Poster: Towards Efficient Multilayer Collaboration for CAV Applications

Abstract

Connected and autonomous vehicles (CAV s) are facing increasing amounts of data and more complex data analysis, which creates challenges for them to make reliable decisions in real-time. To enable time-sensitive CAV applications, we design and implement a vehicle-edge-cloud framework that integrates compressed imaging (CI) and edge computing into CAV systems. Specifically, a lightweight model is used on the vehicle to perform real-time detection based on optical domain compressed data (called measurements). The edge is responsible for receiving the measurements and performing video reconstruction to support (more accurate) analysis based on the reconstructed video with a trigger. At the same time, the measurements, reconstructed videos, and analysis results are sent to the cloud to continuously update the vehicle model. In addition, we apply reinforcement learning to adapt the compression rate in different driving scenarios. The proposed framework is fully evaluated using our designed roadside platform and outdoor delivery vehicles.