Multimodal Semantic Communications Empowered Lane-Change Planning for Autonomous Driving

Integrating multimodal semantic communications with lane-change (LC) planning has been considered as a promising framework, enabling the vehicle-road collaborative autonomous driving with higher degree of efficiency and flexibility. However, it is crucial to dynamically adjust resource allocation based on multimodal semantic representations for adapting to the dynamic and complex traffic situations, which requires the co-design of motion planning and resource allocation. In this paper, we target on a joint optimization problem of the LC planning as well as the bandwidth and transmit power allocation at the modality transmitters of automated vehicles to minimize the overall latency of LC task completion. To tackle the highdimensional, dynamic, and uncertain challenges of this problem, a deep reinforcement learning-based co-design scheme which employs Dueling Double Deep Q-Network (D3QN) algorithm is proposed. Simulation results illustrate the superiority of our proposed scheme, achieving the overall latency reduction by at least 4.13 ms and the percentage of latency improvement by up to 98.21 over the benchmarks.