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20212026
most citedHybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack

15 citations · 25 across the 16 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

cs.HC2026

Human Driver Temperament and the Safety Impact of a C-V2X Denial-of-Service Flooding Attack in Mixed-Autonomy Traffic

Rasheed Bello, Gurcan Comert, Varghese Vaidyan +3

Cooperative and connected automated vehicles (CAVs) rely on Signal Phase and Timing (SPaT) messages to cross signalized intersections; a denial-of-service (DoS) flood that blocks S…

cs.CR2026

Weather Data Spoofing Attacks on Rain-Adaptive Millimeter-Wave Frequency Selection in V2X Communication Networks

Rasheed Bello, Idreez Yusuf, Justice Adjei Owusu +7

Connected vehicles use millimeter-wave (mmWave) sidelinks for the data rates cooperative driving demands, and emerging designs select the carrier band from sensed rainfall. We show…

cs.NI2026

NS3Learn: Transferring 5G NR Mode-2 Reception Realism from ns-3 to the Veins/SUMO Stack for Connected-Vehicle Safety Assessment

Rasheed Bello, Arthur Mukwaya, Gurcan Comert +5

Connected-vehicle safety evaluations rely on coupled traffic and network simulations, but standard channel models ignore radio resource competition in 5G NR sidelink Mode-2, report…

eess.SY2026

Mitigating Degradation Attacks in Cooperative Autonomous Driving via Intention Sharing: A Vehicle-in-the-Loop Study

Prakhar Gupta, Tyler Ard, Rongyao Wang +4

Communication delays induced by cyber attacks present a critical challenge to the safe operation of connected autonomous driving. This study investigates the use of intention shari…

cs.CR2026

Side-Channel Attacks Survive Noise Cancellation in 3D Printers

Eric Yocam, Varghese Vaidyan, Micah Flack +2

Active Motor Noise Cancellation (AMNC) is a noise-reduction feature shipped in commercial fused deposition modeling (FDM) 3D printers. Because it suppresses the acoustic emissions…

cs.CL2026

Adaptive Activation Cancellation for Hallucination Mitigation in Large Language Models

Eric Yocam, Varghese Vaidyan, Gurcan Comert +3

Large Language Models frequently generate fluent but factually incorrect text. We propose Adaptive Activation Cancellation (AAC), a real-time inference-time framework that treats h…