Abstract
Driving could result in driver’s overload if the demands of the tasks are beyond the attentional capacity of the driver, which is the main cause of poor driving performance and high car accident risks. As the use of automation is becoming increasingly common, it provides the potential to reduce such risks. However, when automation relieves the driver from continuous driving tasks, underload may occur. The study investigated driver’s mental workload in partially automated vehicles and conventional vehicles under different traffic density conditions. Eight participants “drove” a simulated vehicle on a 10 mile straight, two-way rural interstate highway in 4 scenarios (2 (traffic density: Low, High) × 2 (vehicle type: Partially automated vehicle, Conventional vehicle) in random order. Data was recorded using a STISIM driving simulator, a Tobbi pro glasses 2 eye tracking device, and a NIRSport system. Workload was evaluated from subjective method (NASA-TLX questionnaire) and objective physiological methods (eye pupil diameter and oxygenated hemoglobin). The findings indicate the importance of combining different approaches to evaluate workload in driving.
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This research was partially supported by the Center for Advances in Port Management (CAPM) at Lamar University. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the CAPM.
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Zhao, R., Liu, Y., Li, T., Li, Y. (2022). A Preliminary Evaluation of Driver’s Workload in Partially Automated Vehicles. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2022. Lecture Notes in Computer Science, vol 13335. Springer, Cham. https://doi.org/10.1007/978-3-031-04987-3_30
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