Driver Inattention Detection based on Eye Gaze—Road Event Correlation

Fletcher, Luke; Zelinsky, Alexander

doi:10.1177/0278364908099459

Cited by 116 publications

(61 citation statements)

References 28 publications

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“…To put our approach into practical use, we will refer to peripheral vehicle-tracking systems based on computer vision techniques [24,33] and replace the laser scanner with a camera-based system.…”

Section: Discussionmentioning

confidence: 99%

“…Some researchers have investigated the correlations between gaze directions and traffic object positions, for example, road curvature, oncoming traffic, road signs, and pedestrians [22][23][24], and have shown that the correlations are high under the neutral state while driving only. Most of the researchers, however, dealt mainly with spatial correlation.…”

Section: Hypothesismentioning

confidence: 99%

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

Hirayama

Mase

Takeda

2013

International Journal of Vehicular Technology

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A car driver's cognitive distraction is a main factor behind car accidents. One's state of mind is subconsciously exposed as a reaction reflecting it by external stimuli. A visual event that occurs in front of the driver when a peripheral vehicle overtakes the driver's vehicle is regarded as the external stimulus. We focus on temporal relationships between the driver's eye gaze and the peripheral vehicle behavior. The analysis result showed that the temporal relationships depend on the driver's state. In particular we confirmed that the timing of the gaze toward the stimulus under the distracted state induced by a music retrieval task using an automatic speech recognition system is later than that under a neutral state while only driving without the secondary cognitive task. This temporal feature can contribute to detecting the cognitive distraction automatically. A detector based on a Bayesian framework using this feature achieves better accuracy than one based on the percentage road center method.

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Section: Discussionmentioning

confidence: 99%

Section: Hypothesismentioning

confidence: 99%

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

Hirayama

Mase

Takeda

2013

International Journal of Vehicular Technology

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“…It is also worth noting that gazing at an instrument does not guarantee that the information is processed, which is often referred to as "looked but failed to see". However, it is possible to reduce the risk of not seeing the information and to prevent "not looked therefore failed" using the eye-tracking [62]. Figure 3 illustrates this simple logic in the form of a flowchart of stage III.…”

Section: Stage Iii: Gaze-based Flight Deck Adaptationmentioning

confidence: 99%

The Neuroergonomics of Aircraft Cockpits: The Four Stages of Eye-Tracking Integration to Enhance Flight Safety

Peysakhovich

Lefrancois

Dehais

et al. 2018

Safety

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Abstract:Commercial aviation is currently one of the safest modes of transportation; however, human error is still one major contributing cause of aeronautical accidents and incidents. One promising avenue to further enhance flight safety is Neuroergonomics, an approach at the intersection of neuroscience, cognitive engineering and human factors, which aims to create better human-system interaction. Eye-tracking technology allows users to "monitor the monitoring" by providing insights into both pilots' attentional distribution and underlying decisional processes. In this position paper, we identify and define a framework of four stages of step-by-step integration of eye-tracking systems in modern cockpits. Stage I concerns Pilot Training and Flight Performance Analysis on-ground; stage II proposes On-board Gaze Recordings as extra data for the "black box" recorders; stage III describes Gaze-Based Flight Deck Adaptation including warning and alerting systems, and, eventually, stage IV prophesies Gaze-Based Aircraft Adaptation including authority taking by the aircraft. We illustrate the potential of these four steps with a description of incidents or accidents that we could certainly have avoided thanks to eye-tracking. Estimated milestones for the integration of each stage are also proposed together with a list of some implementation limitations. We believe that the research institutions and industrial actors of the domain will all benefit from the integration of the framework of the eye-tracking systems into cockpits.

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“…Furthermore, eye movement research in the automotive area, to a great extent, focuses on capturing the driver's eye movements in order to detect the driver's state in terms of, e.g., inattention (e.g., [6]), in-alertness or fatigue (e.g., [2]), or vigilance (e.g., [1]), while more recent research addresses the gaze of the driver as means t o interact with in-vehicle information systems (IVIS, [11]). The gaze of the front-seat passenger and its potential as further source of info rmation for the driver is mostly neglected.…”

Section: Gaze In the Carmentioning

confidence: 99%