With the wide application of artificial intelligence (AI) in the Internet of Vehicles (IoV), IoV is under pressure for data transmission and real-time sensing. Integrated sensing and communication (ISAC) is one of the key technologies to alleviate that pressure. Obstacles can cause communication disruptions and increased delays, hindering autonomous driving information acquisition and causing traffic hazards. The application of Reconfigurable Intelligent Surfaces (RISs) aims to solve this problem. This study focuses on RIS-assisted multi-base station (MBS) scenarios in the presence of obstacles. This study aims to maximize the communication rate, minimize the sensing error, and reduce the switching frequency by optimizing the RIS phase shift and beamforming. The problem is modeled as mixed integer nonlinear programming (MINLP) and further described as a Markov Decision Process (MDP). We use Long Short-Term Memory (LSTM) to predict the environmental state and propose two optimization algorithms, Multi-Factor Decision Deep Deterministic Policy Gradient (MFD-DDPG) and Mixed Discrete and Continuous Action DDPG (MDCA-DDPG). In the first algorithm, we consider multiple factors to make a switching decision and use DDPG to yield the optimal action. The second algorithm improves DDPG by outputting a discrete switching decision and a continuous optimized action simultaneously. Simulations show that the proposed algorithms significantly improve the system performance, and the communication rate is increased by more than 40% in specific multi-vehicle scenarios compared to the benchmark.
Lai et al. (Thu,) studied this question.