{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:53:03Z","timestamp":1782316383364,"version":"3.54.5"},"reference-count":13,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T00:00:00Z","timestamp":1728000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Robot. AI"],"abstract":"<jats:p>Negligence of public transport drivers due to drowsiness poses risks not only to their own lives but also to the lives of passengers. The designed journey tracker system alerts the drivers and activates potential penalties. A custom EfficientNet model architecture, based on EfficientNet design principles, is built and trained using the Media Research Lab (MRL) eye dataset. Reflections in frames are filtered out to ensure accurate detections. A 10 min initial period is utilized to understand the driver\u2019s baseline behavior, enhancing the reliability of drowsiness detections. Input from drivers is considered to determine the frame rate for more precise real-time monitoring. Only the eye regions of individual drivers are captured to maintain privacy and ethical standards, fostering driver comfort. Hyperparameter tuning and testing of different activation functions during model training aim to strike a balance between model complexity, performance and computational cost. Obtained an accuracy rate of 95% and results demonstrate that the \u201cswish\u201d activation function outperforms ReLU, sigmoid and tanh activation functions in extracting hierarchical features. Additionally, models trained from scratch exhibit superior performance compared to pretrained models. This system promotes safer public transportation and enhances professionalism by monitoring driver alertness. The system detects closed eyes and performs a cross-reference using personalization data and pupil detection to trigger appropriate alerts and impose penalties.<\/jats:p>","DOI":"10.3389\/frobt.2024.1433795","type":"journal-article","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T08:33:51Z","timestamp":1728030831000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Journey tracker: driver alerting system with a deep learning approach"],"prefix":"10.3389","volume":"11","author":[{"given":"N. L.","family":"Yashaswini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanishri","family":"Arun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B. 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