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...

On Developing a Driver Identification Methodology Using In-Vehicle Data Recorders

Recently, cutting edge technologies to facilitate data collection have emerged on a large scale. One of the most prominent is the in-vehicle data recorder (IVDR). There are multiple ways to assign the IVDR's data to the different drivers who share the same vehicle. Irrespective of the level of sophistication, all of these technologies still...