Visual Attention During Simulated Autonomous Driving in the US and Japan

Shinohara, Yumiko; Currano, Rebecca; Ju, Wendy; Nishizaki, Yukiko

doi:10.1145/3122986.3122991

Cited by 15 publications

(8 citation statements)

References 20 publications

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“…Likewise, ref. [42] revealed consistency between fixation count and fixation duration on an object and showed that SA during simulated automated driving might be attributed to visual fixation patterns for various traffic scene elements. Similarly, ref.…”

Section: Eye-tracking Metrics and Oculomotor Eventsmentioning

confidence: 78%

Situational Awareness Assessment of Drivers Boosted by Eye-Tracking Metrics: A Literature Review

Arias-Portela,

Mora-Vargas,

Caro

2024

Applied Sciences

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The conceptual framework for assessing the situational awareness (SA) of drivers consists of three hierarchical levels: perception of the elements of the environment, comprehension of the elements, and decision-making in the near future. A common challenge in evaluating SA is the determination of the available subjective and objective techniques and their selection and integration into methodologies. Among the objective techniques, eye tracking is commonly used, considering the influence of gaze behavior on driving. This review is presented as an innovative approach to the subject matter, introducing physiological metrics based on eye tracking and investigating their application in assessing the SA of drivers. In addition, experiments and methodologies that revealed patterns at the three levels of SA were identified. For this purpose, databases were searched, and 38 papers were considered. Articles were clustered according to prevalent themes such as eye-tracking metrics, eye-tracking devices, experiment design, and the relationship between SA and eye-tracking. This review summarizes the main metrics and key findings for each article and reveals a wide relationship between the eye-tracking metrics and SA. The influence of appropriately calibrated equipment, refined data collection protocols, and adequate selection of the eye-tracking metrics was examined. Further reviews are needed to systematically collect more evidence.

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Section: Eye-tracking Metrics and Oculomotor Eventsmentioning

confidence: 78%

Situational Awareness Assessment of Drivers Boosted by Eye-Tracking Metrics: A Literature Review

Arias-Portela,

Mora-Vargas,

Caro

2024

Applied Sciences

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“…Eye tracking has a variety of application areas as well as research areas in today's world. In human-machine interaction it is used as a new input signal [23,1], in computer graphics as a constraint for the area to be rendered [57,38], in medicine using more eye features as data for self-diagnosis systems [31,51,33] or to measure the eyes [39,7], in the field of psychology it is used to detect neurological diseases such as Alzheimer's [5,44], in behavioral research to evaluate expertise as well as train students [42,28], and many more like driver monitoring in autonomous driving [35,50] etc. This wide range of research and application areas requires that new eye features as well as more robust algorithms be easily and quickly deployable by everyone.…”

Section: Introductionmentioning

confidence: 99%

Pistol: Pupil Invisible Supportive Tool to extract Pupil, Iris, Eye Opening, Eye Movements, Pupil and Iris Gaze Vector, and 2D as well as 3D Gaze

Fuhl¹,

Weber²,

Kasneci³

2022

Preprint

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This paper describes a feature extraction and gaze estimation software, named Pistol that can be used with Pupil Invisible projects and other eye trackers in the future. In offline mode, our software extracts multiple features from the eye including, the pupil and iris ellipse, eye aperture, pupil vector, iris vector, eye movement types from pupil and iris velocities, marker detection, marker distance, 2D gaze estimation for the pupil center, iris center, pupil vector, and iris vector using Levenberg Marquart fitting and neural networks. The gaze signal is computed in 2D for each eye and each feature separately and for both eyes in 3D also for each feature separately. We hope this software helps other researchers to extract state-of-the-art features for their research out of their recordings. The tool can be downloaded here https://atreus.informatik.uni-tuebingen.de/seafile/d/8e2ab8c3fdd444e1a135/?p=%2FPISTOL&mode=list.

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“…In today's world, eye tracking can be found in many fields. This includes human-machine interaction [60,1], computer graphics [84,70], self-diagnosis systems [62,81,65], measurement of eye misalignment [71,15], detection of neurological diseases [10,74], behavioral research [66,7], learning systems [73,61], expertise determination [5,77], driver observation [67,78,24], market research [4,79], and many more.…”

Section: Introductionmentioning

confidence: 99%

“…In the area of driver observation, the same applies. Here it is necessary to detect inattentive behavior or drowsiness and warn the driver [67,78]. These systems also need to be tested on a lot of data to ensure their reliability and optimize their usability for the driver.…”

Section: Introductionmentioning

confidence: 99%

HPCGen: Hierarchical K-Means Clustering and Level Based Principal Components for Scan Path Genaration

Fuhl¹

2022

Preprint

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In this paper, we present a new approach for decomposing scan paths and its utility for generating new scan paths. For this purpose, we use the K-Means clustering procedure to the raw gaze data and subsequently iteratively to find more clusters in the found clusters. The found clusters are grouped for each level in the hierarchy, and the most important principal components are computed from the data contained in them. Using this tree hierarchy and the principal components, new scan paths can be generated that match the human behavior of the original data. We show that this generated data is very useful for generating new data for scan path classification but can also be used to generate fake scan paths. https://atreus.informatik.uni-tuebingen.de/seafile/d/8e2ab8c3fdd444e1a135/?p=%2FHPCGen&mode=list.

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Visual Attention During Simulated Autonomous Driving in the US and Japan

Cited by 15 publications

References 20 publications

Situational Awareness Assessment of Drivers Boosted by Eye-Tracking Metrics: A Literature Review

Situational Awareness Assessment of Drivers Boosted by Eye-Tracking Metrics: A Literature Review

Pistol: Pupil Invisible Supportive Tool to extract Pupil, Iris, Eye Opening, Eye Movements, Pupil and Iris Gaze Vector, and 2D as well as 3D Gaze

HPCGen: Hierarchical K-Means Clustering and Level Based Principal Components for Scan Path Genaration

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