78 citations · 234 across the 33 of their papers we have counts for
12 papers · 1 filter
Zero-Shot Scene Understanding with Multimodal Large Language Models for Automated Vehicles
Mohammed Elhenawy, Shadi Jaradat, Taqwa I. Alhadidi +4
Scene understanding is critical for various downstream tasks in autonomous driving, including facilitating driver-agent communication and enhancing human-centered explainability of…
Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trends
Mohammad Abu Tami, Mohammed Elhenawy, Huthaifa I. Ashqar
Traffic safety remains a critical global challenge, with traditional Advanced Driver-Assistance Systems (ADAS) often struggling in dynamic real-world scenarios due to fragmented se…
Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks
Taqwa I. Alhadidi, Asmaa Alazmi, Shadi Jaradat +3
Pavement distress, such as cracks and potholes, is a significant issue affecting road safety and maintenance. In this study, we present the implementation and evaluation of Bidirec…
Visual Reasoning at Urban Intersections: FineTuning GPT-4o for Traffic Conflict Detection
Sari Masri, Huthaifa I. Ashqar, Mohammed Elhenawy
Traffic control in unsignalized urban intersections presents significant challenges due to the complexity, frequent conflicts, and blind spots. This study explores the capability o…
HazardNet: A Small-Scale Vision Language Model for Real-Time Traffic Safety Detection at Edge Devices
Mohammad Abu Tami, Mohammed Elhenawy, Huthaifa I. Ashqar
Traffic safety remains a vital concern in contemporary urban settings, intensified by the increase of vehicles and the complicated nature of road networks. Traditional safety-criti…
Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding
Mohammed Elhenawy, Huthaifa I. Ashqar, Andry Rakotonirainy +3
Scene understanding is essential for enhancing driver safety, generating human-centric explanations for Automated Vehicle (AV) decisions, and leveraging Artificial Intelligence (AI…
Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing
Huthaifa I. Ashqar, Ahmed Jaber, Taqwa I. Alhadidi +1
This study aims to comprehensively review and empirically evaluate the application of multimodal large language models (MLLMs) and Large Vision Models (VLMs) in object detection fo…
The Use of Multimodal Large Language Models to Detect Objects from Thermal Images: Transportation Applications
Huthaifa I. Ashqar, Taqwa I. Alhadidi, Mohammed Elhenawy +1
The integration of thermal imaging data with Multimodal Large Language Models (MLLMs) constitutes an exciting opportunity for improving the safety and functionality of autonomous d…
Using Multimodal Large Language Models for Automated Detection of Traffic Safety Critical Events
Mohammad Abu Tami, Huthaifa I. Ashqar, Mohammed Elhenawy
Traditional approaches to safety event analysis in autonomous systems have relied on complex machine learning models and extensive datasets for high accuracy and reliability. Howev…
Object Detection using Oriented Window Learning Vi-sion Transformer: Roadway Assets Recognition
Taqwa Alhadidi, Ahmed Jaber, Shadi Jaradat +2
Object detection is a critical component of transportation systems, particularly for applications such as autonomous driving, traffic monitoring, and infrastructure maintenance. Tr…
Advancing Roadway Sign Detection with YOLO Models and Transfer Learning
Selvia Nafaa, Hafsa Essam, Karim Ashour +6
Roadway signs detection and recognition is an essential element in the Advanced Driving Assistant Systems (ADAS). Several artificial intelligence methods have been used widely amon…
Automated Pavement Cracks Detection and Classification Using Deep Learning
Selvia Nafaa, Hafsa Essam, Karim Ashour +6
Monitoring asset conditions is a crucial factor in building efficient transportation asset management. Because of substantial advances in image processing, traditional manual class…