The feasibility of detecting phone-use related driver distraction

Waard, Dick de; Brookhuis, Karel; Hernández-Gress, Neil

doi:10.1504/ijvd.2001.001931

Cited by 41 publications

(11 citation statements)

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“…Our study also showed that crossing a red signal was more significantly higher among the drivers who used a mobile phone while driving. De Waard et al (2001) reported that looking at the phone numbers while holding the phone in one hand showed a serious deterioration in driving performance in terms of lane control. The Road and Traffic Department in the State of Qatar has banned the use of handheld mobile phones as of September 2005.…”

Section: Discussionmentioning

confidence: 99%

Mobile phone use while driving: a major public health problem in an Arabian society, State of Qatar—mobile phone use and the risk of motor vehicle crashes

et al. 2009

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Aim The aim of the present study was to assess the rate of mobile phone use among drivers who were involved in road traffic crashes and to determine the factors associated with mobile phone use in Qatar. Methods Structured interviews were conducted and participants were asked to complete a questionnaire concerning socio-demographics, driving attitudes and behaviour, adherence to traffic laws and mobile phone use. The study was conducted from December 2004 to June 2005 during which 1,139 drivers were asked to participate in the study and 822 drivers expressed their consent and met the inclusion criteria with a response rate of 72%. Results There was a high rate of mobile phone use among individuals associated with crashes (73.2%). Of 602 drivers, 497 (82.6%) used handheld mobile phones without any extra add-on equipment. A stepwise logistic regression analysis showed that the vehicle type (four-wheel drive vs small car), excessive speeding, educational level and running a red light were statistically significant factors associated with mobile phone use among drivers who were involved in road traffic crashes. Conclusion Use of mobile phones while driving in Qatar is very high and poses a high risk for violations and motor vehicle crashes. These conclusions are discussed with reference to current interventions and safety regulations that are being launched.

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Section: Discussionmentioning

confidence: 99%

Mobile phone use while driving: a major public health problem in an Arabian society, State of Qatar—mobile phone use and the risk of motor vehicle crashes

et al. 2009

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“…From literature, several studies have proved that in such situations ML approaches can outperform the traditional analytical methods. Moreover, also human's driver mental and physical behavior is non-deterministic [42]- [45].…”

Section: Discussionmentioning

confidence: 99%

Real-Time Detection System of Driver Distraction Using Machine Learning

Tango

Botta

2013

IEEE Trans. Intell. Transport. Syst.

155

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Abstract-There is an accumulating evidence that driver's distraction is a leading cause of vehicle crashes and incidents. In particular, it has become an important and growing safety concern with the increasing use of the so-called In-Vehicle Information Systems (IVIS) and Partially Autonomous Driving Assistance Systems (PADAS). Thereby, the detection of the driver status is of paramount importance, in order to adapt IVIS and PADAS accordingly, so avoiding or mitigating their possible negative effects. The purpose of this paper is to illustrate a method for the non-intrusive and real-time detection of visual distraction, based on vehicle dynamics data and without using the eye-tracker data as inputs to classifiers. Specifically, we present and compare different models, based on well-known Machine Learning methods. Data for training the models were collected using a static driving simulator, with real human subjects performing a specific secondary task (SURT) while driving. Different training methods, model characteristics and feature selection criteria have been compared. Based on our results, SVM has outperformed all the other ML methods, providing the highest classification rate for most of the subjects. Potential applications of this research include the design of adaptive IVIS and of "smarter" PADAS.Index Terms -Accident prevention; artificial intelligence and machine learning; driver' distraction and inattention; intelligent supporting systems.

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“…Selon Reimer, Mehler, Coughlin, Roy et Dusek (2011), le passage d'une simple tâche de conduite à une double tâche augmente la charge de travail (hausse de la fréquence cardiaque moyenne) et dégrade les performances avec une perte du contrôle latéral du véhicule sur la voie. De plus, plusieurs études ont montré que les environnements très complexes menaient à une hausse subjective de la charge de travail et à des dégradations de performances (De Waard, Brookhuis, & Hernandez-Gress, 2001;Baldauf, Burgard, & Wittmann, 2009;Di Stasi et al, 2010).…”

Section: Ii2 Surcharge De Travail En Environnement Complexe Ou Enunclassified

Surcharge de travail en fonction de l’environnement de conduite, de l’expérience des conducteurs et de leur état interne

Albentosa¹,

Berthelon²,

Galy³

2018

Le Travail Humain

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SUMMARY OVERLOAD AS A FUNCTION OF DRIVING ENVIRONMENT, DRIVERS' EXPERIENCE AND THEIR INTERNAL STATEThe factors that lead to young drivers having accidents have been the subject of much research. In this article, we review the current literature concerning the links between the complexity of the driving environment, driving experience, workload, anxiety, vigilance, and driving performance. First, we outline the features that characterize the environment complexity and the drivers' experience. Automatic and controlled processing is typically found in simple and complex environments, respectively. It varies as a function of driving experience automation that increases with experience. The lowest levels of cognitive load and best performance are found among more experienced drivers compared to novices. Secondly, overload can be explained as a function of the environment complexity and driving experience. Several studies confirm theories stating that simple and very complex environments are particularly prone to overload; at the same time, workload is lower, and performance is better for experienced drivers compared to novices. Finally, we examine the literature in order to understand how levels of anxiety and vigilance modulate the effects of environmental complexity and a driver's experience on overload. According to the literature, costly environments (i.e. simple and very complex), combined with a lack of experience can result in high energy expenditure. A high level of anxiety and a lack of vigilance can thus impair performance through an increase in workload. Moreover, the increased workload may be directly due to the costly environment and lack of experience, which can increase energy expenditure, raise anxiety, and lower vigilance. Drawing up on various theories and empirical studies that find a link between these factors, the aim is to establish a model of the links between the characteristics of the driving environment and the individual characteristics that lead to road accidents. That will make it possible to develop specific ways to raise awareness, and new driving training modules.

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The feasibility of detecting phone-use related driver distraction

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Cited by 41 publications

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Mobile phone use while driving: a major public health problem in an Arabian society, State of Qatar—mobile phone use and the risk of motor vehicle crashes

Mobile phone use while driving: a major public health problem in an Arabian society, State of Qatar—mobile phone use and the risk of motor vehicle crashes

Real-Time Detection System of Driver Distraction Using Machine Learning

Surcharge de travail en fonction de l’environnement de conduite, de l’expérience des conducteurs et de leur état interne

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