Leveraging Nanosensor and Vision Transformer for Robust Anomaly Detection in Autonomous Vehicles

Abstract

Autonomous vehicles (AVs) are revolutionizing Intelligent Transportation Systems by seamlessly exchanging real-time data with other AVs and the network. For humans, controlled transportation has numerous advantages. But safety and security are the main concerns because malicious autonomous vehicles can make consequences. To avoid these consequences nanosensors are integrated with Vision Transformer (ViT) in AV which play a pivotal role in enhancing anomaly detection. To evaluate performance of the proposed framework, various evaluation metrics were employed. The experimental findings are compared with existing models, such as Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Reinforcement Learning (DRL), and Deep Belief Networks (DBN). The result shows that ViT method achieves accuracy, precision, recall of about 92%, 93% and 93%, respectively. Experimental results demonstrate the superiority of ViT and nano sensor integration over traditional methods, showcasing its ability to detect a wide range of attacks with high accuracy and robustness.