Demo Abstract: Edge-Cloud Switched Image Segmentation for Autonomous Vehicles

Existing autonomous vehicles have not utilized the cloud computing for execution of their deep learning-based driving tasks due to the long vehicle-to-cloud communication latency. The increasing data transmission speed of the commercial mobile networks sheds light upon the feasibility of using the cloud computing for autonomous driving. In this demo, we introduce the design and implementation of ECSeg, an edge-cloud switched image segmentation system that dynamically selects between the edge and cloud to execute deep learning-based semantic segmentation models. This enables realtime understanding of a vehicle's visual scenes while adapting to dynamic wireless conditions and changing environments.