15 citations · 25 across the 16 of their papers we have counts for
18 papers
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…
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…
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…
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…
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…
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…
Mode Choice Heterogeneity Among Zero-Vehicle Households: A Latent Class Cluster Approach
Nancy Kasamala, Arthur Mukwaya, Nana Kankam Gyimah +4
In transportation planning, Zero-Vehicle Households (ZVHs) are often treated as a uniform group with limited mobility options and assumed to rely heavily on walking or public trans…
A Prescriptive Framework for Determining Optimal Days for Short-Term Traffic Counts
Arthur Mukwaya, Nancy Kasamala, Nana Kankam Gyimah +5
The Federal Highway Administration (FHWA) mandates that state Departments of Transportation (DOTs) collect reliable Annual Average Daily Traffic (AADT) data. However, many U.S. DOT…
Harnessing ADAS for Pedestrian Safety: A Data-Driven Exploration of Fatality Reduction
Methusela Sulle, Judith Mwakalonge, Gurcan Comert +2
Pedestrian fatalities continue to rise in the United States, driven by factors such as human distraction, increased vehicle size, and complex traffic environments. Advanced Driver…
Real-Time Conflict Prediction for Large Truck Merging in Mixed Traffic at Work Zone Lane Closures
Abyad Enan, Abdullah Al Mamun, Gurcan Comert +4
Large trucks substantially contribute to work zone-related crashes, primarily due to their large size and blind spots. When approaching a work zone, large trucks often need to merg…
Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign Classification
Reek Majumder, Mashrur Chowdhury, Sakib Mahmud Khan +6
Deep learning (DL)-based image classification models are essential for autonomous vehicle (AV) perception modules since incorrect categorization might have severe repercussions. Ad…
Unraveling Pedestrian Fatality Patterns: A Comparative Study with Explainable AI
Methusela Sulle, Judith Mwakalonge, Gurcan Comert +2
Road fatalities pose significant public safety and health challenges worldwide, with pedestrians being particularly vulnerable in vehicle-pedestrian crashes due to disparities in p…
Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems
Hannah Musau, Nana Kankam Gyimah, Judith Mwakalonge +2
Advanced Driver Assistance Systems (ADAS) enhance highway safety by improving environmental perception and reducing human errors. However, misconceptions, trust issues, and knowled…
Analysis of Distracted Pedestrians Crossing Behavior: An Immersive Virtual Reality Application
Methusela Sulle, Judith Mwakalonge, Gurcan Comert +4
Pedestrian safety is a critical public health priority, with pedestrian fatalities accounting for 18% of all U.S. traffic deaths in 2022. The rising prevalence of distracted walkin…
Crash Severity Risk Modeling Strategies under Data Imbalance
Abdullah Al Mamun, Abyad Enan, Debbie A. Indah +3
This study investigates crash severity risk modeling strategies for work zones involving large vehicles (i.e., trucks, buses, and vans) under crash data imbalance between low-sever…
An AutoML-based approach for Network Intrusion Detection
Nana Kankam Gyimah, Judith Mwakalonge, Gurcan Comert +6
In this paper, we present an automated machine learning (AutoML) approach for network intrusion detection, leveraging a stacked ensemble model developed using the MLJAR AutoML fram…
Hybrid Quantum-Classical Neural Network for Incident Detection
Zadid Khan, Sakib Mahmud Khan, Jean Michel Tine +7
The efficiency and reliability of real-time incident detection models directly impact the affected corridors' traffic safety and operational conditions. The recent emergence of clo…
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack
Reek Majumder, Sakib Mahmud Khan, Fahim Ahmed +6
Image classification must work for autonomous vehicles (AV) operating on public roads, and actions performed based on image misclassification can have serious consequences. Traffic…