4 papers
VRU-Accident: A Vision-Language Benchmark for Video Question Answering and Dense Captioning for Accident Scene Understanding
Younggun Kim, Ahmed S. Abdelrahman, Mohamed Abdel-Aty
Ensuring the safety of vulnerable road users (VRUs), such as pedestrians and cyclists, is a critical challenge for autonomous driving systems, as crashes involving VRUs often resul…
Advanced Crash Causation Analysis for Freeway Safety: A Large Language Model Approach to Identifying Key Contributing Factors
Ahmed S. Abdelrahman, Mohamed Abdel-Aty, Samgyu Yang +1
Understanding the factors contributing to traffic crashes and developing strategies to mitigate their severity is essential. Traditional statistical methods and machine learning mo…
VRU-CIPI: Crossing Intention Prediction at Intersections for Improving Vulnerable Road Users Safety
Ahmed S. Abdelrahman, Mohamed Abdel-Aty, Quoc Dai Tran
Understanding and predicting human behavior in-thewild, particularly at urban intersections, remains crucial for enhancing interaction safety between road users. Among the most cri…
Video-to-Text Pedestrian Monitoring (VTPM): Leveraging Computer Vision and Large Language Models for Privacy-Preserve Pedestrian Activity Monitoring at Intersections
Ahmed S. Abdelrahman, Mohamed Abdel-Aty, Dongdong Wang
Computer vision has advanced research methodologies, enhancing system services across various fields. It is a core component in traffic monitoring systems for improving road safety…