  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.
