Real-World Deployment of Cloud-based Autonomous Mobility Systems for Outdoor and Indoor Environments

TL;DR

This paper introduces the CAM framework, a cloud-based architecture that combines infrastructure sensors and cloud processing to improve perception, coordination, and safety for autonomous mobility in complex outdoor and indoor environments.

Contribution

The paper presents a novel cloud-based autonomous mobility system integrating infrastructure sensors and cloud coordination, enhancing perception and safety in challenging environments.

Findings

Improved perception robustness in urban and indoor settings.

Enhanced safety and coordination demonstrated in real-world deployments.

Effective multi-modal sensing with distributed sensor nodes.

Abstract

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.