Author

M. Elhenawy

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#explainable ai Open access Sep 2026

A context-aware and human-centered framework for road safety assessment using semantic scene understanding

The proposed framework provides a reliable and explainable approach for assessing perceived road safety by integrating semantic scene understanding with contextual reasoning and establishes a reproducible foundation for future research in interpretable transportation systems, human–AI alignment analysis, and road scene...

Imad Tbaileh, Ahmed Radwan, Oroob Yaseen et al. · Frontiers in Future Transpor... · 0 citations

#small language model Open access Sep 2026

Toward Sustainable Urban Mobility: A Multimodal Large Language Model (MLLM) Framework for Automated Driver Performance Assessment with YOLOv8-Based Scene Detection

An exploratory proof-of-concept framework for automated driver evaluation that combines real-world dashcam footage, YOLOv8-based object detection, and multimodal large language models (MLLMs), specifically Gemini 1.5 Flash is presented.

Mamatha Byreddy, Yara Zayed, Anas M. R. Alsobeh et al. · Infrastructures · 0 citations

Open access 2026

Explainability in Automated Driving: From Spatial Attention to Human-Centred Reasoning

A human-centred explainability framework is proposed that repositions AV explanation as a cognitive human-machine interaction problem, leveraging multimodal large language models as the generative engine for contextualised natural language explanations responsive to user expertise and situational urgency.

A. Rakotonirainy, A. Y. Zadeh, Zishuo Zhu et al. · AHFE International · 0 citations