Towards Real-Time, Nonintrusive Estimation of Driver Workload in a Simulated Environment

van Gent, P.; Farah, H.; Nes, Nicole Van; van Arem, B.

Towards Real-Time, Nonintrusive Estimation of Driver Workload in a Simulated Environment

Title

Towards Real-Time, Nonintrusive Estimation of Driver Workload in a Simulated Environment

Author

van Gent, P. (TU Delft Transport and Planning)
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

2017

Abstract

The aim of this research is to work towards building an open-source, platform-independent algorithm capable of predicting driver workload in real-time and in a non-intrusive way. To work towards a system that can also be implemented in on-road settings, we aimed at using off-the-shelf, non-intrusive sensors that could be implemented into the steering wheel and dashboard of current and future generations of cars, making them non-intrusive.
In order to build the initial predictive model, a driving simulator experiment was performed. Nineteen participants were required to drive a virtual replication of the Dutch A67 C-ITS corridor between Eindhoven and Venlo. We attempted to induce driver workload by varying weather, traffic composition, traffic density and by asking participants to perform various manoeuvres such as lane changing, merging and exiting. We measured heart rate, skin response, blink and performance measures.
Results show that within individuals and within the experimental group, workload was predictable with a high correct rate in both individual models as well as group models. We also evaluated how well the models would generalise when used outside of the experimental setting. Preliminary results for this generalisation are poor. We discuss possible reasons for this and next steps we are planning to take to increase this performance.

Subject

driver workload
Machine Learning
workload prediction
Driving simulator

To reference this document use:

http://resolver.tudelft.nl/uuid:dec12728-9beb-4934-9e48-332c5ad77611

Embargo date

2017-10-19

Source

Proceedings of the 6th International Conference on Road Safety & Simulation (RSS): 17-19 October 2017, The Hague, Netherlands

Event

RSS2017: Road Safety and Simulation International Conference 2017, 2017-10-17 → 2017-10-19, Grand Hotel Amrâth Kurhaus, The Hague, Netherlands

Part of collection

Institutional Repository