STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Electro-Mechanics and Material Behaviour Original Research

Driver Drowsiness Alert System

  1. D.S.S.N. Raju Department of Computer Science and Engineering, Gayatri Vidya Parishad College for degree and PG courses (Autonomous), Visakhapatnam
  2. Bhavana M. Department of Computer Science and Engineering, Gayatri Vidya Parishad College for degree and PG courses (Autonomous), Visakhapatnam
  3. Rishitha V. Department of Computer Science and Engineering, Gayatri Vidya Parishad College for degree and PG courses (Autonomous), Visakhapatnam
  4. Bharat Chandu N. Department of Computer Science and Engineering, Gayatri Vidya Parishad College for degree and PG courses (Autonomous), Visakhapatnam
  5. Harshit B. Department of Computer Science and Engineering, Gayatri Vidya Parishad College for degree and PG courses (Autonomous), Visakhapatnam

Abstract

In the present era, the increasing frequency of accidents during prolonged road trips, primarily attributed to driver fatigue, is a matter of serious concern. Recognizing this challenge, our goal is to develop a driver drowsiness alert system to effectively address and alleviate these incidents. Theproposed system utilizes a webcam to capture real-time images of the driver's eyes, employingmachine learning algorithms to promptly identify signs of fatigue. Upon detecting drowsiness, the system activates a buzzer alarm to immediately alert the driver. If the driver remains unresponsive, the system initiates an auto-pilot mode until the driver awakens and manually disengages the mode. This innovative system, when seamlessly integrated into commercial automotive environments, has the potential to significantly reduce the frequency of accidents resulting from driver drowsiness.

Keywords

References (9)

  1. S S, Banupriya N, M S, H SN. Drowsiness Detection with OpenCV. 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC). 2021:1421-1425. doi:10.1109/icesc51422.2021.9532758
  2. Patnaik R, Krishna KS, Patnaik S, Singh P, Padhy N. Drowsiness Alert, Alcohol Detect and Collision Control for Vehicle Acceleration. 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA). 2020:1-5. doi:10.1109/iccsea49143.2020.9132932
  3. R J, Jacob C. Deep CNN Based Approach for Driver Drowsiness Detection. 2022 IEEE International Power and Renewable Energy Conference (IPRECON). 2022:1-6. doi:10.1109/iprecon55716.2022.10059547
  4. Adochiei IR, Stirbu OI, Adochiei NI, Pericle-Gabriel M, Larco CM, Mustata SM, et al. Drivers’ Drowsiness Detection and Warning Systems for Critical Infrastructures. 2020 International Conference on e-Health and Bioengineering (EHB). 2020:1-4. doi:10.1109/ehb50910.2020.9280165
  5. Titare S, Chinchghare S, Hande KN. Driver Drowsiness Detection and Alert System. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. 2021:583-588. doi:10.32628/cseit2173171
  6. Ojha D, Pawar A, Kasliwal G, Raut R, Devkar A. Driver Drowsiness Detection Using Deep Learning. 2023 4th International Conference for Emerging Technology (INCET). 2023:1-4. doi:10.1109/incet57972.2023.10169941
  7. Nalavade JE, Patil RS. Driver drowsiness detection system using deep neural network. In: 2022 3rd International Conference on Computing, Analytics and Networks (ICAN), Rajpura, India, November 18–19, 2022. doi: 1109/ican56228.2022.10007191.
  8. Agarkar AS, Gandhiraj R, Panda MK. Driver Drowsiness Detection and Warning using Facial Features and Hand Gestures. 2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN). 2023:1-6. doi:10.1109/vitecon58111.2023.10157233
  9. Verma K, Beakta M, Srivastava P, Khan NU. A Non-intrusive Approach for Driver's Drowsiness Detection. 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC). 2020:501-505. doi:10.1109/pdgc50313.2020.9315326