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arXiv 2025年

作者： Elhenawy, Mohammed Ashqar, Huthaifa I. Rakotonirainy, Andry Alhadidi, Taqwa I. Jaber, Ahmed Tami, Mohammad Abu CARRS-Q and Centre for Data Science Queensland University of Technology Kelvin GroveQLD4059 Australia Data Science and AI Department Arab American University P.O Box 240 Jenin Palestine Artificial Intelligence Program Fu Foundation School of Engineering and Applied Science Columbia University New YorkNY10027 United States

Scene understanding is essential for enhancing driver safety, generating human-centric explanations for Automated Vehicle (AV) decisions, and leveraging artificial intelligence (AI) for retrospective driving video analysis. This study developed a dynamic scene retrieval system using Contrastive Language–Image Pretraining (CLIP) models, which can be optimized for real-time deployment on edge devices. The proposed system outperforms state-of-the-art in-context learning methods, including the zero-shot capabilities of GPT-4o, particularly in complex scenarios. By conducting frame-level analysis on the Honda Scenes Dataset, which contains a collection of about 80 hours of annotated driving videos capturing diverse real-world road and weather conditions, our study highlights the robustness of CLIP models in learning visual concepts from natural language supervision. Results also showed that fine-tuning the CLIP models, such as ViT-L/14 and ViT-B/32, significantly improved scene classification, achieving a top F1-score of 91.1%. These results demonstrate the ability of the system to deliver rapid and precise scene recognition, which can be used to meet the critical requirements of Advanced Driver Assistance Systems (ADAS). This study shows the potential of CLIP models to provide scalable and efficient frameworks for dynamic scene understanding and classification. furthermore, this work lays the groundwork for advanced autonomous vehicle technologies by fostering a deeper understanding of driver behavior, road conditions, and safety-critical scenarios, marking a significant step toward smarter, safer, and more context-aware autonomous driving systems. © 2025, CC BY.

关键词： Advanced driver assistance systems

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Factors affecting crash severity in Roundabouts: A comprehensive analysis in the Jordanian context

Transportation Engineering

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Transportation Engineering 2024年 17卷

作者： Ashqar, Huthaifa I. Alhadidi, Taqwa I. Elhenawy, Mohammed Jaradat, Shadi Civil Engineering Department Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Palestine Artificial Intelligence Program Fu Foundation School of Engineering and Applied Science Columbia University 500W 120th St New YorkNY10027 United States Civil Engineering Department Al-Ahliyya Amman University Al-Saro Al-Salt Amman Jordan CARRS-Q Queensland University of Technology 130 Victoria Park Rd Kelvin GroveQLD4059 Australia

Roundabouts are widely embraced for their perceived safety advantages over other types of unsignalized intersections. However, there has been an observed increase in crash rates at roundabouts over time in Jordan. This paper delves into modeling traffic crash severity at roundabouts, considering various factors such as weather, lighting, vehicle characteristics, geometric features, and driver age and gender. To comprehensively analyze roundabout crashes in Jordan, we constructed rule-based classifiers and Random Forest models after balancing the dataset. Rule-based models offer interpretability, albeit with some simplicity trade-off, while Random Forest models provide deeper analysis but require additional explanation. Presenting both outputs to subject matter experts and policymakers facilitates a holistic understanding of factors contributing to roundabout crashes in Jordan. Subsequently, CN2 results revealed that injury severity crashes are influenced by the time of the day, driver age, day of the week, speed, and number of vehicles involved. On the other hand, property damage-only crashes are affected by the number of lanes, time of the day, type of driver fault, lighting conditions, speed, and day of the week. The RF model analysis unveiled crucial factors influencing crash severity in roundabouts, notably the varying impact of driver age, time of day, the number of vehicles involved, seasonality, and vehicle speed. This proposed approach is promising, comprehensive, and not only enhances the understanding of roundabout crashes but also informs the development of effective, localized safety interventions. © 2024

关键词： Economic and social effects

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Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarios

arXiv

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arXiv 2024年

作者： Masri, Sari Ashqar, Huthaifa I. Elhenawy, Mohammed Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Civil Engineering Department Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Artificial Intelligence Program Fu Foundation School of Engineering and Applied Science Columbia University 500 W 120th St New YorkNY10027 United States CARRS-Q Centre for Data Science Queensland University of Technology 130 Victoria Park Rd Kelvin GroveQLD4059 Australia

Urban traffic management faces significant challenges due to the dynamic environments, and traditional algorithms fail to quickly adapt to this environment in real-time and predict possible conflicts. This study explores the ability of a Large Language Model (LLM), specifically, GPT-4o-mini to improve traffic management at urban intersections. We recruited GPT-4o-mini to analyze, predict position, detect and resolve the conflicts at an intersection in real-time for various basic scenarios. The key findings of this study to investigate whether LLMs can logically reason and understand the scenarios to enhance the traffic efficiency and safety by providing real-time analysis. The study highlights the potential of LLMs in urban traffic management creating more intelligent and more adaptive systems. Results showed the GPT-4o-mini was effectively able to detect and resolve conflicts in heavy traffic, congestion, and mixed-speed conditions. The complex scenario of multiple intersections with obstacles and pedestrians saw successful conflict management as well. Results show that the integration of LLMs promises to improve the effectiveness of traffic control for safer and more efficient urban intersection management. © 2024, CC BY.

