Examining drivers' eye glance patterns during distracted driving: Insights from scanning randomness and glance transition matrix

Wang, Yuan; Bao, Shan; Du, Wenjun; Ye, Zhirui; Sayer, James R.

doi:10.1016/j.jsr.2017.10.006

Cited by 44 publications

(27 citation statements)

References 11 publications

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“…A long fixation duration is related to increasing cognitive workloads and people are spending longer extracting information from their point of gaze (Gerhard et al, 2015). In addition, cognitive processing of the situation is impaired by driver distraction (Zeeb, Buchner, & Schrauf, 2016) and drivers in general have longer fixation duration during distracted driving when compared to nondistracted driving (Wang, Bao, Du, Ye, & James, 2017). These previous studies can also explain the results of this paper.…”

Section: Discussionmentioning

confidence: 99%

Study on drivers' visual perception characteristics during the take‐over of vehicle control in automated driving

Niu

Sun

et al. 2020

Hum Ftrs & Erg Mfg Svc

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A high degree of automated driving distracts drivers more easily, resulting in slow recognition of critical events during driving and slow responses to emergencies. Automated driving and manual switching processes are also prone to erroneous decisions. We conducted a simulated automated driving experiment to study participants' visual perception characteristics during the takeover of vehicle control. The present study used dynamic videos to imitate the driving situations when drivers returned their gaze from the distractive source to the road. We collected the drivers' eye movement data to analyze the search strategy and physiological characteristics of the drivers after the takeover reminder. The results showed that the instant information search method of the drivers was scanning of the driving scene. When the degree of distraction deepened and the hazard level of scenes increased, the pupil diameter of the drivers increased and the fixation duration became longer. These findings can help to design takeover warnings and support more intelligent automated driving systems to judge whether measures should be taken to interfere with the driver's operation to avoid collisions. Furthermore, the drivers' fixation point distribution focused on the left side and the lower side of the scene. We suggest that the takeover warning is displayed in the head-up display. This study provides a better understanding of drivers' visual perception characteristics when drivers' eyesight returns from other distractors to the driving scene and a good theoretical basis for the design of hazard warning information for automated driving.

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Section: Discussionmentioning

confidence: 99%

Study on drivers' visual perception characteristics during the take‐over of vehicle control in automated driving

Niu

Sun

et al. 2020

Hum Ftrs & Erg Mfg Svc

View full text Add to dashboard Cite

show abstract

“…A higher visual entropy indicates higher randomness or higher visual scanning complexity, being likely to scan more areas with a shorter period of time in each area (Wang et al, ). The analysis of visual entropy revealed no significant difference among the 4 secondary task types.…”

Section: Discussionmentioning

confidence: 99%

“…Bao and Boyle () investigated age difference in driver's visual scanning at intersections, by measuring the time proportion of scanning to three areas (i.e., left, right sides, and rear‐view mirror), and visual entropy rate as a measure of randomness in visual scanning. In recent research by Wang, Bao, Du, Ye, & Sayer (), drivers’ eye glance patterns were quantified when drivers were engaged in cell phone‐related visual‐manual tasks. However, the existing literature is not sufficient to determine whether driving safety can be evaluated using both static and dynamic glance measures; moreover, diverse modalities of the secondary task have not been investigated.…”

Section: Introductionmentioning

confidence: 99%

Driver glance behaviors and scanning patterns: Applying static and dynamic glance measures to the analysis of curve driving with secondary tasks

Jeong

Kang

Liu

2019

Hum Ftrs & Erg Mfg Svc

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Performing secondary tasks (or non‐driving‐related tasks) while driving on curved roads may be risky and unsafe. The purpose of this study was to explore whether driving safety in situations involving curved roads and secondary tasks can be evaluated using multiple measures of eye movement. We adopted Markov‐based transition algorithms (i.e., transition/stationary probabilities, entropy) to quantify drivers’ dynamic eye movement patterns, in addition to typical static visual measures, such as frequency and duration of glances. The algorithms were evaluated with data from an experiment (Jeong & Liu, 2019) involving multiple road curvatures and stimulus‐response secondary task types. Drivers were more likely to scan only a few areas of interest with a long duration in sharper curves. Total head‐down glance time was longer in less sharp curves in the experiment, but the probability of head‐down glances was higher in sharper curves over the long run. The number of reliable transitions between areas of interest varied with the secondary task type. The visual scanning patterns for visually undemanding tasks were as random as those for visually demanding tasks. Markov‐based measures of dynamic eye movements provided insights to better understand drivers’ underlying mental processes and scanning strategies, compared with typical static measures. The presented methods and results can be useful for in‐vehicle systems design and for further analysis of visual scanning patterns in the transportation domain.

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“…In the context of driving, it results in spatial gaze concentration as well as more frequent and longer fixations away from the road, which can affect the detection of potential hazards [36][37][38][39]. In addition to classical eye movement metrics, including fixation rate and duration, entropy has been derived from information theory [40] to provide a quantitative analysis of gaze behavior in naturalistic environments such as flight simulation [41][42][43][44], surgery [45][46][47] and driving [48][49][50][51]. The entropy captures visual scanning complexity and, by extension, the spatial distribution of visual attention using two measures.…”

Section: Introductionmentioning

confidence: 99%

Eye-head coordination and dynamic visual scanning as indicators of visuo-cognitive demands in driving simulator

et al. 2020

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Driving is an everyday task involving a complex interaction between visual and cognitive processes. As such, an increase in the cognitive and/or visual demands can lead to a mental overload which can be detrimental for driving safety. Compiling evidence suggest that eye and head movements are relevant indicators of visuo-cognitive demands and attention allocation. This study aims to investigate the effects of visual degradation on eye-head coordination as well as visual scanning behavior during a highly demanding task in a driving simulator. A total of 21 emmetropic participants (21 to 34 years old) performed dual-task driving in which they were asked to maintain a constant speed on a highway while completing a visual search and detection task on a navigation device. Participants did the experiment with optimal vision and with contact lenses that introduced a visual perturbation (myopic defocus). The results indicate modifications of eye-head coordination and the dynamics of visual scanning in response to the visual perturbation induced. More specifically, the head was more involved in horizontal gaze shifts when the visual needs were not met. Furthermore, the evaluation of visual scanning dynamics, based on time-based entropy which measures the complexity and randomness of scanpaths, revealed that eye and gaze movements became less explorative and more stereotyped when vision was not optimal. These results provide evidence for a reorganization of both eye and head movements in response to increasing visual-cognitive demands during a driving task. Altogether, these findings suggest that eye and head movements can provide relevant information about visuo-cognitive demands associated with complex tasks. Ultimately, eye-head coordination and visual scanning dynamics may be good candidates to estimate drivers’ workload and better characterize risky driving behavior.

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Examining drivers' eye glance patterns during distracted driving: Insights from scanning randomness and glance transition matrix

Cited by 44 publications

References 11 publications

Study on drivers' visual perception characteristics during the take‐over of vehicle control in automated driving

Study on drivers' visual perception characteristics during the take‐over of vehicle control in automated driving

Driver glance behaviors and scanning patterns: Applying static and dynamic glance measures to the analysis of curve driving with secondary tasks

Eye-head coordination and dynamic visual scanning as indicators of visuo-cognitive demands in driving simulator

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