most citedPerformance Evaluation of Real-Time Object Detection for Electric Scooters

2 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

cs.HC2025

Assessing Workers Neuro-physiological Stress Responses to Augmented Reality Safety Warnings in Immersive Virtual Roadway Work Zones

Fatemeh Banani Ardecani, Omidreza Shoghli

This paper presents a multi-stage experimental framework that integrates immersive Virtual Reality (VR) simulations, wearable sensors, and advanced signal processing to investigate…

cs.HC2025

Electrodermal Insights into Stress Dynamics of AR-Assisted Safety Warnings in Virtual Roadway Work Zone Environments

Fatemeh Banani Ardecani, Omidreza Shoghli

This study examines stress levels in roadway workers utilizing AR-assisted multi-sensory warning systems under varying work intensities. A high-fidelity Virtual Reality environment…

cs.CV20251 cited

Real-Time Roadway Obstacle Detection for Electric Scooters Using Deep Learning and Multi-Sensor Fusion

Zeyang Zheng, Arman Hosseini, Dong Chen +2

The increasing adoption of electric scooters (e-scooters) in urban areas has coincided with a rise in traffic accidents and injuries, largely due to their small wheels, lack of sus…

cs.HC20251 cited

Adoption of AI-Assisted E-Scooters: The Role of Perceived Trust, Safety, and Demographic Drivers

Amit Kumar, Arman Hosseini, Arghavan Azarbayjani +2

E-scooters have become a more dominant mode of transport in recent years. However, the rise in their usage has been accompanied by an increase in injuries, affecting the trust and…

cs.CV20242 cited

Performance Evaluation of Real-Time Object Detection for Electric Scooters

Dong Chen, Arman Hosseini, Arik Smith +4

Electric scooters (e-scooters) have rapidly emerged as a popular mode of transportation in urban areas, yet they pose significant safety challenges. In the United States, the rise…