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

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...

Heart Rate Analysis for Human Factors: Development and Validation of an Open Source Toolkit for Noisy Naturalistic Heart Rate Data

Heart rate data are collected often in human factors studies. Advances in open hardware platforms and offtheshelf photoplethysmogram (PPG) sensors allow the nonintrusive collection of heart rate data at very low cost. However, the signal is not trivial to analyse, since the morphology of PPG waveforms differs from electrocardiogram (ECG)...

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

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...

The Use of Persuasive In-Car Technology to Persuade Drivers at the Tactical Level

The use of in-car technology has become more prevalent, both as driver assistance systems as well as connectivity or entertainment systems. Driver assistance systems can be built-in, after-market or run on a smartphone. The challenge however, is to increase drivers’ compliance with these systems. Stimulating the driver to adopt certain...