Analysis of Temporal Relationships between Eye Gaze and Peripheral Vehicle Behavior for Detecting Driver Distraction

Hirayama, Takatsugu; Mase, Kenji; Takeda, Kazuya

doi:10.1155/2013/285927

Cited by 11 publications

(4 citation statements)

References 23 publications

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“…Driving at intersections is just a small part of this variety of driving situations and conditions. Similar correlations between driving performance and visual distraction were reported by other researchers (Hirayama et al, 2013;Yang et al, 2015).…”

Section: Discussionsupporting

confidence: 89%

The Effects of an In-vehicle Collision Warning System on Older Drivers' On-road Head Movements at Intersections

et al. 2021

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With age might come a decline in crucial driving skills. The effect of a collision warning system (CWS) on older drivers' head movements behavior at intersections was examined.Methods: Twenty-six old-adults, between 55 and 64 years of age, and 16 Older drivers between 65 and 83 years of age, participated in the study. A CWS (Mobileye Inc.) and a front-back in-vehicle camera (IVC) were installed in each of the participants' own vehicles for 6 months. The CWS was utilized to identify unsafe events during naturalistic driving situations, and the IVC was used to capture head direction at intersections. The experimental design was conducted in three phases (baseline, intervention, and carryover), 2 months each. Unsafe events were recorded by the CWS during all phases of the study. In the second phase, the CWS feedback was activated to examine its effect on drivers' head movement' behavior at intersections.Results: Older drivers (65+) drove significantly more hours in total during the intervention phase (M = 79.1 h, SE = 10) than the baseline phase (M = 39.1 h, SE = 5.3) and the carryover phase (M = 37.7 h, SE = 5.4). The study revealed no significant differences between the head movements of older and old-adult drivers at intersections. For intersection on the left direction, a significant improvement in drivers' head movements' behavior was found at T-junctions, turns and four-way intersections from phase 1 to phase 3 (p < 0.01), however, two intersection types presented a decrease along the study phases. The head movements' behavior at roundabouts and merges was better at phase 1 compared to phase 3 (p < 0.01). There was no significant reduction of the mean number of CWS unsafe events across the study phases.Conclusions: The immediate feedback provided by the CWS was effective in terms of participants' head movements at certain intersections but was harmful in others. However, older drivers drove many more hours during the active feedback phase, implying that they trusted the system. Therefore, in the light of this complex picture, using the technological feedback with older drivers should be followed with an additional mediation or follow-up to ensure safety.

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Section: Discussionsupporting

confidence: 89%

The Effects of an In-vehicle Collision Warning System on Older Drivers' On-road Head Movements at Intersections

et al. 2021

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“…Indeed, visual lane analysis can be used for “higher-order tasks”, which are defined by interacting with other modules in a complex driver assistance system (e.g., understanding the driver’s attentiveness—distraction—to the lane-keeping task [272]). Hirayama et al [273] focused on temporal relationships between the driver’s eye gaze and the peripheral vehicles behaviour. In particular, they concluded that the timing when a driver gazes towards the overtaking event under cognitive distraction is later than that under the neutral state.…”

Section: General Discussion and Challenges Aheadmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

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Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

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“…The authors of [184] reported a correlation between driving performance and visual distraction by utilizing gaze duration as a detection feature. The existence of such correlation was also confirmed by the authors of [185].…”

Section: Measurement Approachesmentioning

confidence: 99%

Gaze and Eye Tracking: Techniques and Applications in ADAS

Khan

Lee

2019

Sensors

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Tracking drivers’ eyes and gazes is a topic of great interest in the research of advanced driving assistance systems (ADAS). It is especially a matter of serious discussion among the road safety researchers’ community, as visual distraction is considered among the major causes of road accidents. In this paper, techniques for eye and gaze tracking are first comprehensively reviewed while discussing their major categories. The advantages and limitations of each category are explained with respect to their requirements and practical uses. In another section of the paper, the applications of eyes and gaze tracking systems in ADAS are discussed. The process of acquisition of driver’s eyes and gaze data and the algorithms used to process this data are explained. It is explained how the data related to a driver’s eyes and gaze can be used in ADAS to reduce the losses associated with road accidents occurring due to visual distraction of the driver. A discussion on the required features of current and future eye and gaze trackers is also presented.

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Analysis of Temporal Relationships between Eye Gaze and Peripheral Vehicle Behavior for Detecting Driver Distraction

Cited by 11 publications

References 23 publications

The Effects of an In-vehicle Collision Warning System on Older Drivers' On-road Head Movements at Intersections

The Effects of an In-vehicle Collision Warning System on Older Drivers' On-road Head Movements at Intersections

Driver Distraction Using Visual-Based Sensors and Algorithms

Gaze and Eye Tracking: Techniques and Applications in ADAS

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