Digital Twin and DRL-Driven Semantic Dissemination for 6G Autonomous Driving Service

Data dissemination is critical for 6G autonomous driving (AD) service because of the extensive demand for real-time traffic information. However, the heavier data transmission burden and more stringent requirements of AD service bring challenges for current data dissemination methods. In this paper, we first propose a novel digital twin (DT)-based semantic dissemination architecture to better support 6G AD service. Under this architecture, an energy-efficient semantic communication mechanism is developed to reduce the data dissemination burden while keeping low semantic model update costs. Meanwhile, the DT network is leveraged to disseminate semantic data in parallel with the physical vehicular networks, which alleviates the physical transmission contention and improves the dissemination efficiency. Second, we design a deep-reinforcement-learning (DRL)-driven semantic data dissemination scheme for the proposed architecture, named Proximal-policy-optimization for Digital-twin-aided Data Dissemination (PD3), which seeks the optimal DT transfer and semantic transmission scheduling strategy. Finally, experimental results show that our approach surpasses the state-of-the-art methods by 18.36% lower dissemination delay and 4.51% higher dissemination ratio on average.