Fresh2comm: Information Freshness Optimized Collaborative Perception

TL;DR

This paper introduces Fresh2comm, an AoI-based framework that optimizes communication resource allocation to improve information freshness and perception accuracy in collaborative perception for connected vehicles under real-world delays.

Contribution

It develops a systematic AoI-based model for evaluating and enhancing collaborative perception performance amid communication delays in intelligent vehicles.

Findings

AoI-based optimization significantly improves perception accuracy.

The framework effectively manages communication delays in real scenarios.

Experimental results validate the approach's robustness and effectiveness.

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.