Do eye movements reflect driving manoeuvres?

Lethaus, Firas; Rataj, Jürgen

doi:10.1049/iet-its:20060058

Cited by 35 publications

(17 citation statements)

References 3 publications

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“…It has been known for some time now that the direction of driver gaze possesses predictive qualities in regards to the detection of driver intent and following driver maneuvers [2]. In particular, the relation between gaze direction and subsequent vehicle steering has been clearly demonstrated in several studies [3], [4], in which it was found that driver gaze generally points towards the tangent point of a road bend, and does so 0.8 to 1s before effecting steering maneuvers, providing evidence that the eyes begin processing information before the associated action has begun [4].…”

Section: A Literature Surveymentioning

confidence: 99%

Multi-depth cross-calibration of remote eye gaze trackers and stereoscopic scene systems

Kowsari

Beauchemin

Bauer

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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Abstract-We present a robust and accurate technique for the cross-calibration of 3D remote gaze trackers with stereoscopic scene vision systems between which no common imaging area exists. We empirically demonstrate that a multidepth calibration approach yields remarkably superior results for obtaining 3D Point-of-Gaze (PoG) when compared with traditional methods using monocular scene cameras and coplanar eye gaze calibration points.

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Section: A Literature Surveymentioning

confidence: 99%

Multi-depth cross-calibration of remote eye gaze trackers and stereoscopic scene systems

Kowsari

Beauchemin

Bauer

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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“…A driving manoeuvre can be executed in a variety of styles. It can differ in time profile, action sequence and intensity of action, e. g. visual behaviour during execution of lane changes [4], [5].…”

Section: Driver Intent Recognitionmentioning

confidence: 99%

“…Research of lane change manoeuvres shows that the driver's glance behaviour can be an early indicator for the intent to change lanes [4], [5], [6], and [7]. To prepare a safe lane change the driver needs to perform specific glance sequences covering distinct spatial regions of the vehicles' surroundings.…”

Section: Driver Intent Recognitionmentioning

confidence: 99%

Head tracking based glance area estimation for driver behaviour modelling during lane change execution

Pech

Lindner

Wanielik

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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This paper describes a method to analyse driver behaviour before lane change manoeuvres to detect the lane change intent before the actual manoeuvre itself is initiated. Recent research shows that driver's visual behaviour is an essential indicator to estimate intent to change lane. To create stochastic models for this process, it is necessary to detect which areas are frequently taken into focus by the driver (like looking towards the front window and side mirrors, for example). In a laboratory setting these regions could directly be measured using eye tracking methods. When focusing on solutions which can be integrated into cars under realistic settings, common eye tracking methods fail. More stable solutions are head tracking systems, mostly based on mono or stereo cameras. In this article an approach is presented for estimating the drivers glance areas from head tracking data.

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“…Bhise et al [2] found that drivers rotate their heads before searching visually in real vehicle driving scenarios. Lethaus and Rataj [3] conducted both real and simulated driving tests. After collecting data on drivers' visual characteristics in real vehicle tests, they proposed driving behavioral predictors using vision related parameters, drawing the conclusion that eye movements precede other behavioral features.…”

Section: Introductionmentioning

confidence: 99%

Division of Area of Fixation Interest for Real Vehicle Driving Tests

Guo

Shao

et al. 2017

Mathematical Problems in Engineering

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The area of interest (AOI) reflects the degree of attention of a driver while driving. The division of AOI is visual characteristic analysis required in both real vehicle tests and simulated driving scenarios. Some key eye tracking parameters and their transformations can only be obtained after the division of AOI. In this study, 9 experienced and 7 novice drivers participated in real vehicle driving tests. They were asked to drive along a freeway section and a highway section, wearing the Dikablis eye tracking device. On average, 8132 fixation points for each driver were extracted. After coordinate conversion, the MSAP (Mean Shift Affinity Propagation) method is proposed to classify the distribution of fixation points into a circle type and a rectangle type. Experienced drivers’ fixation behavior falls into the circle type, in which fixation points are concentrated. Novice drivers’ fixation points, which are decentralized, are illustrated in the rectangle type. In the clustering algorithm, the damping coefficient λ determines the algorithm convergence, and the deviation parameter p mainly affects the number of clusters, where larger p values generate more clusters. This study not only provides the cluster type and cluster counts, but also presents the borderlines for each cluster. The findings provide significant contribution to eye tracking research.

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Do eye movements reflect driving manoeuvres?

Cited by 35 publications

References 3 publications

Multi-depth cross-calibration of remote eye gaze trackers and stereoscopic scene systems

Multi-depth cross-calibration of remote eye gaze trackers and stereoscopic scene systems

Head tracking based glance area estimation for driver behaviour modelling during lane change execution

Division of Area of Fixation Interest for Real Vehicle Driving Tests

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