21 papers
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…
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…
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…