Marco Molz

German Research Centre for Artificial Intelligence

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Alexandra Alles

German Research Centre for Artificial Intelligence

Elena Meiser

German Research Centre for Artificial Intelligence

Guillermo Reyes

German Research Centre for Artificial Intelligence

Amr Gomaa

Saarland University

Lydia Helene Rupp

Friedrich-Alexander-Universität Erlangen-Nürnberg

Samuel Selter

German Research Centre for Artificial Intelligence

Guillermo Reyes

Hospital Universitario de La Princesa

Amr Gomaa

Suez Canal University

Cooperative Institutions

German Research Centre for Artificial Intelligence

Saarland University

Universidad Internacional del Ecuador

New America

Friedrich-Alexander-Universität Erlangen-Nürnberg

Hospital Universitario de La Princesa

Universidad Autónoma de Madrid

Centro de Investigación en Red en Enfermedades Cardiovasculares

Centro de Investigación Biomédica en Red

Hospital Universitario HM Madrid

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What's on your mind? A Mental and Perceptual Load Estimation Framework towards Adaptive In-vehicle Interaction while Driving

arXiv (Cornell University) (2022)

Amr Gomaa Alexandra Alles Elena Meiser Lydia Helene Rupp Marco Molz

Several researchers have focused on studying driver cognitive behavior and mental load for in-vehicle interaction while driving. Adaptive interfaces that vary with mental and perceptual load levels could help in reducing accidents and enhancing the driver experience. In this paper, we analyze the effects of mental workload and perceptual load on psychophysiological dimensions and provide a machine learning-based framework for mental and perceptual load estimation in a dual task scenario for in-vehicle interaction (https://github.com/amrgomaaelhady/MWL-PL-estimator). We use off-the-shelf non-intrusive sensors that can be easily integrated into the vehicle's system. Our statistical analysis shows that while mental workload influences some psychophysiological dimensions, perceptual load shows little effect. Furthermore, we classify the mental and perceptual load levels through the fusion of these measurements, moving towards a real-time adaptive in-vehicle interface that is personalized to user behavior and driving conditions. We report up to 89% mental workload classification accuracy and provide a real-time minimally-intrusive solution.

Cognitive Load

Interface (matter)

10.48550/arxiv.2208.05564

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What’s on your mind? A Mental and Perceptual Load Estimation Framework towards Adaptive In-vehicle Interaction while Driving

Amr Gomaa Alexandra Alles Elena Meiser Lydia Helene Rupp Marco Molz

10.1145/3543174.3546840

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Citations (7)

In-Vehicle Interface Adaptation to Environment-Induced Cognitive Workload

Elena Meiser Alexandra Alles Samuel Selter Marco Molz Amr Gomaa

Many car accidents are caused by human distractions, including cognitive distractions. In-vehicle human-machine interfaces (HMIs) have evolved throughout the years, providing more and more functions. Interaction with the HMIs can, however, also lead to further distractions and, as a consequence, accidents. To tackle this problem, we propose using adaptive HMIs that change according to the mental workload of the driver. In this work, we present the current status as well as preliminary results of a user study using naturalistic secondary tasks while driving (i.e., the primary task) that attempt to understand the effects of one such interface.

Interface (matter)

Human multitasking

Task Analysis

10.1145/3544999.3552533

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Citations (3)