云边协同感知提升室内外自动驾驶的可靠性与安全性
Real-World Deployment of Cloud-based Autonomous Mobility Systems for Outdoor and Indoor Environments
- 用分布式的智能传感器节点+云端融合感知，突破单车感知盲区
- 实测在城市路口和医院环境实现更稳定的安全导航
- 适合研究智能交通、自动驾驶系统集成的开发者
自动驾驶系统在密集动态环境中面临感知遮挡、传感覆盖有限及多智能体交互等挑战。虽然车载传感器提供局部感知，但在拥挤的城市或室内场景中难以维持可靠的态势感知。本文提出基于云的自主移动（CAM）框架，通过基础设施级智能感知与云端协调，增强自主运行能力。系统部署配备摄像头、激光雷达和边缘计算的分布式智能传感器节点（ISNs），以高速无线通信将结构化信息上传至云端平台。云端聚合多个节点的观测数据，生成全局场景表示，供决策、路径规划等模块使用。在城市环岛和类医院室内环境的真实部署验证表明，该系统显著提升了感知鲁棒性、安全性和协同效率，为未来智能出行系统提供可靠支撑。
原文摘要 · Abstract (English)
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.
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