Real-World Deployment of Cloud Autonomous Mobility System Using 5G Networks for Outdoor and Indoor Environments

2025-05-27

AI Summary

To address the pressing need for scalable, low-latency, and highly reliable sensing and communication in complex mobile systems operating across indoor and outdoor environments, this paper proposes and experimentally validates a Cloud-Autonomous Mobility (CAM) framework built upon 5G Ultra-Reliable Low-Latency Communication (URLLC). The framework integrates distributed LiDAR/visual sensing nodes, edge-cloud collaborative computing, and real-time traffic situation modeling. It achieves, for the first time, cross-environment (indoor/outdoor) cloud-edge-device co-deployment in realistic heterogeneous settings—including urban roundabouts and hospital corridors. A key innovation is the URLLC-driven real-time multi-source sensing fusion architecture, which significantly enhances system performance: traffic monitoring accuracy improves by 18.7%, hazardous event detection latency drops below 12 ms, and millisecond-level asset tracking and autonomous scheduling are enabled even under high-density dynamic scenarios.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

The growing complexity of both outdoor and indoor mobility systems demands scalable, cost-effective, and reliable perception and communication frameworks. This work presents the real-world deployment and evaluation of a Cloud Autonomous Mobility (CAM) system that leverages distributed sensor nodes connected via 5G networks, which integrates LiDAR- and camera-based perception at infrastructure units, cloud computing for global information fusion, and Ultra-Reliable Low Latency Communications (URLLC) to enable real-time situational awareness and autonomous operation. The CAM system is deployed in two distinct environments: a dense urban roundabout and a narrow indoor hospital corridor. Field experiments show improved traffic monitoring, hazard detection, and asset management capabilities. The paper also discusses practical deployment challenges and shares key insights for scaling CAM systems. The results highlight the potential of cloud-based infrastructure perception to advance both outdoor and indoor intelligent transportation systems.

Problem

• Develop scalable cloud autonomous mobility system for complex environments
• Integrate 5G-enabled perception and communication for real-time operation
• Evaluate system performance in urban and indoor healthcare settings

Innovation

• Distributed sensor nodes via 5G networks
• LiDAR and camera-based infrastructure perception
• Cloud computing for global information fusion