关键词： Traffic congestion

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Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm

arXiv

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arXiv 2024年

This study introduces a novel approach for traffic control systems by using Large Language Models (LLMs) as traffic controllers. The study utilizes their logical reasoning, scene understanding, and decision-making capabilities to optimize throughput and provide feedback based on traffic conditions in real-time. LLMs centralize traditionally disconnected traffic control processes and can integrate traffic data from diverse sources to provide context-aware decisions. LLMs can also delivere tailored outputs using various means such as wireless signals and visuals to drivers, infrastructures, and autonomous vehicles. To evaluate LLMs ability as traffic controllers, this study proposed a four-stage methodology. The methodology includes data creation and environment initialization, prompt engineering, conflict identification, and fine-tuning. We simulated multi-lane four-leg intersection scenarios and generates detailed datasets to enable conflict detection using LLMs and Python simulation as a ground truth. We used chain-of-thought prompts to lead LLMs in understanding the context, detecting conflicts, resolving them using traffic rules, and delivering context-sensitive traffic management solutions. We evaluated the prformance GPT-mini, Gemini, and Llama as traffic controllers. Results showed that the fine-tuned GPT-mini achieved 83% accuracy and an F1-score of 0.84. GPT-mini model exhibited a promising performance in generating actionable traffic management insights, with high ROUGE-L scores across conflict identification of 0.95, decision-making of 0.91, priority assignment of 0.94, and waiting time optimization of 0.92. This methodology confirmed LLMs' benefits as a traffic controller in real-world applications. We demonstrated that LLMs can offer precise recommendations to drivers in real-time including yielding, slowing, or stopping based on vehicle dynamics. This study demonstrates LLMs transformative potential for traffic control, enhancing efficiency and safety a

关键词： Street traffic control

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Detecting AI-Generated Text in Educational Content: Leveraging Machine Learning and Explainable AI for Academic Integrity

arXiv

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arXiv 2025年

作者： Najjar, Ayat A. Ashqar, Huthaifa I. Darwish, Omar A. Hammad, Eman Faculty of Modern Science Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Civil Engineering Department Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Artificial Intelligence Program Fu Foundation School of Engineering and Applied Science Columbia University 500 W 120th St New YorkNY10027 United States Information Security and Applied Computing Eastern Michigan University 900 Oakwood St YpsilantiMI48197 United States iSTAR Lab Engineering Technology & Industrial Distribution Texas A&M University 400 Bizzell St College StationTX77840 United States

This study seeks to enhance academic integrity by providing tools to detect AI-generated content in student work using advanced technologies. The findings promote transparency and accountability, helping educators maintain ethical standards and supporting the responsible integration of AI in education. A key contribution of this work is the generation of the CyberHumanAI dataset, which has 1000 observations, 500 of which are written by humans and the other 500 produced by ChatGPT. We evaluate various machine learning (ML) and deep learning (DL) algorithms on the CyberHumanAI dataset comparing human-written and AI-generated content from Large Language Models (LLMs) (i.e., ChatGPT). Results demonstrate that traditional ML algorithms, specifically XGBoost and Random Forest, achieve high performance (83% and 81% accuracies respectively). Results also show that classifying shorter content seems to be more challenging than classifying longer content. further, using Explainable artificial intelligence (XAI) we identify discriminative features influencing the ML model's predictions, where human-written content tends to use a practical language (e.g., use and allow). Meanwhile AI-generated text is characterized by more abstract and formal terms (e.g., realm and employ). Finally, a comparative analysis with GPTZero show that our narrowly focused, simple, and fine-tuned model can outperform generalized systems like GPTZero. The proposed model achieved approximately 77.5% accuracy compared to GPTZero's 48.5% accuracy when tasked to classify Pure AI, Pure Human, and mixed class. GPTZero showed a tendency to classify challenging and small-content cases as either mixed or unrecognized while our proposed model showed a more balanced performance across the three classes. © 2025, CC BY.

