FitDrive: Monitoring devices for overall FITness of DRIVErs
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Driving includes multiple complex activities; performance depends on the utilization of both physiological and cognitive capabilities. Short-term factors based on personal lifestyles such as alcohol and drug use are widely known to affect fitness to drive. However, there are long-term factors such as physical or cognitive impairment that account for 6 % of all fatal crashes, while fatigue is a factor in 10-20% of road accidents. Professional drivers in particular are at risk of being involved in a fatigue-related crash. A primary goal of the law enforcement authorities is, therefore, to ensure practical tools for specific and reliable controls “on the road”: in other words to couple road controls with specific tools for evaluating driver’s fitness and thus its performance. FITDrive will rely on a multidisciplinary and ambitious consortium of relevant entities able to cover all the required research areas in a well-balanced way, based on their expertise, prior collaborations (EU H2020 SIMUSAFE project, constituting the base for the research to be developed in FITDrive), state-of-the-art technical background and relevant collaborations to provide the desired impact. FITDrive aims to decrease traffic accidents in 6% by early identifying drivers affected by impairing causes.
Svenskt forskningsprojekt: AI rapporterar farliga yrkesförare till polisen
Artificiell intelligens förbättrar trafiksäkerheten
Artificial Intelligence improves road safety
Lyssna på P4 Västmanland med Mobyen Uddin Ahmed
| First Name | Last Name | Title |
|---|---|---|
| Mobyen Uddin | Ahmed | Professor |
| Shahina | Begum | Professor |
| Shaibal | Barua | Senior Lecturer |
| Arnab | Barua | Doctoral student |
In-Depth Analysis of Diverse Driver Behaviors using Hybrid Multimodal Machine Learning (Mar 2025) Mera Abudiab , Francisco Javier Pérez Núñez , Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Arnab Barua International Conference on Computer and Information Technology (ICCIT)
Role of Multi-modal Machine Learning, Explainable AI and Human-AI Teaming in Trusted Intelligent Systems for Remote Digital Towers (Jan 2025) Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Waleed Reafee Sbu Jmoona, Ricky Stanley D Cruze , Alexandre Veyrie , Christophe Hurter 7th Artificial Intelligence and Cloud Computing Conference (AICCC2024)
Second-Order Learning with Grounding Alignment: A Multimodal Reasoning Approach to Handle Unlabelled Data (Feb 2024) Arnab Barua, Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Andrea Giorgi 16th International Conference Agents and Artificial Intelligence (ICAART2024)
Neurophysiological mental fatigue assessment for developing usercentred Artificial Intelligence as a solution for autonomous driving (Nov 2023) Andrea Giorgi , Vincenzo Ronca , Alessia Vozzi , Pietro Aricò , Gianluca Borghini , Rossella Capotorto , Luca Tamborra , Ilaria Simonetti , Simone Sportiello , Marco Petrelli , Carlo Polidori , Rodrigo Varga , Marteyn van Gasteren , Arnab Barua, Mobyen Uddin Ahmed, Fabio Babiloni , Gianluca Di Flumeri Frontiers in Neurorobotics (Front Neurorobot)
Multi-scale Data Fusion and Machine Learning for Vehicle Manoeuvre Classification (Nov 2023) Arnab Barua, Mobyen Uddin Ahmed, Shahina Begum IEEE International Conference on System Engineering and Technology (ICSET2023)
A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions (Mar 2023) Arnab Barua, Mobyen Uddin Ahmed, Shahina Begum Journal of IEEE Access (IEEE-Access)
