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Unlike conventional systems that rely on in\u2010cabin facial or physiological analysis, the proposed architecture prioritizes privacy by inferring fatigue through vehicle dynamics and road interaction alone. Built upon the YOLOP deep learning model, the system performs lane segmentation and object detection to extract two critical indicators: lane deviation and inter\u2010vehicle distance, both computed from monocular vision. These signals are interpreted via a fuzzy logic module that incorporates trapezoidal, triangular, and Gaussian membership functions, enabling context\u2010sensitive and explainable fatigue assessment. Comparative evaluation of these functions illustrates trade\u2010offs in responsiveness and generalization. Initial validation against expert human assessments shows promising alignment in perceived fatigue levels, suggesting the system can meaningfully approximate fatigue\u2010related judgments. By aligning with emerging ethical frameworks for non\u2010intrusive AI in mobility, the system marks a step toward socially responsible and practically deployable fatigue monitoring in intelligent transportation.<\/jats:p>","DOI":"10.1049\/ipr2.70202","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T15:06:25Z","timestamp":1756911985000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Safer Roads: A Deep Learning and Fuzzy Logic\u2010Based Driver Fatigue Detection System"],"prefix":"10.1049","volume":"19","author":[{"given":"Marios","family":"Akrivopoulos","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece"}]},{"given":"Socratis","family":"Gkelios","sequence":"additional","affiliation":[{"name":"Department of Computer Science Neapolis University Pafos Pafos Cyprus"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1595-2683","authenticated-orcid":false,"given":"Angelos","family":"Amanatiadis","sequence":"additional","affiliation":[{"name":"Department of Production and Management Engineering Democritus University of Thrace Xanthi Greece"}]},{"given":"Yiannis","family":"Boutalis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece"}]},{"given":"Savvas","family":"Chatzichristofis","sequence":"additional","affiliation":[{"name":"Department of Computer Science Neapolis University Pafos Pafos Cyprus"}]}],"member":"265","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2868499"},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","unstructured":"S.\u2010Y.Shi W.\u2010Z.Tang andY.\u2010Y.Wang \u201cA Review on Fatigue Driving Detection \u201d inITM Web of Conferences(EDP Science 2017) 01019.","DOI":"10.1051\/itmconf\/20171201019"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3333252"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci14050436"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trf.2024.05.008"},{"key":"e_1_2_10_7_1","doi-asserted-by":"crossref","unstructured":"W.Zhang Y. 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