Driver drowsiness detection using modified deep learning architecture

Author:

Kumar VijayORCID,Sharma Shivam,Ranjeet

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Vision and Pattern Recognition,Mathematics (miscellaneous)

Reference43 articles.

1. World Health Organization (2015) ; World Health Organization: Geneva, Switzerland, 2015

2. Distracted Driving-Motor Vehicle Safety-CDC Injury Center. https://www.cdc.gov/motorvehiclesafety/distracteddriving/

3. Johnson T(2018) 2017 Traffic Safety Culture Index. https://aaafoundation.org/2017-traffic-safety-culture-index/

4. Ed-doughmi Y, Idrissi N(2019) Driver Fatigue Detection using Recurrent Neural Networks. In: Proceedings of 2nd International Conference on Networking, Information Systems & Security, Rabat, Morocco, NY, USA, pp. 44

5. Chirra VRR, Uyyala SR, Kolli VKK (2019) Deep CNN: A machine learning approach for driver drowsiness detection based on eye state. Reve d’Intelligence Artificielle 33(6):461–466

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4. Raspberry Pi-Based Driver Drowsiness Detection;2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT);2024-02-09

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