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

P4 Sörmland : Med Lucia Sivertsen, 23rd September, kl 10:03

[Show all publications]

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)