Mohammed Elhenawy

Dr. Mohammed Elhenawy is a Senior Lecturer and Senior Research Fellow at the Centre for Accident Research & Road Safety – Queensland (CARRS-Q), Queensland University of Technology (QUT), Brisbane, Australia. He holds a PhD in Computer Engineering from Virginia Tech. His research focuses on applying machine learning, AI, and statistical learning to transportation safety and intelligent transportation systems (ITS). He has authored over 120 papers and led projects like Australia’s largest Cooperative Intelligent Transport Systems (C-ITS) pilot, the LAARMA system for animal-vehicle collision prevention, and the 'Hold the Red' traffic signal evaluation. His work has been recognized with awards such as the Best Paper Award in Intelligent Vehicle Technologies.

• Affiliations: CARRS-Q, Faculty of Health (School of Psychology & Counselling)
• Education: PhD in Computer Engineering (Virginia Tech)

Research Interests: Transportation safety, AI-driven traffic solutions, cooperative vehicle-infrastructure systems, and data-driven safety models. His work spans connected vehicles (V2V/V2I), near-miss detection, and multimodal large language models (MLLMs) for real-time traffic control. He also explores applications in pedestrian safety, e-scooter rider protection, and tunnel glare mitigation.

Articles Trends: Recent work emphasizes LLMs for traffic safety analysis, multimodal data fusion, and autonomous vehicle perception. Key themes include real-time intersection control, zero-shot learning for crash analysis, and optimizing urban mobility systems.

• Awards: Best Paper Award in Intelligent Vehicle Technologies

Advising & Grants: Leads interdisciplinary projects funded by Australian transport agencies and collaborates globally on ITS initiatives. His work bridges academia and industry, focusing on deployable safety solutions.

Labs/Teams: CARRS-Q, collaborating with QUT’s School of Psychology & Counselling and external transport agencies.