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Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification.

Shaibal Barua1, Mobyen Uddin Ahmed1, Shahina Begum1

  • 1School of Innovation, Design and Engineering, Mälardalen University, Högskoleplan 1, 72220 Västerås, Sweden.

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|August 13, 2020
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Summary
This summary is machine-generated.

Cognitive load from secondary tasks impairs driving. This study used machine learning with physiological and vehicle data to classify driver cognitive load, achieving better results with combined data sources.

Keywords:
cognitive loadmachine learningmulticomponent signalsmultimodal data analytics

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Area of Science:

  • Traffic Safety Research
  • Cognitive Psychology
  • Machine Learning

Background:

  • Cognitive load from secondary tasks is a known factor that can impair primary task performance, such as driving.
  • Identifying driver cognitive load is crucial for enhancing traffic safety.
  • Existing methods using physiological measures for cognitive load identification face challenges with confounding factors and noise.

Purpose of the Study:

  • To investigate the impact of cognitive load on driving performance using a simulated environment.
  • To develop and evaluate machine learning models for classifying driver cognitive load.
  • To assess the effectiveness of a multimodal approach combining physiological and vehicular data for cognitive load classification.

Main Methods:

  • A simulated driving environment was used, with drivers performing a version of the n-back task as a secondary cognitive load task.
  • Multivariate data analytics employing machine learning algorithms, including k-nearest neighbour (k-NN), support vector machine (SVM), and random forest (RF), were utilized.
  • A multimodal approach integrating physiological measures and vehicular features was adopted to improve classification accuracy and mitigate noise.

Main Results:

  • Feature selection algorithms (SFFS and RF) identified an optimal subset of 42 features from an initial 323.
  • The random forest (RF) algorithm demonstrated superior performance in classifying cognitive load states, achieving an F1-score of 0.75 for multiclass and 0.80 for binary classification.
  • Classifiers utilizing multicomponent features (physiological and vehicular) outperformed those using single-source features.

Conclusions:

  • The study confirms that cognitive load significantly impacts driving.
  • A multimodal approach integrating physiological and vehicular data, analyzed with machine learning, is effective for accurately classifying driver cognitive load.
  • The findings suggest that combining diverse data sources enhances the robustness and accuracy of cognitive load detection systems in driving contexts.