关键词： Adversarial machine learning

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Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing

arXiv

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arXiv 2024年

作者： Ashqar, Huthaifa I. Jaber, Ahmed Alhadidi, Taqwa I. Elhenawy, Mohammed Civil Engineering Department Arab American University 13 Zababdeh Jenin P.O Box 240 New YorkNY10027 United States Palestine Artificial Intelligence Program Fu Foundation School of Engineering and Applied Science Columbia University 500 W 120th St New YorkNY10027 United States Department of Transport Technology and Economics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary Civil Engineering Department Al-Ahliyya Amman University Al-Saro Al-Salt Amman19111 Jordan CARRS-Q Queensland University of Technology 130 Victoria Park Rd Kelvin GroveQLD4059 Australia

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 for transportation systems. In the first fold, we provide a background about the potential benefits of MLLMs in transportation applications and conduct a comprehensive review of current MLLM technologies in previous studies. We highlight their effectiveness and limitations in object detection within various transportation scenarios. The second fold involves providing an overview of the taxonomy of end-to-end object detection in transportation applications and future directions. Building on this, we proposed empirical analysis for testing MLLMs on three real-world transportation problems that include object detection tasks namely, road safety attributes extraction, safety-critical event detection, and visual reasoning of thermal images. Our findings provide a detailed assessment of MLLM performance, uncovering both strengths and areas for improvement. Finally, we discuss practical limitations and challenges of MLLMs in enhancing object detection in transportation, thereby offering a roadmap for future research and development in this critical area. © 2024, CC BY.

关键词： Multimodal transportation

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The definition of sng: The expert consensus by the sng taxonomy Task force of the Taiwan sng Society

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Journal of the Formosan Medical Association 2025年

作者： Han, Der-Sheng Lin, Jiann-Her Hung, Chih-Hsien Huang, Shuping Chen, Jen-Yin Chiang, Yung-Hsiao Chiu, Hsiao-Yean Chou, Li-Wei Hsieh, Cheng-Ta Huang, Ming-Chyi Huang, Wei-Lieh Kang, Jiunn-Horng Lee, Hsun-Hua Lin, Chia-Shu Lin, Chien-Min Lin, Chih-Peng Lin, Ying-Tung Liu, Yen-Chin Ro, Long-Sun Shen, Chiung-Chyi Su, Lily I-Wen Su, Yu-Jih Sun, Jui-Ming Sung, Jia-Ying Tsai, Rong-Kung Wang, Man-Ling Wen, Yeong-Ray Weng, Hao-Jui Wu, Ming-Shun Yu, Yu-Wen Hsieh, Sung-Tsang Wang, Shuu-Jiun Sun, Wei-Zen Chen, Chih-Cheng Department of Physical Medicine and Rehabilitation National Taiwan University Hospital Beihu Branch Taipei Taiwan Department of Physical Medicine and Rehabilitation National Taiwan University College of Medicine Taipei Taiwan Division of Neurosurgery Department of Surgery Taipei Medical University Hospital Taipei Taiwan Department of Surgery School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Taipei Neuroscience Institute Taipei Medical University Taipei Taiwan TMU Research Center of Neuroscience Taipei Medical University Taipei Taiwan Department of Neurology Kaohsiung Medical University Hospital Kaohsiung Taiwan School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Foreign Languages and Literature National Sun Yat-sen University Kaohsiung Taiwan Department of Anesthesiology Chi Mei Medical Center Tainan Taiwan School of Medicine College of Medicine National Sun Yat-sen University Kaohsiung Taiwan School of Nursing College of Nursing Taipei Medical University Taipei Taiwan Department of Nursing Taipei Medical University Hospital Taipei Taiwan Department of Physical Medicine and Rehabilitation China Medical University Hospital Taichung Taiwan Department of Physical Medicine and Rehabilitation Asia University Hospital Asia University Taichung Taiwan Department of Physical Therapy and Graduate Institute of Rehabilitation Science China Medical University Taichung Taiwan Division of Neurosurgery Department of Surgery Cathay General Hospital Taipei Taiwan School of Medicine National Tsing-Hua University Hsinchu Taiwan Department of Medicine School of Medicine Fu Jen Catholic University New Taipei City Taiwan Department of Psychiatry Taipei City Psychiatric Center Taipei City Hospital Taipei Taiwan Department of Psychiatry National Taiwan University Hospital Yunlin Branch Yunlin Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taip

“Sng” is a Taiwanese term describing a distinct state of soreness. Accumulating evidence indicates that sng is a specific somatosensory modality, independent of pain, with distinct receptors and transduction pathways. In recent years, variations in its presentation across different medical specialties have highlighted the need for a standardized definition of sng-related terminology. To address this, the Taiwan Sng Society established a multidisciplinary Sng Taxonomy Task Force, comprising 32 experts in both clinical and basic sciences related to sng. Here we summarize the discussions and expert consensus from the Task Force. A total of 16 sng-related terminology was defined. The first edition of sng terminology aims to establish a clear, standardized framework for the clinical practice and research of sng. © 2025 Formosan Medical Association

关键词： Definition sng Sngception Taxonomy Terminology

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