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Original Article
An Adam W-Optimized Vision Transformer Framework with Back propagation Training for Driver Drowsiness Detection for Smart Vehicular Safety
Mani Bakkiaraj P1
Dr. K. Karuppasamy2
Thenmalar R3
Sinduja R4
1 PG student, Department of Computer Science & Engineering, RVS College Of Engineering & Technology, Coimbatore, Tamilnadu, India. 2 Head of Department, Professor, Department of Computer Science & Engineering, RVS College Of Engineering & Technology, Coimbatore, Tamilnadu, India. 3 Project Guide, Assistant Professor, Department of Computer Science & Engineering, RVS College Of Engineering & Technology, Coimbatore, Tamilnadu, India. 4 Project Coordinator, Assistant Professor, Department of Computer Science & Engineering, RVS College Of Engineering & Technology, Coimbatore, Tamilnadu, India.
Published Online: January-April 2026
Pages: 273-279
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501039
References
1. Driver Drowsiness Detection Navya Kiran et al.
2. A Deep Learning Approach to Detect Driver Drowsiness – Tibrewal et al.
3. State-of-the-Art in Driver’s Drowsiness Detection: A Comprehensive Survey- Yagna V. Bhatt1 and Narayan A. Joshi2
4. Driver Drowsiness Detection Using Artificial Intelligence and Machine Learning - Dhaval Rane, Rohit Danavale, Abhijit Rathiya, Shwet
Pawar.
5. A Review of Driver Drowsiness Detection Systems: Techniques, Advantages and Limitations - Ismail Nasri, Mohammed Karrouchi, Kamal
Kassmi, and Abdelhafid Messaoudi.
6. Real-Time Driver Drowsiness Detection Using Eye Aspect Ratio and Facial Landmark Analysis -Taufiya Fathima1, Dr. H Girisha
7. Eye Aspect Ratio for Real-Time Drowsiness Detection to Improve Driver Safety. by Christine Dewi, Rung-Ching Chen, Chun-Wei Chang,
Shih-Hung Wu, Xiaoyi Jiang and Hui yu.
8. Applying Spatiotemporal Attention to Identify Distracted and Drowsy Driving with Vision Transformers - Samay Lakhani
9. Vision Transformers and YoloV5 based Driver Drowsiness Detection Framework - Ghanta Sai Krishna, Kundrapu Supriya, Jai Vardhan and
Mallikharjuna Rao K Data Science and Artificial Intelligence Department, IIIT Naya Raipur Computer science and Engineering, IIIT Naya
Raipur.
2. A Deep Learning Approach to Detect Driver Drowsiness – Tibrewal et al.
3. State-of-the-Art in Driver’s Drowsiness Detection: A Comprehensive Survey- Yagna V. Bhatt1 and Narayan A. Joshi2
4. Driver Drowsiness Detection Using Artificial Intelligence and Machine Learning - Dhaval Rane, Rohit Danavale, Abhijit Rathiya, Shwet
Pawar.
5. A Review of Driver Drowsiness Detection Systems: Techniques, Advantages and Limitations - Ismail Nasri, Mohammed Karrouchi, Kamal
Kassmi, and Abdelhafid Messaoudi.
6. Real-Time Driver Drowsiness Detection Using Eye Aspect Ratio and Facial Landmark Analysis -Taufiya Fathima1, Dr. H Girisha
7. Eye Aspect Ratio for Real-Time Drowsiness Detection to Improve Driver Safety. by Christine Dewi, Rung-Ching Chen, Chun-Wei Chang,
Shih-Hung Wu, Xiaoyi Jiang and Hui yu.
8. Applying Spatiotemporal Attention to Identify Distracted and Drowsy Driving with Vision Transformers - Samay Lakhani
9. Vision Transformers and YoloV5 based Driver Drowsiness Detection Framework - Ghanta Sai Krishna, Kundrapu Supriya, Jai Vardhan and
Mallikharjuna Rao K Data Science and Artificial Intelligence Department, IIIT Naya Raipur Computer science and Engineering, IIIT Naya
Raipur.
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