Collaborative Redundancy Reduction for Communication-based Low-Latency Video Analytics in Autonomous Driving

Autonomous driving vehicles, leveraging on-board cameras for real-time surrounding environment sensing, have boosted the development of safe and efficient transportation. However, limited on-board computation resources pose a challenge in obtaining low-latency video analytics, as performing multiple analytics tasks simultaneously with processing massive redundancy in spatial, temporal, and semantic dimensions related to video analytics is of daunting computational complexity, resulting in long analytics latency. In this paper, we propose CoVA, a collaborative architecture based on multi-hop V2V communications, to achieve low-latency and accurate video analytics. Specifically, CoVA first builds the same lane-based vehicle fleets and formulates a multi-objective optimization problem, aiming to minimize spatial redundancy and latency with reliable communications. Then, a variable-threshold video frame filtering algorithm is designed to dynamically handle unpredictable surrounding environment changes by adjusting the similarity threshold for video frames captured by vehicles, which minimizes temporal redundancy while guaranteeing analytics accuracy. Furthermore, considering the semantic redundancy, CoVA reconstructs the original formulated problem using collaborative vehicle sensing and processing across lanes. Finally, joint redundancy minimization and efficient resource allocation optimization is proposed for properly allocating computation and bandwidth resources. Experimental results demonstrate the superiority of CoVA against other video analytics approaches, significantly reducing up to 70% in latency while only sacrificing less tolerated accuracy.