Print Email Facebook Twitter Multi-Level Driver Workload Prediction Using Machine Learning and Off-The-Shelf Sensors Title Multi-Level Driver Workload Prediction Using Machine Learning and Off-The-Shelf Sensors Author van Gent, P. (TU Delft Transport and Planning) Melman, T. (Student TU Delft) Farah, H. (TU Delft Transport and Planning) Nes, Nicole Van (Stichting Wetenschappelijk Onderzoek Verkeersveiligheid (SWOV)) van Arem, B. (TU Delft Transport and Planning) Department Transport and Planning Date 2018 Abstract The present 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-class basis, rather than a binary high/low distinction as often found in litearature. 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 on low-power embedded systems.Two simulator studies were performed, one inducing workload using realistic driving conditions, and one inducing workload with a relatively demanding lane-keeping task. Individual and group-based machine learning models were trained on both datasets and evaluated. For the group-based models the generalising capability, that is the performance when predicting data from previously unseen individuals, was also assessed.Results show that multi-class workload prediction on the individual and group level works well, achieving high correct rates and accuracy scores. Generalising between individuals proved difficult using realistic driving conditions, but worked very well in the high demanding lane-keeping task. Reasons for this discrepancy are discussed as well as future research directions. Subject Driver workloadmachine learningworkload predictionrandom forestsupport vector machineembedded workload prediction To reference this document use: http://resolver.tudelft.nl/uuid:cb38fffc-2f18-47d2-8d0a-3df8c318da78 Publisher Transportation Research Board (TRB) Source Transportation Research Board Conference Proceedings 2018 Event TRB 2018: 97th Annual Meeting of the Transportation Research Board, 2018-01-07 → 2018-01-11, Walter E. Washington Convention Center, Washington D.C., United States Part of collection Institutional Repository Document type conference paper Rights © 2018 P. van Gent, T. Melman, H. Farah, Nicole Van Nes, B. van Arem Files PDF 18_02628.pdf 1.4 MB Close viewer /islandora/object/uuid:cb38fffc-2f18-47d2-8d0a-3df8c318da78/datastream/OBJ/view