P. (Paul) van Gent

Profile

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.

Research

My research focuses on embedded driver workload prediction using deep learning, as well as interaction between car drivers and in-car systems.

Research themes:

  • 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.

My PhD project

Lane-Specific Driver Advice: driver persuasion and the role of driver workload

Paul van Gent

MSc. Paul van Gent

Postdoc


Personal website

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