Driver Inattention Monitoring System for Intelligent Vehicles: A Review

By Yanchao Dong, Zhencheng Hu, Keiichi Uchimura, Nobuki Murayama. IEEE transactions on intelligent transportation systems (Print), 2009

717 citations recorded by the source corpus.

Subfields: Automotive Engineering, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/524cc65ce1567500d707af9832ae74dac66bbd1f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Ciarán Hughes
2. Senthil Kumar Yogamani
3. Jonathan Horgan
4. John McDonald Jr.

In Experimental and Cognitive Psychology

1. Wei-Long Zheng
2. Bao-Liang Lu
3. Gulbadan Sikander
4. Shahzad Anwar
5. Wei Liu
6. Zhen Cui
7. Wenming Zheng
8. Jinpeng Li

In Social Psychology

1. Bao-Liang Lu
2. Gulbadan Sikander
3. Wei-Long Zheng
4. Shahzad Anwar

Citations in the corpus

• Vision-Based Driver Assistance Systems: Survey, Taxonomy and Advances (2015, background): “The purpose of these systems is typically for monitoring the occupants of the car (the driver in particular), providing functions such as driver monitoring (DM), back-seat child monitoring [13] and gesture recognition [14].”
• Driver-Activity Recognition in the Context of Conditionally Autonomous Driving (2015, background): “The typical application field of automated driver-activity recognition is the detection of driving risks, e.g. when the driver is inattentive [1].”
• Driver Behavior Analysis for Safe Driving: A Survey (2015, background): “Dong [124] presented a summary of some studies that use hybrid solutions for detecting the driver fatigue and distraction.”
• Brain computer interfacing: Applications and challenges (2015, background): “It has been found that distraction and fatigue are two main sources for driver’s inattention, which is considered as a strong cause for most traffic accidents [63].”
• A multimodal approach to estimating vigilance using EEG and forehead EOG (2016, background): “Moreover, drivers’ vigilance levels cannot be simply classified into several discrete categories but should be quantified in the same way as the blood alcohol level (Dong et al. 2011, Ranney 2008).”
• Survey on Deep Neural Networks in Speech and Vision Systems (2019, background): “However, recent advancements in IS are permeating almost every aspect of our life with the introduction of intelligent transportation [1-10], intelligent diagnosis and health monitoring for precision medicine [11-14], robotics and automation in home appliances [15], virtual online assistance [16], e”
• Driver Fatigue Detection Systems: A Review (2019, background): “Many techniques (to detect driver fatigue) are presented in literature [6]–[10].”