Dynamic simulation and prediction of drivers’ attention distribution

Wortelen, Bertram; Baumann, Martin; Lüdtke, Andreas

doi:10.1016/j.trf.2013.09.019

Cited by 35 publications

(28 citation statements)

References 8 publications

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“…These models support the Adaptive Information Expectancy (AIE) model [5] to predict the distribution of attention and the average reaction time to a certain event. Further on, we exchanged the hard-coded widgets palette of CogTool to annotate the designs with a model-based backend that enables us to define new annotation options (like for the IS in this case) without recompiling the tool.…”

Section: Tool-supported Attention Prediction With the Heementioning

confidence: 86%

“…Earlier AIE model applications extracted the expectancy coefficients during the simulation of the cognitive model in interaction with realistic environment simulations [5], [8]. Since this approach is focused on evaluating design sketches, we derive them manually using the lowest ordinal heuristic for this purpose as proposed by Wickens et al [16].…”

Section: Definition Of Expectancy Coefficientsmentioning

confidence: 99%

“…With the HEE predictions can be performed by a cognitive human operator simulation in a very early design phase where only HMI design images or sketches of future interfaces are available. The HEE uses the Adaptive Information Expectancy model (AIE model) [5], which is based on the SEEV model [6] and is a dynamic simulation model of attention distribution.…”

Section: Introductionmentioning

confidence: 99%

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Revealing Differences in Designers’ and Users’ Perspectives

Feuerstack

Wortelen

2015

Human-Computer Interaction – INTERACT 2015

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Section: Tool-supported Attention Prediction With the Heementioning

confidence: 86%

Section: Definition Of Expectancy Coefficientsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Revealing Differences in Designers’ and Users’ Perspectives

Feuerstack

Wortelen

2015

Human-Computer Interaction – INTERACT 2015

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“…The visual demand of driving can be defined as the frequency at which the driver has to update the focus of visual attention in order to decrease uncertainty of the task-critical event states (e.g. speed, lane position) in the immediate field of view to a preferred level, after [14] and [20]. In the visual occlusion technique the visual field of the driver (driving scene) is intermittently occluded by the means of visor, goggles or blanked screens on system-or driver-paced intervals in order to get an idea of the visual demands of driving.…”

Section: Introductionmentioning

confidence: 99%

Critical Analysis on the NHTSA Acceptance Criteria for In-Vehicle Electronic Devices

Kujala

Lasch²,

Mäkelä

2014

Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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We tested a commercial in-car navigation system prototype against the NHTSA criteria for acceptance testing of in-vehicle electronic devices, in order to see what types of in-car tasks fail the acceptance test and why. In addition, we studied the visual demands of the driving scenario recommended by NHTSA for task acceptance testing. In the light of the results, NHTSA guidelines and acceptance criteria need to be further developed. In particular visual demands of the driving scenario and for different simulators need to be standardized in order to enable fair testing and comparable test results. We suggest the visual occlusion method for finding a driving scenario that corresponds better with real-life driving in visual demands as well as for standardizing the visual demands of the scenario when applied to different driving simulators. Furthermore, the acceptance criteria need to be reevaluated. Especially the TEORT limit's applicability to a variety of test tasks needs to be validated and exceptions for certain task types considered. The utility of the average glance duration criterion should be reconsidered.

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“…Therefore, output was obtained with too simple statistical correlations that gave a limited robustness and reproducibility to the entire model. It may be mentioned in this regard the study of Harbluk et al (2007) that developed Downloaded by ["Queen's University Libraries, Kingston"] at 02:47 04 February 2015 a cognitive model of distraction, evaluating the impact of this feature on the visual behavior and braking activity, or, yet, Wortelen et al (2013) that proposed a cognitive model of attention control capable of making predictions with very different scenarios.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Drivers’ Visual Strategy Using an Analytical Model

Bosurgi

D’Andrea

Pellegrino

2014

Journal of Transportation Safety & Security

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The proposed research addresses the problem of predicting driver's visual behavior. Notably, this issue is of great importance for road safety because it provides information about the true perception of the road environment. This need is particularly felt when the real situation is so complex that it cannot be represented by simple analytical audits contained in the road international standard. Therefore, with the help of an instrumented vehicle, the authors conducted some trials to identify relevant data useful to recognize the road environment as perceived by drivers. Based on this data, the authors prepared a linear model that, properly calibrated, would enable them to predict the drivers' visual strategy. The results were encouraging, because a model so simple in structure as well as in the number of variables involved could control a very complex phenomenon, such as that of visual perception. The proposed methodology can be applied as a further control in the design of new roads and in the maintenance of existing ones and, in particular, in the analysis of special scenarios (e.g., unusual conditions of weather or traffic) that are not represented by ordinary audits contained in the road standards.

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Dynamic simulation and prediction of drivers’ attention distribution

Cited by 35 publications

References 8 publications

Revealing Differences in Designers’ and Users’ Perspectives

Revealing Differences in Designers’ and Users’ Perspectives

Critical Analysis on the NHTSA Acceptance Criteria for In-Vehicle Electronic Devices

Prediction of Drivers’ Visual Strategy Using an Analytical Model

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