Edge-Cloud Switched Low-Carbon Image Segmentation for Autonomous Vehicles

Existing autonomous vehicles (AVs) utilize neither cloud computing for execution of their deep learning-based driving tasks due to the long vehicle-to-cloud communication latency, nor solar energy to offset the energy usage of car-borne computing. They are in general equipped with the resource-constrained edge computing devices which may be unable to execute the compute-intensive deep learning models in real time. The increasing data transmission speed of the commercial mobile networks sheds light upon the feasibility of using the cloud computing for autonomous driving, which is demonstrated by our city-scale real-world measurements over the fifth generation (5G) mobile networks. Moroever, the cost and form factor declines of solar harvesting systems make the quest of integrating them with AVs for improving carbon efficiency more promising. In this paper, we present the design and implementation of ECSeg, an edge-cloud switched low-carbon image segmentation system for AVs equipped with roof-mounted solar panels. ECSeg dynamically switches between edge and cloud processing to execute semantic segmentation models in real time while aiming to decarbonize AV computing by maximizing the use of harvested solar energy. The switching decision is challenging due to the complex interdependencies among various factors, including dynamic wireless channel conditions, vehicle motion, visual scene changes, and available renewable energy. To tackle this, ECSeg employs deep reinforcement learning to learn an optimal switching policy. Extensive evaluations based on real-world experiments and trace-driven simulations demonstrate that ECSeg outperforms six baseline approaches, achieving 98.8% reduction in computing’s carbon emission, while maintaining high image segmentation accuracy, compared with our earlier design without integrating the solar panel.