Task-Oriented Source-Channel Coding Enabled Autonomous Driving Based on Edge Computing

The communication system is under a paradigm transformation that shifts from traditional bit-level transmission to semantic-level transmission. This transition lays the foundation for complex autonomous driving, necessitating instantaneous processing of substantial data within the constraints of computing capacity and communication bandwidth. In this paper, we propose a novel Task-oriented Source-Channel Coding (TSCC) framework that jointly optimizes source coding and channel coding in a task-oriented manner. Specifically, to reduce communication overhead and guarantee autonomous driving performance, we leverage an autonomous driving agent to guide source-channel coding based on a modified Conditional Variational Autoencoder (CVAE). We test the proposed framework on a well-known autonomous driving platform with different communication channel conditions. The results show that compared to traditional communication and state-of-the-art deep Joint Source-Channel Coding (JSCC), our proposed framework achieves superior performance by saving 98.36% communication overhead and maintains an 83.24% driving score even at 0 dB Signal-to-Noise Ratios (SNR).