P. (Paul) van Gent
I studied Cognitive Psychology at Leiden University. Following this, I worked at the Dutch Road Safety Research Institute (SWOV), where I developed an open sensor platform for use in naturalistic studies. Following this, I started a PhD at TU Delft. Here my research focuses on embedded driver workload prediction, as well as interaction between car drivers and in-car systems.
My research focuses on embedded driver workload prediction using deep learning, as well as interaction between car drivers and in-car systems.
- workload prediction
- driver interfaces
- physiological monitoring
- embedded hardware for experimental use
- deep learning
- van Gent, P., Melman, T., Farah, H., van Nes, N., & van Arem, B. (2018). Multi-Class Driver Workload Prediction Using 783 Machine Learning and Off-The-Shelf Sensors. Accepted and to be presented at Transportation Research Board Conference 2018.
- Gent, P. Van, Farah, H., Nes, N. Van, & Arem, B. Van. (2017). Towards Real-Time, Nonintrusive Estimation of Driver Workload: A Simulator Study. In Road Safety and Simulation 2017 Conference Proceedings.
- Gent, P. Van, Farah, H., Nes, N. Van, & Arem, B. Van. (2017). The Use of Persuasive In-Car Technology to Persuade Drivers at the Tactical Level. Presented at the Road Safety and Simulation 2017 Conference.
- Gent, P. Van, Farah, H., Nes, N. Van, & Arem, B. Van. (2017). A Conceptual Model for Persuasive In-Vehicle Technology to Influence Tactical. Level Driver Behaviour. Submitted to Transportation Research Part F: Traffic Psychology and Behaviour.