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Article: Multi-Level Driver Workload Prediction using Machine Learning and Off-the-Shelf Sensors

SWOV researcher van Nes published the article 'Multi-Level Driver Workload Prediction using Machine Learning and Off-the-Shelf Sensors' in the Journal of the Transportation Research Board.

The study aims to add to the literature on driver workload prediction using machine learning methods. The main aim is to develop workload prediction on a multi-level basis, rather than a binary high/low distinction as often found in literature. The presented approach relies on measures that can be obtained unobtrusively in the driving environment with off-the-shelf sensors, and on machine learning methods that can be implemented in low-power embedded systems.

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