Related Experiment Video
Updated: Jul 30, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
Published on: February 1, 2020
Robust detection framework for adversarial threats in Autonomous Vehicle Platooning
1University of Vienna, Diplomatic Academy of Vienna, Vienna, Austria.
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.
Related Concept Videos
Collisions in Multiple Dimensions: Problem Solving
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
Detection of Black Holes
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
Distributed Loads: Problem Solving
Rolling Resistance: Problem Solving
Distribution Reliability and Automation
Automatic Processing and Automatic Social Behavior

