Estimating driver workload with systematically varying traffic complexity using machine learning: Experimental design

Udara E. Manawadu, Takahiro Kawano, Shingo Murata, Mitsuhiro Kamezaki, Shigeki Sugano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationIntelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018
Subtitle of host publicationIntegrating People and Intelligent Systems
PublisherSpringer-Verlag
Pages106-111
Number of pages6
ISBN (Print)9783319738871
DOIs
Publication statusPublished - 2018 Jan 1
Event1st International Conference on Intelligent Human Systems Integration: Integrating People and Intelligent Systems, IHSI 2018 - Dubai, United Arab Emirates
Duration: 2018 Jan 72018 Jan 9

Publication series

NameAdvances in Intelligent Systems and Computing
Volume722
ISSN (Print)2194-5357

Other

Other1st International Conference on Intelligent Human Systems Integration: Integrating People and Intelligent Systems, IHSI 2018
CountryUnited Arab Emirates
CityDubai
Period18/1/718/1/9

Fingerprint

Design of experiments
Learning systems
Simulators
Sensors
Electrocardiography
Skin
Experiments
Testing

Keywords

  • Driver workload
  • Driving simulation
  • Intelligent vehicles

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Manawadu, U. E., Kawano, T., Murata, S., Kamezaki, M., & Sugano, S. (2018). Estimating driver workload with systematically varying traffic complexity using machine learning: Experimental design. In Intelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018: Integrating People and Intelligent Systems (pp. 106-111). (Advances in Intelligent Systems and Computing; Vol. 722). Springer-Verlag. https://doi.org/10.1007/978-3-319-73888-8_18

Estimating driver workload with systematically varying traffic complexity using machine learning : Experimental design. / Manawadu, Udara E.; Kawano, Takahiro; Murata, Shingo; Kamezaki, Mitsuhiro; Sugano, Shigeki.

Intelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018: Integrating People and Intelligent Systems. Springer-Verlag, 2018. p. 106-111 (Advances in Intelligent Systems and Computing; Vol. 722).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Manawadu, UE, Kawano, T, Murata, S, Kamezaki, M & Sugano, S 2018, Estimating driver workload with systematically varying traffic complexity using machine learning: Experimental design. in Intelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018: Integrating People and Intelligent Systems. Advances in Intelligent Systems and Computing, vol. 722, Springer-Verlag, pp. 106-111, 1st International Conference on Intelligent Human Systems Integration: Integrating People and Intelligent Systems, IHSI 2018, Dubai, United Arab Emirates, 18/1/7. https://doi.org/10.1007/978-3-319-73888-8_18
Manawadu UE, Kawano T, Murata S, Kamezaki M, Sugano S. Estimating driver workload with systematically varying traffic complexity using machine learning: Experimental design. In Intelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018: Integrating People and Intelligent Systems. Springer-Verlag. 2018. p. 106-111. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-319-73888-8_18
Manawadu, Udara E. ; Kawano, Takahiro ; Murata, Shingo ; Kamezaki, Mitsuhiro ; Sugano, Shigeki. / Estimating driver workload with systematically varying traffic complexity using machine learning : Experimental design. Intelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018: Integrating People and Intelligent Systems. Springer-Verlag, 2018. pp. 106-111 (Advances in Intelligent Systems and Computing).
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