Real-time system for monitoring driver vigilance

By Luis Miguel Bergasa, Jesús Nuevo. Proceedings of the IEEE International Symposium on Industrial Electronics, 2005. ISIE 2005., 2004

841 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/0ac8b4aa2afd62a749ffc11488c743c95536fcdd

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In Automotive Engineering

In Computer Vision and Pattern Recognition

In Human-Computer Interaction

Citations in the corpus

Driver Fatigue Detection Systems: A Review (2019, background): “[86] predict fatigue from eye, face and head features.”
Driver Inattention Monitoring System for Intelligent Vehicles: A Review (2009, methodology): “[50] implemented a finite-state machine (FSM), with the following five states defined:”
Detecting Driver Drowsiness Based on Sensors: A Review (2012, methodology): “In order to overcome this limitation, some researchers have used active illumination utilizing an infrared Light Emitting Diode (LED) [43].”
Driver Behavior Analysis for Safe Driving: A Survey (2015, background): “The eye state, eye blinking frequency, mouth state and yawning frequency of a driver are the key factors for detecting drowsiness [39].”
Driver Behavior and Situation Aware Brake Assistance for Intelligent Vehicles (2007, background): “Bergasa et al. [ 18 ] recently proposed a real-time system for monitoring driver vigilance using fuzzy systems.”
A multimodal approach to estimating vigilance using EEG and forehead EOG (2016, background): “It is one of the most widely accepted vigilance indices in the literature (Trutschel, Sirois, Sommer, Golz & Edwards 2011, Bergasa, Nuevo, Sotelo, Barea & Lopez 2006, Dong et al. 2011).”
Real-Time Eye Blink Detection using Facial Landmarks (2016, background): “infrared cameras and illuminators [2], wearable devices, glasses with a special close-up cameras observing the eyes [10].”
Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines (2007, methodology): “The degree of driver stress [12] and vigilance [ 13 ] has been predicted from physiological measures and used to help manage IVIS functions.”
Head Pose Estimation and Augmented Reality Tracking: An Integrated System and Evaluation for Monitoring Driver Awareness (2010, methodology): “D face position have been used to estimate driver attentiveness [9].”