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ADABase: A Multimodal Dataset for Cognitive Load Estimation.

Maximilian P Oppelt^1,2, Andreas Foltyn³, Jessica Deuschel³

¹Department Digital Health Systems, Fraunhofer IIS, Fraunhofer Institute for Integrated Circuits IIS, 91058 Erlangen, Germany.

Sensors (Basel, Switzerland)

|January 8, 2023

Summary

This study introduces ADABase, a new dataset for assessing driver cognitive load in autonomous vehicles. Machine learning models effectively identify cognitive load using physiological and behavioral data, enhancing traffic safety.

Keywords:

affective computing autonomous driving cognitive load machine learning multimodal dataset

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Area of Science:

Human-computer interaction
Automotive engineering
Cognitive science

Background:

Driver monitoring systems are crucial for autonomous vehicle safety.
Estimating driver cognitive load is key to proactive safety interventions.
Current methods for assessing cognitive load in driving contexts are limited.

Purpose of the Study:

To develop and validate a comprehensive database (ADABase) for assessing driver cognitive load.
To investigate the utility of physiological, behavioral, and performance metrics for cognitive load detection.
To train and evaluate machine learning models for real-time cognitive load estimation.

Main Methods:

Induced cognitive load in 51 participants using n-back and k-drive tests.
Collected multimodal data: physiological (ECG, EDA, EMG, PPG, respiration, skin temperature), eye-tracking, facial action units, reaction time, and subjective feedback.
Developed ADABase (Autonomous Driving Cognitive Load Assessment Database).

Main Results:

Significant changes detected across multiple physiological and behavioral modalities under varying cognitive loads.
Machine learning models trained on single and multimodal data successfully distinguished cognitive load levels.
Feature importance analysis provided insights into key indicators of cognitive load.

Conclusions:

ADABase provides a valuable resource for cognitive load research in autonomous driving.
Multimodal data fusion enhances the accuracy of cognitive load detection.
The developed methods show promise for improving driver-state estimation and traffic safety.