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Found 6 papers, 0 papers with code
A Hybrid Defense Method against Adversarial Attacks on Traffic Sign Classifiers in Autonomous Vehicles
Moreover, the hybrid defense method, presented in this study, improves the accuracy for traffic sign classification compared to the traditional defense methods (i. e., JPEG filtering, feature squeezing, binary filtering, and random filtering) up to 6%, 50%, and 55% for FGSM, MIM, and PGD attacks, respectively.
Hybrid Quantum-Classical Neural Network for Cloud-supported In-Vehicle Cyberattack Detection
A classical computer works with ones and zeros, whereas a quantum computer uses ones, zeros, and superpositions of ones and zeros, which enables quantum computers to perform a vast number of calculations simultaneously compared to classical computers.
Hybrid Quantum-Classical Neural Network for Incident Detection
The incident detection performance of the hybrid model is evaluated against baseline classical ML models.
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack
To make classification models resilient against adversarial attacks, we used a hybrid deep-learning model with both the quantum and classical layers.
Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control
Thus, the objective of this study is to develop queue length prediction models for signalized intersections that can be leveraged by ASCS using four variations of Grey systems: (i) the first order single variable Grey model (GM(1, 1)); (ii) GM(1, 1) with Fourier error corrections; (iii) the Grey Verhulst model (GVM), and (iv) GVM with Fourier error corrections.
Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network
This attack detection model can detect false information with an accuracy, precision and recall of 95%, 95% and 87%, respectively, while satisfying the real-time communication and computational requirements.
