State Consistent Edge-enhanced Perception for Connected and Automated Vehicles

Vehicle function offloading has been an active research topic in the connected and automated vehicles (CAV) domain. Mobile edge computing can execute sophisticated algorithms on abundant computing resources, leading to superior accuracy for vehicle system state estimation. On the other hand, cellular network latency causes edge-computed information to be obsolete.In this paper, by focusing on camera-based object tracking applications, we develop a novel state fusion framework that not only achieves the benefits (enhanced detection accuracy) but also mitigates the disadvantages (unpredictable network latency) of edge computing. This is achieved by carefully managing the system state consistency between the vehicle onboard system and the remote edge system through our novel backward-and-forward algorithms. We evaluate our system by comparing our method with the edge-only and onboard-only counterparts through extensive empirical experiments. The presented framework improves the accuracy of camera-based perception by at least 2x compared to traditional techniques, with an average response time of 48.13 ms (strictly less than the mission-critical latency threshold of 100 ms [1]).