Journal ArticleDOI
Visual Analysis of Eye State and Head Pose for Driver Alertness Monitoring
TLDR
Experimental results show that the proposed scheme offers high classification accuracy with acceptably low errors and false alarms for people of various ethnicity and gender in real road driving conditions.Abstract:
This paper presents visual analysis of eye state and head pose (HP) for continuous monitoring of alertness of a vehicle driver. Most existing approaches to visual detection of nonalert driving patterns rely either on eye closure or head nodding angles to determine the driver drowsiness or distraction level. The proposed scheme uses visual features such as eye index (EI), pupil activity (PA), and HP to extract critical information on nonalertness of a vehicle driver. EI determines if the eye is open, half closed, or closed from the ratio of pupil height and eye height. PA measures the rate of deviation of the pupil center from the eye center over a time period. HP finds the amount of the driver's head movements by counting the number of video segments that involve a large deviation of three Euler angles of HP, i.e., nodding, shaking, and tilting, from its normal driving position. HP provides useful information on the lack of attention, particularly when the driver's eyes are not visible due to occlusion caused by large head movements. A support vector machine (SVM) classifies a sequence of video segments into alert or nonalert driving events. Experimental results show that the proposed scheme offers high classification accuracy with acceptably low errors and false alarms for people of various ethnicity and gender in real road driving conditions.read more
Citations
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Journal ArticleDOI
Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State
TL;DR: The experimental results show the advantages of the vision-based fatigue detection system for bus driver monitoring on accuracy and robustness for the challenging situations when a camera of an oblique viewing angle to the driver's face is used for driving state monitoring.
Journal ArticleDOI
Looking at Humans in the Age of Self-Driving and Highly Automated Vehicles
Eshed Ohn-Bar,Mohan M. Trivedi +1 more
TL;DR: The role of humans in the next generation of driver assistance and intelligent vehicles is highlighted and efforts within each domain, integrative frameworks across domains, and scientific tools required for future developments are discussed to provide a human-centered perspective on research in intelligent vehicles.
Journal ArticleDOI
A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms
Anuradha Kar,Peter Corcoran +1 more
TL;DR: A key outcome from this review is the realization of a need to develop standardized methodologies for the performance evaluation of gaze tracking systems and achieve consistency in their specification and comparative evaluation.
Journal ArticleDOI
Driver Distraction Detection Using Semi-Supervised Machine Learning
TL;DR: This paper explored semi-supervised methods for driver distraction detection in real driving conditions to alleviate the cost of labeling training data.
Journal ArticleDOI
MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction With Automation
Lex Fridman,Daniel E. Brown,Michael Glazer,William Angell,Spencer Dodd,Benedikt Jenik,Jack Terwilliger,Aleksandr Patsekin,Julia Kindelsberger,Li Ding,Sean Seaman,Alea Mehler,Andrew Sipperley,Anthony Pettinato,Bobbie Seppelt,Linda Angell,Bruce Mehler,Bryan Reimer +17 more
TL;DR: The governing objectives of the MIT Advanced Vehicle Technology study are to undertake large-scale real-world driving data collection that includes high-definition video to fuel the development of deep learning-based internal and external perception systems and gain a holistic understanding of how human beings interact with vehicle automation technology.
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