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. Driving a car is a complex and dynamic task and there is a wide range of conditions that temporarily affect the ability to drive safely like consuming substances or fatigue. Professional drivers are particularly affected by fatigue. The main effect of fatigue is a progressive withdrawal of attention from the road and traffic demands leading to impaired driving performance. The particular practice of professional drivers include working long hours, prolonged night work, working irregular hours, little or poor sleep, and early starting times which in many cases lead to fatigue. Fatigue causes reduced alertness, longer reaction times, memory problems, poorer psychometric coordination, and less efficient information processing. The results of different surveys world-wide show that over 50% of long-haul drivers have at some time almost fallen asleep at the wheel.
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.