The VDM project aims to provide tools for development of safety systems including a basis for continuously monitoring driver’s state and based on these, carry out suitable counter measurements to prevent/reduce accidents. It will develop state of the art knowledge in the area of physiological measuring and analysis, strengthen the competitiveness of the Swedish automotive industry and provide tools for development of active safety systems. The participants in the project have extensive experience of studying different driver states and human monitoring e.g. of research on mental load, sleepiness and advanced analysis methods. In the VDM project this will be brought together in a general research framework and a mental-load model, using physiological measures as indicators. The results of the VDM project will provide a basis for future research and development in the field of driver states.
Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification (Aug 2020) Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum Brain Sciences (Special Issue: Brain Plasticity, Cognitive Training and Mental States Assessment) (Brain Sci)
Drivers' Sleepiness Classification using Machine Learning with Physiological and Contextual data (Mar 2019) Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum First International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2019)
Automatic Driver Sleepiness Detection using EEG, EOG and Contextual Information (Jan 2019) Shaibal Barua, Mobyen Uddin Ahmed, Christer Ahlström , Shahina Begum Expert Systems with Applications (ESWA)
Automated EEG Artifact Handling with Application in Driver Monitoring (Nov 2017) Shaibal Barua, Mobyen Uddin Ahmed, Christer Ahlström , Shahina Begum, Peter Funk IEEE Journal of Biomedical and Health Informatics (J-BHI)
Distributed Multivariate Physiological Signal Analytics for Drivers’ Mental State Monitoring (Oct 2017) Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum 4th EAI International Conference on IoT Technologies for HealthCare (HealthyIoT'17)
|The Swedish National Road and Transport Research Institute||Industrial|