What is the difference between perceived and actual risk of distracted driving? A field study on a real highway

Li, Zhen; Wang, Chang; Fu, Rui; Sun, Qinyu; Zhang, Hongjia

doi:10.1371/journal.pone.0231151

Cited by 14 publications

(3 citation statements)

References 42 publications

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“…"Naturalistic driving study" (NDS) provides insight into this issue under natural driving conditions [71]. "On-road test" is a middle way of simulation and NDS: similar to NDSs, participants engage in real driving tasks; similar to simulation, non-driving tasks are assigned by experimenters [72,73]. Furthermore, the two epidemiological methods, "observational study" and "questionnaire survey", reflect social behaviors and public opinions in time and thus support subsequent technological development, educational campaign and legislation [74,75].…”

Section: Plos Onementioning

confidence: 99%

Understanding the domain of driving distraction with knowledge graphs

Feng

2022

PLoS ONE

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This paper aims to provide insight into the driving distraction domain systematically on the basis of scientific knowledge graphs. For this purpose, 3,790 documents were taken into consideration after retrieving from Web of Science Core Collection and screening, and two types of knowledge graphs were constructed to demonstrate bibliometric information and domain-specific research content respectively. In terms of bibliometric analysis, the evolution of publication and citation numbers reveals the accelerated development of this domain, and trends of multidisciplinary and global participation could be identified according to knowledge graphs from Vosviewer. In terms of research content analysis, a new framework consisting of five dimensions was clarified, including “objective factors”, “human factors”, “research methods”, “data” and “data science”. The main entities of this domain were identified and relations between entities were extracted using Natural Language Processing methods with Python 3.9. In addition to the knowledge graph composed of all the keywords and relationships, entities and relations under each dimension were visualized, and relations between relevant dimensions were demonstrated in the form of heat maps. Furthermore, the trend and significance of driving distraction research were discussed, and special attention was given to future directions of this domain.

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Section: Plos Onementioning

confidence: 99%

Understanding the domain of driving distraction with knowledge graphs

Feng

2022

PLoS ONE

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“…Motion demands include task completion time (TCT) as well as the duration of time hands are off the steering wheel. A large number of driving simulator-based experiments have been conducted to show that as a secondary task takes up more visual resources and as TCT increases, lateral driving performance will be worse, and the risk of lane departure will increase significantly [15][16][17].…”

Section: Research On the Impact Of Secondary Tasks On Resourcementioning

confidence: 99%

Differences in Drivers’ Glance Behavior and Lateral Control Ability during Full-Touch Interaction Mode and Conventional Interaction Mode: A Case Study of Road Experiments

Zhao

et al. 2022

Journal of Advanced Transportation

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In recent years, the full-touch human–machine interaction (HMI) mode has gained popularity in the automotive market. However, little research has been conducted on how this interaction mode affects drivers’ glance behavior and lateral control ability. In this study, we evaluated the visual engagement and driving performance of 30 participants while driving two vehicles equipped with either the full-touch interaction mode (FTIM) or the conventional interaction mode (CIM) provided by the original equipment manufacturer (OEM). We found that both air conditioning–related tasks required more visual engagement, longer task completion time, and worse lateral vehicle control under FTIM. Furthermore, the gray correlation analysis demonstrated that FTIM exhibited slightly different disadvantages in the two secondary tasks. In the temperature adjustment task, the correlations of glance behavior and lateral control ability between the two interactive modes were 0.688 and 0.680, respectively. In the airflow adjustment task, the correlations of glance behavior and lateral control ability between the two interactive modes were 0.659 and 0.668, respectively. In addition, this study revealed that driving speed had significant effects on glance behavior and lateral driving performance in both interaction modes. As speed increased, self-adjusting glance behavior was evident in performing the secondary task; however, this behavior could not compensate for the deterioration in lateral driving performance caused by the increased speed. The findings will help improve drivers’ perception of FTIM and provide theoretical guidance for the design development of HMI mode.

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“…About 1.3 million lives are ended as a result of road traffic accidents every year, and a range of 20–50 million people suffer injuries and disability [ 1 ]. It is reported that the majority of road traffic accidents are contributed to drivers, and distracted driving is the most important contributing factor [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Effects of Distracting Behaviors on Driving Workload and Driving Performance in a City Scenario

Luo

Yi²,

Shao

et al. 2022

IJERPH

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Distractors faced by drivers grow continuously, and concentration on driving becomes increasingly difficult, which has detrimental influences on road traffic safety. The present study aims to investigate changes in driving workload and driving performance caused by distracting tasks. The recruited subjects were requested to drive along a city route in a real vehicle and perform three secondary tasks sequentially. Electrocardiography and driving performance were measured. Heart rate variability (HRV) was adopted to quantitatively analyze the driving workload. Findings show that: (i) increments are noticed in the root mean square differences of successive heartbeat intervals (RMSSD), the standard deviation of normal-to-normal peak (SDNN), the heart rate growth rate (HRGR), and the ratio of low-frequency to high-frequency powers (LF/HF) compared to undistracted driving; (ii) the hands-free phone conversation task has the most negative impacts on driving workload; (iii) vehicle speed reduces due to secondary tasks while changes in longitudinal acceleration exhibit inconsistency; (iv) the experienced drivers markedly decelerate during hands-free phone conversation, and HRGR shows significant differences in both driving experience and gender under distracted driving conditions; (v) correlations exist between HRV and driving performance, and LF/HF correlates positively with SDNN/RMSSD in the hands-free phone conversation and chatting conditions while driving.

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What is the difference between perceived and actual risk of distracted driving? A field study on a real highway

Cited by 14 publications

References 42 publications

Understanding the domain of driving distraction with knowledge graphs

Understanding the domain of driving distraction with knowledge graphs

Differences in Drivers’ Glance Behavior and Lateral Control Ability during Full-Touch Interaction Mode and Conventional Interaction Mode: A Case Study of Road Experiments

Effects of Distracting Behaviors on Driving Workload and Driving Performance in a City Scenario

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