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Personalized Driver Workload Estimation in Real-World Driving
Technical Paper
2018-01-0511
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Drivers often engage in secondary in-vehicle activity that is not related to vehicle control. This may be functional and/or to relieve monotony. Regardless, drivers believe they can safely do so when their perceived workload is low. In this paper, we describe a data acquisition system and machine learning based algorithms to determine perceived workload. Data collected were from on-road driving in light and heavy traffic, and individual physiological measures were recorded while the driver also performed in-vehicle tasks. Initial results show how the workload function can be personalized to an individual, and what implications this may have for vehicle design.
Authors
Citation
Murphey, Y., Xie, Y., and Kochhar, D., "Personalized Driver Workload Estimation in Real-World Driving," SAE Technical Paper 2018-01-0511, 2018, https://doi.org/10.4271/2018-01-0511.Data Sets - Support Documents
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