TY - GEN
T1 - Estimating driver workload with systematically varying traffic complexity using machine learning
T2 - 1st International Conference on Intelligent Human Systems Integration: Integrating People and Intelligent Systems, IHSI 2018
AU - Manawadu, Udara E.
AU - Kawano, Takahiro
AU - Murata, Shingo
AU - Kamezaki, Mitsuhiro
AU - Sugano, Shigeki
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Driver workload
KW - Driving simulation
KW - Intelligent vehicles
UR - http://www.scopus.com/inward/record.url?scp=85040239081&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040239081&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-73888-8_18
DO - 10.1007/978-3-319-73888-8_18
M3 - Conference contribution
AN - SCOPUS:85040239081
SN - 9783319738871
T3 - Advances in Intelligent Systems and Computing
SP - 106
EP - 111
BT - Intelligent Human Systems Integration - Proceedings of the 1st International Conference on Intelligent Human Systems Integration IHSI 2018
PB - Springer-Verlag
Y2 - 7 January 2018 through 9 January 2018
ER -