Multi-Level Driver Workload Prediction Using Machine Learning and Off-The-Shelf Sensors

Multi-Level Driver Workload Prediction Using Machine Learning and Off-The-Shelf Sensors

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)

Transport and Planning

2018

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.

Driver workload
machine learning
workload prediction
random forest
support vector machine
embedded workload prediction

http://resolver.tudelft.nl/uuid:cb38fffc-2f18-47d2-8d0a-3df8c318da78

Transportation Research Board (TRB)

Transportation Research Board Conference Proceedings 2018

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

Institutional Repository

conference paper

© 2018 P. van Gent, T. Melman, H. Farah, Nicole Van Nes, B. van Arem