Robust detection framework for adversarial threats in Autonomous Vehicle Platooning

Introduction: The study addresses adversarial threats in Autonomous Vehicle Platooning (AVP) using machine learning. Methods: A novel method integrating active learning with RF, GB, XGB, KNN, LR, and AdaBoost classifiers was developed. Results: Random Forest with active learning yielded the highest accuracy of 83.91%. Discussion: The proposed framework significantly reduces labeling efforts and improves threat detection, enhancing AVP system security.