Scalable AI Generative Content for Vehicular Network Semantic Communication

TL;DR

This paper presents a scalable AI-generated content system using encoder-decoder architecture to improve semantic communication in vehicular networks, balancing accuracy and delay for safe blind spot detection.

Contribution

It introduces a novel AI-generated content system with reinforcement learning to enhance semantic communication reliability in bandwidth-limited vehicular scenarios.

Findings

Outperforms baseline in blind spot vehicle perception

Effectively compresses communication data

Maintains semantic equivalence across tasks

Abstract

Perceiving vehicles in a driver's blind spot is vital for safe driving. The detection of potentially dangerous vehicles in these blind spots can benefit from vehicular network semantic communication technology. However, efficient semantic communication involves a trade-off between accuracy and delay, especially in bandwidth-limited situations. This paper unveils a scalable Artificial Intelligence Generated Content (AIGC) system that leverages an encoder-decoder architecture. This system converts images into textual representations and reconstructs them into quality-acceptable images, optimizing transmission for vehicular network semantic communication. Moreover, when bandwidth allows, auxiliary information is integrated. The encoder-decoder aims to maintain semantic equivalence with the original images across various tasks. Then the proposed approach employs reinforcement learning to enhance the reliability of the generated contents. Experimental results suggest that the proposed method surpasses the baseline in perceiving vehicles in blind spots and effectively compresses communication data. While this method is specifically designed for driving scenarios, this encoder-decoder architecture also holds potential for wide use across various semantic communication scenarios.