems increasingly operate in dense and dynamic environments where perception occlusions, limited sensing coverage, and multi-agent interactions pose major challenges. While onboard sensors provide essential local perception, they often struggle to maintain reliable situational awareness in crowded urban or indoor settings. This article presents the Cloud-based Autonomous Mobility (CAM) framework, a generalized architecture that integrates infrastructure-based intelligent sensing with cloud-level coordination to enhance autonomous operations. The system deploys distributed Intelligent Sensor Nodes (ISNs) equipped with cameras, LiDAR, and edge computing to perform multi-modal perception and transmit structured information to a cloud platform via high-speed wireless communication. The cloud aggregates observations from multiple nodes to generate a global scene representation for other autonomous modules, such as decision making, motion planning, etc. Real-world deployments in an urban roundabout and a hospital-like indoor environment demonstrate improved perception robustness, safety, and coordination for future intelligent mobility systems.

翻译：自主移动系统日益在密集动态环境中运行，其中感知遮挡、有限传感覆盖范围以及多智能体交互构成了主要挑战。虽然车载传感器提供了必要的局部感知能力，但在拥挤的城市或室内环境中，它们往往难以维持可靠的情境感知。本文提出了基于云端的自主移动（CAM）框架，这是一种将基于基础设施的智能传感与云端协调相结合的通用架构，旨在增强自主操作能力。该系统部署了配备摄像头、激光雷达和边缘计算能力的分布式智能传感节点（ISNs），以执行多模态感知，并通过高速无线通信将结构化信息传输至云端平台。云端聚合来自多个节点的观测数据，为其他自主模块（如决策制定、运动规划等）生成全局场景表征。在城市环形交叉路口和类似医院的室内环境中的实际部署表明，该系统提升了未来智能移动系统的感知鲁棒性、安全性和协调性。