In 6G vehicle-to-everything (V2X) scenary, precise millimeter-wave beam prediction under dynamic environmental disturbances faces critical challenges of latency-accuracy trade-offs and cross-scenario generalization. It not only requires accurate and prompt determination of the vehicle’s position, but also needs to overcome external environmental disturbances. However, most existing schemes suffer from critical limitations such as poor adaptability in extreme scenarios, prolonged decision latency, and restricted cross-scenario generalization capability. Towards this end, we propose LLM-MM: an end-to-end robust multimodal beam prediction framework for 6G V2X networks via MoE-LoRA Adaptation. Specifically, we first construct a distinctive beam prediction architecture to leverage the powerful inference capabilities of the Large Language Model (LLM), thereby shortening the inference time while ensuring the inference accuracy. Secondly, by integrating the Mixture-of-Experts with Low-Rank Adaptation model, LLM-MM not only ensures its generalization ability across multiple scenarios but also reduces the training cost. Our framework integrates a rigorously evaluated open-source LLM, selected through systematic comparison of multiple candidates to achieve optimal performance. Extensive numerical results demonstrate the advantages of proposed framework from multiple perspectives.

With the advancement of the Internet of Vehicles, accurate beam prediction is crucial for maintaining stable and high-quality wireless communication in dynamic environments. A large number of beam prediction schemes have been proposed, which can be broadly categorized into two types: beam prediction schemes based on channel state and side information. However, the beam prediction schemes based on channel state require significant training overhead and computational complexity. In addition, most schemes based on side information neither make effective use of multimodal data (such as camera, lidar, and position), nor consider the impact of environmental noise on prediction accuracy. To address these weaknesses, we propose a novel beam prediction scheme with high robustness based on the multimodal data. Specifically, the scheme first uses a variety of data augmentation methods to reduce the noise interference caused by the adverse environment. Then, we employ ResNet to map heterogeneous data into a unified linear space to achieve effective feature alignment and correspondence. Finally, we exploit the distinctive multi-head attention mechanism of the Transformer model to guarantee that the fused features are both representative and informative. The comparison of numerous numerical results demonstrates that the proposed scheme offers both high robustness and accuracy across various scenarios.

As the most widely used in-vehicle network, the controller area network bus lacks effective encryption and authentication mechanisms, exposing it to significant security threats. Voltage-based intrusion detection systems (IDSs), which detect malicious frames and locate attackers by establishing voltage fingerprints, have attracted widespread attention from researchers. However, the voltage signals of frames are vulnerable to temperature variations, leading to false positives and false negatives in voltage-based IDS. To address this, researchers have proposed the scheme that involves frequently updating voltage fingerprints and the temperature compensation-based scheme. Unfortunately, frequent updates to voltage fingerprints have been shown to be vulnerable to poisoning attacks, while the temperature compensation-based approach requires knowledge of the sender node's temperature. To this end, we propose utilizing robust voltage features to develop a robust voltage-based IDS without knowing sender node's temperature. Experiments conducted on both the prototype and real vehicle show that, compared to existing mainstream voltage-based IDS, our system demonstrates superior robustness under temperature variations. To the best of our knowledge, we are the first to detect intrusion by collecting voltage signals and extracting their features on resource-constrained device. Our system also includes an alarm module, which can trigger a buzzer to alert the driver when the intrusion is detected.

The development of 5G-vehicle-to-everything (5G-V2X) technology makes vehicle-road-cloud collaboration possible. Vehicles and roads transmit sensor data to the cloud via 5G-V2X technology and then the cloud sends the data to the target vehicle. The target vehicle utilizes dynamic environmental data from surrounding vehicles and roadside units to predict the driving trajectory of surrounding vehicles in order to ensure its own safety. However, many existing trajectory prediction schemes are based on incomplete single-vehicle perception and ignore surrounding road conditions, which will greatly limit their value in real-world scenarios. Therefore, this paper proposes a flexible multi-channel vehicle trajectory prediction scheme based on vehicle-road collaboration. Specifically, we first design a flexible multi-channel vehicle trajectory prediction scheme that can extract different vehicle and map features from various information sources. Then, we use the Transformer model to generate predicted trajectories of surrounding vehicles by fusing features from different sources, and achieve parallel computing effects. The most popular dataset, INTERACTION, is used to evaluate the proposed scheme. The results show that our scheme is robust across different scenarios and possesses better accuracy.