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Estimating Driver Workload with Systematically Varying Traffic Complexity Using Machine Learning: Experimental Design

  • Udara E. ManawaduEmail author
  • Takahiro Kawano
  • Shingo Murata
  • Mitsuhiro Kamezaki
  • Shigeki Sugano
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 722)

Abstract

Traffic complexity is one of the factors affecting driver workload. In order to study the relationship between traffic complexity levels and workload, a designed experiment is required, especially to vary traffic flow parameters systematically in a simulated environment. This paper describes the experimental design of a simulator study for developing a computational model to estimate the behavior of driver workload based on traffic complexity. Driving simulators allow creating and testing different traffic scenarios and manipulating independent variables to improve the quality of data, as compared to real world experiments. Physiological responses such as heart rate, skin conductance, and pupil size have been found to be related to workload. By adapting a data-driven method, we integrated electrocardiography sensors, electro-dermal activity sensors, and eye-tracker to acquire driver physiological signals and gaze information. Preliminary results show a positive correlation between traffic complexity levels and corresponding physiological responses, performance, and subjective measures.

Keywords

Driving simulation Driver workload Intelligent vehicles 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Udara E. Manawadu
    • 1
    Email author
  • Takahiro Kawano
    • 1
  • Shingo Murata
    • 1
  • Mitsuhiro Kamezaki
    • 1
  • Shigeki Sugano
    • 1
  1. 1.Waseda UniversityTokyoJapan

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