Cascaded multimodal analysis of alertness related features for drivers safety applications

Author:

Abouelenien Mohamed1,Burzo Mihai2,Mihalcea Rada1

Affiliation:

1. University of Michigan

2. University of Michigan-Flint

Funder

National Science Foundation

Publisher

ACM

Reference20 articles.

1. Stay alert! the ford challenge March 2011. Available at http://www.kaggle.com/c/stayalert. Stay alert! the ford challenge March 2011. Available at http://www.kaggle.com/c/stayalert.

2. Driver drowsiness monitoring based on yawning detection

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1. Hypo-Driver: A Multiview Driver Fatigue and Distraction Level Detection System;Computers, Materials & Continua;2022

2. Multimodal Detection of Drivers Drowsiness and Distraction;Proceedings of the 2021 International Conference on Multimodal Interaction;2021-10-18

3. A Methodological Review on Prediction of Multi-Stage Hypovigilance Detection Systems Using Multimodal Features;IEEE Access;2021

4. Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis;Sensors;2020-12-24

5. Towards detecting levels of alertness in drivers using multiple modalities;Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments;2020-06-26

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