Driving distracted with friends: Effect of passengers and driver distraction on young drivers’ behavior

Zhang, Fangda; Mehrotra, Shashank; Roberts, Shannon C.

doi:10.1016/j.aap.2019.07.022

Cited by 32 publications

(17 citation statements)

References 48 publications

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“…For in-vehicle environmental factors, chatting with passengers when driving, comfortable temperature, and playing multimedia in the vehicle are prone to MW driving. With the presence of passengers, the mean proportion of time having elevated gravitational-force events in curves is significantly higher than when there was no passenger [4]. Driving with passengers or people they have met has an impact on driving behavior.…”

Section: Discussionmentioning

confidence: 99%

“…e U.S. National Highway Traffic Safety Administration reports that distracted driving caused 3,142 deaths in 2019 and expects a 10.59% increase per year for fatal traffic accidents [1]. Using a smartphone, tuning a radio, or chatting with passengers, and many other behaviors unrelated to the driving tasks can impact driving performance [2][3][4]. In addition, there is a kind of distracted driving behavior wherein the driver is thinking unrelated to the driving task at hand without any attention [5].…”

Section: Introductionmentioning

confidence: 99%

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Factors Identification and Prediction for Mind Wandering Driving Using Machine Learning

Lin

Zhang

Gong

et al. 2021

Journal of Advanced Transportation

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Traffic safety is affected by many complex factors. Mind wandering (MW) is a fatal cause affecting driving safety and is hard to be detected and prevented due to its uncertain and complex occurrence mechanism. The aim of this study was to propose a framework for analyzing and predicting MW based on readily available driving status data. The data used in this study are the single-trip information collected by the questionnaire, which includes drivers’ personal characteristics, contextual information in which MW occurs, and in-vehicle environmental factors. After investigating the extent of factors that influence MW, these chosen factors are used to forecast MW. Based on these results, we select factors reliable to be obtained in real life to forecast MW. To verify that the new factors explored are useful in improving the forecast accuracy, the compared analysis is conducted with the results found by our approach and the existing approaches. We compare results obtained by four machine-learning-enabled forecasting approaches on a real-life data set. The result shows that the factors found in this paper can significantly improve forecast accuracy. The confusion matrix, ROC curves, and AUC are conducted, and the performance of the gradient boosting decision tree algorithm is better than other forecast approaches. The importance rankings of most factors obtained by the Gradient Boosting Decision Tree and questionnaire are the same.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Factors Identification and Prediction for Mind Wandering Driving Using Machine Learning

Lin

Zhang

Gong

et al. 2021

Journal of Advanced Transportation

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“…Gershon et al [10] presented statistics on various distracted-driving behaviors that have resulted in accidents, indicating that interaction with passengers often causes accidents among teenage drivers. Zhang et al [11] investigated the influence of passengers on distracted driving, indicating that passengers aged 16-20 years have the highest negative impact on the driver. According to Maasaloa et al's [12] report, 70% of parents have to feed children during driving and 40% of parents have to pick up their children's toys.…”

Section: Introductionmentioning

confidence: 99%

Innovative Framework for Distracted-Driving Alert System Based on Deep Learning

Lin

Hsu

2022

IEEE Access

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Distracted driving is the most common cause of traffic accidents. According to a World Health Organization report, the number of traffic accidents has been increasing in recent years. To address this issue, distracted-driving recognition is an important area of traffic safety research. However, distracted behavior may be a part of a driver's regular tasks. For example, sometimes a delivery person must use his/her phone while driving. The use of walkie-talkies is required for container-truck drivers because they improve unloading efficiency and reduce the time cargo ships spend in a port, resulting in cost savings. While driving on the highway, it is sometimes necessary to tune the radio to receive an update on road conditions. Furthermore, drinking water is permitted while waiting for a traffic signal for an extended period of time. Therefore, for the distracted-driving alert system, the driving scenario is important. To address this issue, we present a novel framework herein that combines driving perception and driver behavior recognition to provide the driver with appropriate warnings. By combining driver perception and behavior recognition, our proposed framework can reduce false alerts. We also define different time-to-collision standards to achieve humane and effective warnings. We try to study various behaviors and define various time-tocollision standards for making safety-level decisions. For driving perception and driver behavior recognition, a modified convolutional neural network is used, which alerts the driver immediately.

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“…This includes a broad array of issues that impact on driver behaviour and ability to perform the tasks of driving including the use of recreational substances such as alcohol (Peck et al 2008, Lenné et al 2010, Mann et al 2010, Irwin et al 2017, Vollrath and Fischer 2017, Yadav and Velaga 2019 and cannabis (Lenné et al 2010, Downey et al 2013, Bondallaz et al 2016), driver's engagement in secondary tasks such as mind wandering (Yanko andSpalek 2014, Geden andFeng 2015), conversing on the phone (Caird et al 2008, Oviedo-Trespalacios et al 2016, Choudhary and Velaga 2017, Lipovac et al 2017 or with passengers (Charlton 2009). In addition to the role of external factors, studies on human factors have also explored the role of individual factors including driver's sociodemographic characteristics (Zhang et al 2019), personality traits (Deffenbacher et al 2002, Bogdan et al 2016, Deffenbacher et al 2016, Demir et al 2016, Sârbescu 2016, Tao et al 2017, style of driving (Taubman- Ben-Ari et al 2004), and driver's mental health, in particular, the effect of high-prevalence psychiatric conditions such as attention-deficit/hyperactivity disorder (ADHD) (Reimer et al 2010, Vaa 2014 or depression (Aduen et al 2015, Aduen et al 2018.…”

Section: Introductionmentioning

confidence: 99%