Smart Mobility: Driver State Estimation and Advanced Driver-Vehicle Interfaces

Noldus, L.P.J.J.; Spink, Andrew; Bollen, Ramon; Heffelaar, Tobias

doi:10.1007/978-981-10-3099-4_2

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Besides, eye-movement interaction was an important interaction mode of autonomous driving (24), which was of great significance to the driving scenes with a large amount of information such as instrument reading. Eye-movement data, including gaze and blinks, could be combined with physiology signals (such as heart rate and skin conductance), facial expression and behaviors to evaluate the state of intelligent vehicle drivers effectively (25). Yet, some limitations existed in the collection and analysis of eye tracking data.…”

Section: Literature Reviewmentioning

confidence: 99%

Dashboard Layout Effects on Drivers' Searching Performance and Heart Rate: Experimental Investigation and Prediction

Yang

Wang

Jia

2022

Front. Public Health

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Carsharing scale has been increasing rapidly with sharing economy. However, many users are reluctant to rent cars any longer due to the low-quality of interactive experience and usability, especially in terms of the dashboard design. This challenge should be urgently addressed in order to maintain the sustainable development of car-sharing industry and its environmental benefits. This study aims to investigate the relationship between users' driving activities (e.g., searching time, reading time, eye movement, heart rate) and dashboard layout. This study was conducted based on the experimental investigation among 58 respondents who were required to complete driving tasks in four types of cars with different dashboard layouts. Afterwards, a prediction model was developed to predict users heart rate (HR) based on the long short-term memory model, and logistic models were used to examine the relationship between the occurrence probability of minimum HR and dashboard reading. The results showed that the system usability of a dashboard was related to the drivers' eye movement characteristics including fixation duration, fixation times and pupil diameter. Most indicators had significant effects (p < 0.05) on the system usability score of corresponding dashboard. The long short-term memory model network (RMSE = 1.105, MAE = 0.009) was capable of predicting heart rate (HR) that happened in the process of instrument reading, which presented a periodic pattern rather than a continuous increase or decrease. It reflected that the network could better fit the non-linear and time-sequential laws of HR data. Furthermore, the probability of the lowest heart rate occurrence during the interaction with four dashboards was influenced by the average searching time, reading time and reading accuracy that were related to a specific layout. Overall, this study provided a theoretical reference for uncovering users' adaptive behaviors with the central control screen and for the optimal choice of a suitable dashboard layout in interface design.

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Section: Literature Reviewmentioning

confidence: 99%

Dashboard Layout Effects on Drivers' Searching Performance and Heart Rate: Experimental Investigation and Prediction

Yang

Wang

Jia

2022

Front. Public Health

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“…En parallèle, depuis plusieurs années, en neurophysiologie et psychophysiologie, il existe des méthodes de vidéo télémétrique et des logiciels pour coupler les données psychophysiologiques et vidéo afin de faciliter l'analyse de l'information. Toutefois, les études utilisant ces méthodes mobilisent les données vidéo afin de savoir où analyser des données psychophysiologiques en fonction du comportement d'un participant (Noldus et Bollen, 2017), et non l'inverse.…”

Section: Introductionunclassified

Exploration automatisée de données vidéo à partir d’un fil de données psychophysiologiques : une preuve de concept en sciences de l’éducation

Lapierre¹

2018

Neuroed.

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RÉSUMÉBien que la collecte de données vidéo soit de plus en plus présente en recherche, notamment due à une accessibilité et une facilité d'utilisation accrue des technologies permettant l'enregistrement vidéo, les analyses liées à de tels outils de collecte demeurent toutefois un réel défi. De nombreux logiciels permettent une analyse accélérée grâce à, notamment, la reconnaissance par image. Cependant, ces techniques ne s'appliquent que peu en éducation, puisque l'apprentissage n'est pas nécessairement lié au mouvement. Ainsi, cet article présente une méthodologie novatrice afin de générer automatiquement des marqueurs temporels sur des données vidéo à partir de données psychophysiologiques. Les apports et limites de cette méthode sont discutés en lien avec la recherche en éducation.

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