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Journal ArticleDOI

Driver Behavior Analysis for Safe Driving: A Survey

TLDR
A proposal is made for the active of such systems into car-to-car communication to support vehicular ad hoc network's (VANET) primary aim of safe driving and the dissemination of driver behavior via C2C communication.
Abstract
Driver drowsiness and distraction are two main reasons for traffic accidents and the related financial losses. Therefore, researchers have been working for more than a decade on designing driver inattention monitoring systems. As a result, several detection techniques for the detection of both drowsiness and distraction have been proposed in the literature. Some of these techniques were successfully adopted and implemented by the leading car companies. This paper discusses and provides a comprehensive insight into the well-established techniques for driver inattention monitoring and introduces the use of most recent and futuristic solutions exploiting mobile technologies such as smartphones and wearable devices. Then, a proposal is made for the active of such systems into car-to-car communication to support vehicular ad hoc network's (VANET's) primary aim of safe driving. We call this approach the dissemination of driver behavior via C2C communication. Throughout this paper, the most remarkable studies of the last five years were examined thoroughly in order to reveal the recent driver monitoring techniques and demonstrate the basic pros and cons. In addition, the studies were categorized into two groups: driver drowsiness and distraction. Then, research on the driver drowsiness was further divided into two main subgroups based on the exploitation of either visual features or nonvisual features. A comprehensive compilation, including used features, classification methods, accuracy rates, system parameters, and environmental details, was represented as tables to highlight the (dis)advantages and/or limitations of the aforementioned categories. A similar approach was also taken for the methods used for the detection of driver distraction.

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Citations
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Journal ArticleDOI

Driver Fatigue Detection Systems: A Review

TL;DR: This paper presents state-of-the-art review of recent advancement in the field of driver fatigue detection and various approaches have been compared for fatigue detection, and areas open for improvements are deduced.
Journal ArticleDOI

Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

TL;DR: In this paper, the authors summarized the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone.
Journal ArticleDOI

Heart Rate Variability-Based Driver Drowsiness Detection and Its Validation With EEG

TL;DR: A driver drowsiness detection algorithm based on heart rate variability (HRV) analysis is proposed and validates the proposed method by comparing with electroencephalography (EEG)-based sleep scoring and demonstrates the usefulness of the framework of HRV-based anomaly detection.
Posted Content

Smartphone-based Vehicle Telematics - A Ten-Year Anniversary

TL;DR: This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone.
Journal ArticleDOI

The Role of Machine Vision for Intelligent Vehicles

TL;DR: An overview on the state of research in the field of machine vision for intelligent vehicles covers the range from advanced driver assistance systems to autonomous driving and addresses computing architectures suited to real-time implementation.
References
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TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Proceedings ArticleDOI

Robust real-time face detection

TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.
Journal ArticleDOI

Statistical pattern recognition: a review

TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Journal ArticleDOI

Statistical Pattern Recognition

TL;DR: In this paper, the primary goal of pattern recognition is supervised or unsupervised classification, and the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been used.
Journal ArticleDOI

In the Eye of the Beholder: A Survey of Models for Eyes and Gaze

TL;DR: This review shows that, despite their apparent simplicity, the development of a general eye detection technique involves addressing many challenges, requires further theoretical developments, and is consequently of interest to many other domains problems in computer vision and beyond.
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