  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.
