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Proceedings ArticleDOI

Driver inattention monitoring system for intelligent vehicles: A review

TLDR
A review of the literature on driver inattention monitoring system for the purpose of active safe driving can be found in this paper, where the authors classified driving inattentions into two categories: fatigue and distraction.
Abstract
This paper gives a review of the literature on driver inattention monitoring system for the purpose of active safe driving. In this paper driving inattention is classified into two categories: fatigue and distraction, while fatigue and distraction can also contain many types and levels. Individual difference on inattention phenomenon makes it more complicated to correctly detect and recognize driving inattention. Driver attention monitoring has been intensively researched in recent years and many approaches have been proposed, which include biological signal (EEG, ECG, EOG and sEMG) processing method, subjective report method, and behavior analysis method. This survey reviews a number of promising approaches and provides an overview of recent developments in this domain. The emphasis of this paper is to discuss the various methodologies to monitor driving inattention. We conclude with some thoughts about future directions.

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Citations
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Journal ArticleDOI

Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey

TL;DR: A survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends is provided.
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.
Proceedings ArticleDOI

Detecting emotional stress from facial expressions for driving safety

TL;DR: A real-time non-intrusive monitoring system is developed, which detects the emotional states of the driver by analyzing facial expressions, and which operates very well on simulated data even with generic models.
Journal ArticleDOI

Automatic gaze-based user-independent detection of mind wandering during computerized reading

TL;DR: The automatically detected mind wandering rate correlated negatively with measures of learning and transfer even after controlling for prior knowledge, thereby providing evidence of predictive validity.
Journal ArticleDOI

Vehicles of the Future: A Survey of Research on Safety Issues

TL;DR: An overview of research on ICT-based support and assistance services for the safety of future connected vehicles and a categorized literature survey of safety critical applications is presented in detail.
References
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Journal ArticleDOI

Random Forests

TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI

A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting

TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
Book

Attention and Effort

Journal ArticleDOI

Toward a Theory of Situation Awareness in Dynamic Systems

TL;DR: A theoretical model of situation awareness based on its role in dynamic human decision making in a variety of domains is presented and design implications for enhancing operator situation awareness and future directions for situation awareness research are explored.

The Impact of Driver Inattention on Near-Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data

TL;DR: In this article, the authors presented a study on the safety of self-driving cars with the National Highway Traffic Safety Administration (NHTSA) and the U.S. Office of Human-Vehicle Performance Research.
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