SEECAD: Semantic End-to-End Communication for Autonomous Driving

Semantic communication is a key paradigm in future 6G systems, designed to revolutionize the physical layer of traditional communication, in order to enhance efficiency of data transmission. Fueled by advancements in deep learning architectures, image processing has made significant strides in segmentation and scenes analysis for autonomous vehicles (AVs). Motivated by these advancements, we present an innovative outlook on communication systems, by leveraging semantic data. Thus, we introduce a novel semantic end-to-end communication system, named SEECAD, specifically designed for image transmission in autonomous driving environments. SEECAD is based on a theoretical model, aligning with the semantic level concepts and leveraging a shared knowledge base to efficiently transmit meaningful image data. The semantic encoder and decoder of SEECAD are built upon deep learning architecture, empowered by Low-Density Parity-Check (LDPC) codes. This integration serves to minimize semantic error transmission and enhance the segmentation accuracy at the receiver. Our proposed semantic communication approach was extensively evaluated in various wireless image transmission scenarios over an AWGN channel, using different QAM modulations (4QAM and 16QAM). Our experimental results demonstrated that the proposed SEECAD achieves accurate and effective image transmission in noisy environments.