RSU-Aided Energy-Efficient Collaborative Perception for Connected Autonomous Vehicles

In recent years, the concept of collaborative perception (CP) in self-driving vehicles has emerged as a new paradigm for augmenting the safety and efficiency of connected autonomous vehicles (CAVs). However, CP's energy consumption remains a major concern, due to their computation- and transmission-intensive characteristics. To address this issue, this paper first presents a theoretical definition of CP coverage along with a 2-dimensional CP model, followed by a novel framework that leverages roadside units (RSU) to facilitate CP, namely the RSU-Aided Energy-Efficient Sensing, Computation, and Communication (RE2SCC). Through a mix of centralized scheduling and a decentralized data-sharing approach, RE2SCC improves perception performance and energy efficiency. The core of RE2SCC is a novel approach for reducing the overall computation load and energy-efficient CP by scheduling computation and transmission depending on CAVs topology while maintaining the perception performance. The centralized scheduling exploits CP capabilities via sensing data selection, avoiding redundant computation, and direct transmission of perception object data to CAVs, enabling extended perception while minimizing the transmission power. Simulations show the efficiency of the RE2SCC framework for energy savings along with increased perception performance by up to 51% in a given scenario.