Zillur, Sheikh FaiyadhZaman, SrejoniTasfia, Faiza ZahinRafid, Sk Tahmed SalimSumaya, Atoshe IslamHuda, A. S. Nazmul2026-09-222026-09-222023-01-01S. F. Zillur, S. Zaman, F. Z. Tasfia, S. T. S. Rafid, A. I. Sumaya and A. S. N. Huda, "Towards Safer Roads: Raspberry Pi-driven Drowsy Driver Detection Using EAR Analysis," 2023 26th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2023, pp. 1-5, doi: 10.1109/ICCIT60459.2023.10441072.97983503590152-s2.0-85187392539https://hdl.handle.net/10361/30157The detection of drowsiness is a safety measure that can prevent road accidents caused by drivers who fell asleep behind the wheel. This article develops a driver drowsiness detection system which is based on Raspberry Pi processor and "facial-68-landmark"model for facial feature extraction. The developed system is extensively evaluated through various test cases, yielding valuable insights into its performance and accuracy. By utilizing the Eye Aspect Ratio (EAR), the system establishes drowsiness thresholds, identifying an EAR below 0.25 as indicative of drowsiness and an EAR below 0.15 as a sign of sleepiness. In order to assess the performance of the proposed system, test results for 10 volunteers is considered. The study reveals that the system performs optimally with a direct camera placement pointed towards the driver, achieving an impressive accuracy of 99% when drivers do not wear glasses. Under different conditions including spectacles and viewing angles variation, the system maintains an average accuracy of 97% across 600 tested frames. These results prove that the developed system can be applied on real world applications.5 Pagesen-USRoad accidentsSleepRoadsWheelsSafetyVehiclesDriver drowsinessOpenCVRaspberry PiIntelligent transportation systems.Conference Proceeding10.1109/ICCIT60459.2023.10441072