How are different sources of distraction associated with at-fault crashes among drivers of different age gender groups?

Liang, Ou Stella; Yang, Christopher C.

doi:10.1016/j.aap.2021.106505

Cited by 15 publications

(11 citation statements)

References 28 publications

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“…A general approach to road safety is to identify and analyze all distraction activities that can lead to a crash [8,9]. For example, in 2019, the road traffic injuries statistics showed that a total of 36,096 deaths were reported in the US, of which 8.7 percent were attributed to driver distraction due to phone use, eating, and so on [10].…”

Section: Introductionmentioning

confidence: 99%

“…Driver distraction can be defined as "any activity that diverts attention from driving, including talking or texting on the cell phone, eating and drinking, talking to people in the vehicle, fiddling with the stereo, entertainment or navigation system" [16]. The most common sources of distractions are mobile phone use, interaction with passengers, drinking, eating, and controlling in-vehicle devices [9]. There are three basic techniques to determine the distracted state of the driver: studying drivers' visual scanning patterns, detecting physiological signals, and evaluating driving performance.…”

Section: Introductionmentioning

confidence: 99%

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Texting While Driving: A Literature Review on Driving Simulator Studies

Voinea

Boboc

Buzdugan

et al. 2023

IJERPH

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Road safety is increasingly threatened by distracted driving. Studies have shown that there is a significantly increased risk for a driver of being involved in a car crash due to visual distractions (not watching the road), manual distractions (hands are off the wheel for other non-driving activities), and cognitive and acoustic distractions (the driver is not focused on the driving task). Driving simulators (DSs) are powerful tools for identifying drivers’ responses to different distracting factors in a safe manner. This paper aims to systematically review simulator-based studies to investigate what types of distractions are introduced when using the phone for texting while driving (TWD), what hardware and measures are used to analyze distraction, and what the impact of using mobile devices to read and write messages while driving is on driving performance. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. A total of 7151 studies were identified in the database search, of which 67 were included in the review, and they were analyzed in order to respond to four research questions. The main findings revealed that TWD distraction has negative effects on driving performance, affecting drivers’ divided attention and concentration, which can lead to potentially life-threatening traffic events. We also provide several recommendations for driving simulators that can ensure high reliability and validity for experiments. This review can serve as a basis for regulators and interested parties to propose restrictions related to using mobile phones in a vehicle and improve road safety.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Texting While Driving: A Literature Review on Driving Simulator Studies

Voinea

Boboc

Buzdugan

et al. 2023

IJERPH

View full text Add to dashboard Cite

show abstract

“…Previously, many scholars have investigated driving distractions, mostly from the driver's perspective, vehicle performance perspective, and different road environment perspectives to study the specific causes of driver distraction, the potential hazards of distraction to traffic accidents, and the establishment of different models to predict and analyze the driver's driving status. In the driver's perspective to study including the driver's eye-movement characteristics, commonly used are oculomotor to collect data on eye gaze duration, sweeping paths, eye states such as eye-opening and closing, and the angle of the driver's head rotation, and then include other characteristics of the driver themselves, such as aggressive driver cognitive failure can have a direct impact on distracted driving tendencies [3], and adolescents and adults are more likely to be distracted than older adults [4].…”

Section: Introductionmentioning

confidence: 99%

A Distracted Driving Detection Model Based On Driving Performance

Shang

Sun

et al. 2023

IEEE Access

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A large body of evidence on traffic accidents indicates that distracted driving is a major cause of accidents. To investigate the relationship between driver performance in both normal and distracted states and to discriminate which state a driver is in based on driving performance, a simulator was used to simulate drivers driving in an urban road environment, and a distraction task was designed to motivate drivers to enter a cognitive distraction state. Data were collected from 80 drivers in each of the two driving states, and a database was built. Deep neural network (DNN) is a multilayer perceptron structure, relative to the defects of other classical algorithms with unclear classification thresholds, DNN can be used to process high-dimensional data through its neural node node-linked structure. Combining the Gray Wolf algorithm (GWO) for the initial weights and thresholds of the DNN network as a whole, a four-layer network structure is built to predict the test samples, while support vector machines ( SVM) and random forest as controls. The results show that the accuracy of DNN for predicting test samples is 95.13%, that of SVM is 77.56%, and that of random forest is 72.32%. The F1 score of the DNN model is higher than that of SVM, and the detection effect is better and can be applied to detect the driving status of drivers.INDEX TERMS Driving behavior, cognitive distraction, simulated driving, deep neural networks, support vector machines.

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“…External distractions (outside-of-vehicle) can refer to unimportant events or objects outside the car that the driver is focusing on [ 20 ], such as billboards, work zones, crash scenes, police cars [ 21 ], or, in other words, the rubbernecking at on-road events [ 22 ]. Recent studies showed that interaction with a passenger, entertainment, mobile devices, and external scenes are the most common distractions for drivers of different age groups [ 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Talking on the Phone While Driving: A Literature Review on Driving Simulator Studies

Boboc

Voinea

Buzdugan

et al. 2022

IJERPH

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Distracted driving is a growing concern around the world and has been the focus of many naturalistic and simulator-based studies. Driving simulators provide excellent practical and theoretical help in studying the driving process, and considerable efforts have been made to prove their validity. This research aimed to review relevant simulator-based studies focused on investigating the effects of the talking-on-the-phone-while-driving distraction on drivers’ behavior. This work is a scoping review which followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. The search was performed on five databases, covering twenty years of research results. It was focused on finding answers to three research questions that could offer an overview of the main sources of distraction, the research infrastructure, and the measures that were used to analyze and predict the effects of distractions. A number of 4332 studies were identified in the database search, from which 83 were included in the review. The main findings revealed that TPWD distraction negatively affects driving performance, exposing drivers to dangerous traffic situations. Moreover, there is a general understanding that the driver’s cognitive, manual, visual, and auditory resources are all involved, to a certain degree, when executing a secondary task while driving.

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How are different sources of distraction associated with at-fault crashes among drivers of different age gender groups?

Cited by 15 publications

References 28 publications

Texting While Driving: A Literature Review on Driving Simulator Studies

Texting While Driving: A Literature Review on Driving Simulator Studies

A Distracted Driving Detection Model Based On Driving Performance

Talking on the Phone While Driving: A Literature Review on Driving Simulator Studies

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