Year: 2025
Type: article
Abstract: 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.
Source: Frontiers in Big Data
Author: Stephanie Ness
Institutions: University of Vienna, Diplomatic Academy of Vienna
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,418
OpenAlex ID: W4416404888