Driver inattention: A contributing factor to crashes and near-crashes

Klauer, Sheila G.; Neale, Vicki L.; Dingus, Thomas A.; Ramsey, David; Sudweeks, Jeremy

doi:10.1037/e577512012-008

Cited by 24 publications

(26 citation statements)

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“…Two previous naturalistic driving studies reported no associations between the prevalence of secondary task engagement and characteristics of the driving environment (e.g., road surface conditions, time of drive; Klauer, Dingus, Neale, Sudweeks, & Ramsey, 2006; Stutts, Reinfurt, & Rodgman, 2001). In contrast, data from a roadside observational study indicated that the prevalence of secondary task engagement was greater in less demanding driving environments (Kidd, Tison, Chaudhary, McCartt, & Casanova-Powell, 2016).…”

Section: Introductionmentioning

confidence: 93%

Teens' distracted driving behavior: Prevalence and predictors

Gershon

Zhu

Klauer

et al. 2017

Journal of Safety Research

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Introduction: Teen drivers′ over-involvement in crashes has been attributed to a variety of factors, including distracted driving. With the rapid development of in-vehicle systems and portable electronic devices, the burden associated with distracted driving is expected to increase. The current study identifies predictors of secondary task engagement among teenage drivers and provides basis for interventions to reduce distracted driving behavior. We described the prevalence of secondary tasks by type and driving conditions and evaluated the associations between the prevalence of secondary task engagement, driving conditions, and selected psychosocial factors. Methods: The private vehicles of 83 newly-licensed teenage drivers were equipped with Data Acquisition Systems (DAS), which documented driving performance measures, including secondary task engagement and driving environment characteristics. Surveys administered at licensure provided psychosocial measures. Results: Overall, teens engaged in a potentially distracting secondary task in 58% of sampled road clips. The most prevalent types of secondary tasks were interaction with a passenger, talking/singing (no passenger), external distraction, and texting/dialing the cell phone. Secondary task engagement was more prevalent among those with primary vehicle access and when driving alone. Social norms, friends’ risky driving behaviors, and parental limitations were significantly associated with secondary task prevalence. In contrast, environmental attributes, including lighting and road surface conditions, were not associated with Teens′ engagement in secondary tasks. Conclusions: Our findings indicated that teens engaged in secondary tasks frequently and poorly regulate their driving behavior relative to environmental conditions. Practical applications: Peer and parent influences on secondary task engagement provide valuable objectives for countermeasures to reduce distracted driving among teenage drivers.

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Section: Introductionmentioning

confidence: 93%

Teens' distracted driving behavior: Prevalence and predictors

Gershon

Zhu

Klauer

et al. 2017

Journal of Safety Research

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“…For experimental analysis and corresponding results, YawDD [31] used two video datasets of drivers with various facial expressions. The videos are taken and shot in real time, under varying and capricious illumination conditions.…”

Section: Resultsmentioning

confidence: 99%

Intelligent Driver Warning System using Deep Learning-based Facial Expression Recognition

Suchitra*¹,

Priya²,

Poovaraghan³

et al. 2019

IJRTE

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Driver’s inattention is one of the major factors and reasons in occurrence of many road accidents and unforeseen crashes. Hence it is crucial to develop an automatic driver warning system that can send timely warning signals to the drivers. This issue involves determining the driver’s mental state that is ultimately based on the driver’s facial expressions. Automated facial emotion recognition is a recent development in the image processing domain and is the need of the hour in applications like driver warning systems. The existing methods are capable of recognizing facial emotions even when provided with a noisy signal or imperfect data, but ultimately it lacks accuracy. It is also ineffective in dealing with spontaneous emotions, and recognition. The proposed approach develops a driver warning system that extracts the facial expressions based on a novel efficient Local Octal Pattern (LOP) and effectively recognizes the facial expressions based on Deep Neural Networks, Convolutional Neural Networks (CNN). The LOP feature map serves as an input to CNN and guides in the selection of CNN learning data thereby improving and further enhancing the understanding and learning of CNN. It also has an ability to recognize both natural and spontaneous emotions, as well as image and video can be considered as an input.The experimental results consideringYawDD dataset indicates that the proposed system has been efficiently evaluated by considering the with metrics such as Precision, Recall and F-Score and thereby it is observed and inferred that the proposed system obtained a high recall rate of 96.09% in comparison with the other state-of-the-art methods

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“…Examples of distracted driving behaviors include use of smartphones/mobile devices, smoking tobaccos, engaging in a conversation with other passengers, drinking beverages, eating foods, and such, that are irrelevant to the task of driving itself. Different forms of distractions such as drowsiness, fatigues, medication effects, and other medical/physiological issues can also cause life threatening situations [16].…”

Section: Introductionmentioning

confidence: 99%

NADS-Net: A Nimble Architecture for Driver and Seat Belt Detection via Convolutional Neural Networks

Chun

Ghalehjegh

Choi

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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A new convolutional neural network (CNN) architecture for 2D driver/passenger pose estimation and seat belt detection is proposed in this paper. The new architecture is more nimble and thus more suitable for in-vehicle monitoring tasks compared to other generic pose estimation algorithms. The new architecture, named NADS-Net, utilizes the feature pyramid network (FPN) backbone with multiple detection heads to achieve the optimal performance for driver/passenger state detection tasks. The new architecture is validated on a new data set containing video clips of 100 drivers in 50 driving sessions that are collected for this study. The detection performance is analyzed under different demographic, appearance, and illumination conditions. The results presented in this paper may provide meaningful insights for the autonomous driving research community and automotive industry for future algorithm development and data collection.

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Driver inattention: A contributing factor to crashes and near-crashes

Cited by 24 publications

References 0 publications

Teens' distracted driving behavior: Prevalence and predictors

Teens' distracted driving behavior: Prevalence and predictors

Intelligent Driver Warning System using Deep Learning-based Facial Expression Recognition

NADS-Net: A Nimble Architecture for Driver and Seat Belt Detection via Convolutional Neural Networks

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