Fresh2comm: Information Freshness Optimized Collaborative Perception
Ziyong Wu, Zhilin Peng, Lei Yu
TL;DR
This work addresses high and variable transmission delays in vehicle networks that degrade collaborative perception. It proposes Fresh2comm, an $AoI$-based framework that integrates a perception model, a realistic communication model, and an $AoI$ optimizer to minimize $AoI$ and improve perception accuracy. The main contributions include integrating information freshness into collaborative perception, presenting a low-complexity GreedyPA for transmit-power allocation, and an experimental approach to quantify the impact of different delay types on perception using real-world datasets. Results demonstrate improvements in information freshness and perception robustness under realistic delays, indicating strong practical potential for latency-aware autonomous driving systems.
Abstract
Collaborative perception is a cornerstone of intelligent connected vehicles, enabling them to share and integrate sensory data to enhance situational awareness. However, measuring the impact of high transmission delay and inconsistent delay on collaborative perception in real communication scenarios, as well as improving the effectiveness of collaborative perception under such conditions, remain significant challenges in the field. To address these challenges, we incorporate the key factor of information freshness into the collaborative perception mechanism and develop a model that systematically measures and analyzes the impacts of real-world communication on collaborative perception performance. This provides a new perspective for accurately evaluating and optimizing collaborative perception performance. We propose and validate an Age of Information (AoI)-based optimization framework that strategically allocates communication resources to effectively control the system's AoI, thereby significantly enhancing the freshness of information transmission and the accuracy of perception. Additionally, we introduce a novel experimental approach that comprehensively assesses the varying impacts of different types of delay on perception results, offering valuable insights for perception performance optimization under real-world communication scenarios.