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Classifying driver workload using physiological and driving performance data: two field studies

Published: 26 April 2014 Publication History

Abstract

Understanding the driver's cognitive load is important for evaluating in-vehicle user interfaces. This paper describes experiments to assess machine learning classification algorithms on their ability to automatically identify elevated cognitive workload levels in drivers, leading towards the development of robust tools for automobile user interface evaluation. We look at using both driver performance as well as physiological data. These measures can be collected in real-time and do not interfere with the primary task of driving the vehicle. We report classification accuracies of up to 90% for detecting elevated levels of cognitive load, and show that the inclusion of physiological data leads to higher classification accuracy than vehicle sensor data evaluated alone. Finally, we show results suggesting that models can be built to classify cognitive load across individuals, instead of building individual models for each per-son. By collecting data from drivers in two large field studies on the highway (20 drivers and 99 drivers), this work extends prior work and demonstrates feasibility and potential of such measures for HCI research in vehicles.

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    cover image ACM Conferences
    CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
    April 2014
    4206 pages
    ISBN:9781450324731
    DOI:10.1145/2556288
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    Published: 26 April 2014

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    Author Tags

    1. cognitive workload
    2. driving
    3. heart rate
    4. machine learning
    5. physiological computing
    6. skin conductance

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    April 26 - May 1, 2014
    Ontario, Toronto, Canada

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    Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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    • (2025)Cognitive workload quantification for air traffic controllers: An ensemble semi-supervised learning approachAdvanced Engineering Informatics10.1016/j.aei.2024.10306564(103065)Online publication date: Mar-2025
    • (2024)Classification of Driver Cognitive Load in Conditionally Automated Driving: Utilizing Electrocardiogram-Based Spectrogram with Lightweight Neural NetworkTransportation Research Record: Journal of the Transportation Research Board10.1177/036119812412527972678:12(1560-1573)Online publication date: 10-Jun-2024
    • (2024)Cognitive Workload Estimation in Conditionally Automated Vehicles Using Transformer Networks Based on Physiological SignalsTransportation Research Record: Journal of the Transportation Research Board10.1177/036119812412500232678:12(1183-1196)Online publication date: 10-Jun-2024
    • (2024)Driver Profiling and Bayesian Workload Estimation Using Naturalistic Peripheral Detection Study DataIEEE Transactions on Intelligent Vehicles10.1109/TIV.2023.33134199:1(3047-3060)Online publication date: Jan-2024
    • (2024)Real-Time Driver Cognitive Workload Recognition: Attention-Enabled Learning With Multimodal Information FusionIEEE Transactions on Industrial Electronics10.1109/TIE.2023.328818271:5(4999-5009)Online publication date: May-2024
    • (2024)User-Aware Multilevel Cognitive Workload Estimation From Multimodal Physiological SignalsIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2023.334213916:4(1212-1222)Online publication date: Aug-2024
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