Monitoring devices for overall FITness of DRIVErs

The aim of determining fitness to drive is to achieve a balance between minimising any driving-related road safety risks for the individual and the community and maintaining the driver’s lifestyle and employment-related mobility independence.

Project manager at MDU

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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 by 6% by early identifying drivers affected by impairing causes.

Project objectives

The project will design, implement and test a new tool, for the monitoring and evaluation of driving performance, cognitive load, physical fatigue and reaction time. The system will create neurophysiological models able to detect the onset of abnormal drivers’ fitness based on data obtained from IoT devices during working activities and while driving, on board intelligence and smart tachographs.

Artificial Intelligent models will associate different kinds of anomalous behaviour to its most probable cause: drugs, medicines, alcohol, fatigue, etc and a cloud-based system will communicate driver, police patrols, infrastructure the necessary information to improve road safety.

Drugs and alcohol have also the potential to adversely affect driving skills; the project will also develop screening methods to detect new drugs and to reduce the time needed to perform the tests.