This survey explores the transformative role of reconfigurable intelligent surface (RIS) technologies in vehicular communication systems. It begins by introducing key RIS concepts and classifying them into passive, active, and hybrid types, with particular focus on beyond diagonal RIS (BD-RIS) and its various architectural designs. The study then delves into the evolution of vehicular communication technologies, followed by channel modeling and strategies to ensure long-term system adaptability. The survey further investigates key technological advancements, such as advanced channel modeling and estimation techniques, strategies for optimizing network sum-rate and energy efficiency (EE), and enhancing security within vehicular networks through RIS. Additionally, it examines the integration of RIS with vehicular edge computing (VEC) frameworks, its potential in unmanned aerial vehicle (UAV) supported networks, and its application in visible light communication (VLC) systems. The analysis also considers the synergy between RIS and mmWave technologies. Then, a comprehensive performance assessment is conducted to evaluate the impact of these advancements in RIS-based vehicular networks. The aim is to thoroughly elaborate, compare, and evaluate the literature concerning network scenarios, RIS details, methodologies, and optimization objectives. Conclusively, the research highlights key findings and charts future research directions, pinpointing imminent technical challenges and emerging areas for further exploration in the rapidly evolving landscape of vehicular communications.

To understand the audience's subjective perception of quality of service (QoS), it is important to analyze the data acquired from the logistics service logs and online evaluation system reasonably and effectively. Based on the analysis, rational impr...

Vehicular Ad Hoc Networks (VANETs) face challenges due to high vehicle mobility, which leads to frequent changes in network topology and unstable links. To tackle this, we propose a novel routing strategy called MPFD, which enhances message delivery rates and reduces End-to-End (E2E) delay. MPFD operates in two phases. The first phase, the message management phase, prioritizes messages based on urgency and controls message propagation by considering hop count (H) and Time-To-Live (TTL), ensuring only critical messages are forwarded. The second phase, the relay node selection phase, uses free nodes to identify the best relay for forwarding messages. Candidate nodes are evaluated based on community association, movement direction, and proximity to the destination. Simulations show that the MPFD strategy significantly improves message delivery speed, reduces overhead and E2E delay, and enhances the overall message delivery ratio in VANETs.

The increasing complexity of urban environments has amplified the demand for secure, reliable, and energy-efficient wireless communications. Traditional terrestrial base stations frequently face challenges in dense urban areas, mainly due to recurring non-line-of-sight (NLOS) scenarios, intense interference, and high susceptibility to eavesdropping. Unmanned aerial vehicles (UAVs) have stood out as a valuable supplementary option, thanks to their ability to support flexible deployment and reliable line-of-sight (LOS) connections. Meanwhile, millimeter-wave (mmWave) bands provide abundant spectrum to satisfy the growing capacity demands of urban networks, but remain highly susceptible to path loss, blockages, and beam misalignment, limiting their effectiveness in such environments. As a cost-efficient and energy-conscious option, reconfigurable intelligent surfaces (RIS) work by smartly reconfiguring wireless reflections to recover blocked links, expand coverage range, and boost secrecy performance. This paper investigates a RIS-aided UAV mmWave communication framework engineered to jointly address energy efficiency and physical layer security under potential eavesdropping threats. A deep reinforcement learning (DRL) strategy is formulated, where the UAV agent interacts with the dynamic urban environment based on imperfect channel state information (CSI) and local position feedback. The agent jointly optimizes UAV active beamforming, RIS phase shifts, and UAV trajectory in real time. To mitigate estimation bias, a softmax operator is incorporated into the learning process. Simulation findings demonstrate that the method put forward in this study outperforms baseline solutions relying on DRL. Specifically, it alleviates both overestimation and underestimation issues while significantly improving secrecy capacity and secure energy efficiency in RIS-assisted UAV mmWave systems.