Task-oriented semantic communication is an emerging technology that transmits
only the relevant semantics of a message instead of the whole message to
achieve a specific task. It reduces latency, compresses the data, and is more
robust in low SNR scenarios. This work presents a multi-task-oriented semantic
communication framework for connected and autonomous vehicles (CAVs). We
propose a convolutional autoencoder (CAE) that performs the semantic encoding
of the road traffic signs. These encoded images are then transmitted from one
CAV to another CAV through satellite in challenging weather conditions where
visibility is impaired. In addition, we propose task-oriented semantic decoders
for image reconstruction and classification tasks. Simulation results show that
the proposed framework outperforms the conventional schemes, such as QAM-16,
regarding the reconstructed image's similarity and the classification's
accuracy. In addition, it can save up to 89 % of the bandwidth by sending fewer
bits.

通过提出一种多任务语义通信框架，本研究旨在解决连接和自动驾驶汽车（CAV）中的语义通信问题。采用卷积自编码器（CAE）对道路交通标志进行语义编码，并通过卫星在低可视性天气条件下从一个 CAV 传输到另一个 CAV。同时，该框架还提出了用于图像重建和分类任务的任务导向语义解码器，模拟结果表明该框架在图像相似度和分类准确性方面优于传统方案，并通过发送更少的比特节省了高达 89% 的带宽。