Journal ArticleDOI
Detecting Driver Drowsiness: A survey of system designs and technology.
TLDR
Three paramount efforts in the development of DFD systems are covered: academic, governmental, and corporate; the reader will see the complete picture of this area in just one article.Abstract:
Driving and transporting goods are necessary for human activity. As a consequence of drivers spending a considerable amount of time at the workplace, and usually under pressure, vehicular accidents have become a great contributor to mortality in several countries. Traffic accidents in countries such as the United States are a central concern. For instance, the U.S. National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System Encyclopedia [1] shows that there were approximately 55,926 vehicles involved in collisions in 2007, 9,797 of which were due to driver fatigue and inattention. The reported driver-related factors include the driver was drowsy, sleepy, asleep, and/or fatigued, the driver was under the influence of alcohol, drugs, and/or medication, the driver was inattentive (talking, eating, etc.), a cellular telephone was present in the vehicle, a cellular telephone was in use in vehicle.read more
Citations
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Journal ArticleDOI
Advanced Driver-Assistance Systems: A Path Toward Autonomous Vehicles
TL;DR: A survey of different hardware and software ADAS technologies and their capabilities and limitations is presented and approaches used for vision-based recognition and sensor fusion in ADAS solutions are discussed.
Journal ArticleDOI
Automatic driver sleepiness detection using EEG, EOG and contextual information
TL;DR: The many vehicle crashes that are caused by driver sleepiness each year advocates the development of automated driverSleepiness detection (ADSD) systems.
Journal ArticleDOI
Driver Sleepiness Classification Based on Physiological Data and Driving Performance From Real Road Driving
TL;DR: If signal analysis and machine learning can be used to develop an accurate sleepiness warning system is investigated, and a random forest classifier was found to be the most robust classifier with an accuracy of 94.1%.
Journal ArticleDOI
Cloud-Based Driver Monitoring System Using a Smartphone
TL;DR: An approach and case study of a distributed driver monitoring system that utilizes smartphone sensors for detecting dangerous states for a driver in a vehicle and proposes a cloud system architecture to capture statistics from vehicle drivers, analyze it and personalize the smartphone application for the driver.
Journal Article
Face detection and tracking in video
TL;DR: Face detection based on Adaboost and cascade classifier and face detection which is combined effectively with tracking algorithm based on CamShift is proposed and can operate successfully detection and tracking in scale variation, view variation or occlusion.
References
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Journal ArticleDOI
Real-time nonintrusive monitoring and prediction of driver fatigue
Qiang Ji,Zhiwei Zhu,P. Lan +2 more
TL;DR: A probabilistic model is developed to model human fatigue and to predict fatigue based on the visual cues obtained, and it was found to be reasonably robust, reliable, and accurate in fatigue characterization.
Journal ArticleDOI
Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences
Maja Pantic,Ioannis Patras +1 more
TL;DR: This paper presents a system for automatic recognition of facial action units (AUs) and their temporal models from long, profile-view face image sequences and introduces facial-action-dynamics recognition from continuous video input using temporal rules.
Journal ArticleDOI
Driver Inattention Monitoring System for Intelligent Vehicles: A Review
TL;DR: The hybrid measures are believed to give more reliable solutions compared with single driver physical measures or driving performance measures, because the hybrid measures minimize the number of false alarms and maintain a high recognition rate, which promote the acceptance of the system.
Evaluation of techniques for ocular measurement as an index of fatigue and the basis for alertness management
TL;DR: In this article, the validity of the ocular measure "Perclose" as a generally useful and reliable index of lapses in visual attention, i.e., the percentage of eyelid closure over the pupil, was established in a controlled sleep deprivation study.
Journal ArticleDOI
Modeling drivers' visual attention allocation while interacting with in-vehicle technologies
TL;DR: In this paper, the authors examined how characteristics of a simulated traffic environment and in-vehicle tasks impact driver performance and visual scanning and the extent to which a computational model of visual attention (SEEV model) could predict scanning behavior.
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