Periodic Reporting for period 1 - FITDRIVE (Monitoring devices for overall FITness of Drivers)
Período documentado: 2021-09-01 hasta 2023-02-28
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 FitDrive project was conceived to identify and prevent driving stress states for professional drivers (and consequently driving fitness) with artificial intelligence and machine learning techniques able to build a "normal" driving profile of each driver after some thousands of KMs driven. Once the "normal" profile of a specific driver has been defined, the AI system is able to detect abnormal behaviour (outside the "normal" profile) and associate them with the most probable causes, such as fatigue, or other cognitive disorders. The behavioural function consists of a set of indicators representing the range of normal driving behaviour for each specific driver: such “normality” will be defined by the self-learning system during a period of ordinary driving. Deviations are determined by the comparison between daily driving results and the personal “normal” profile.
The FitDrive system will provide a continuous screening of the driver's psychophysical capabilities, alerting him or her to potential illness on the way: in fact, the abnormal variations detected by the Artificial Intelligence can be associated with early situations of sickness that are not yet apparent to the subject but are about to manifest themselves. The system will continuously learning ad adapting itself to the driver: this means that the more a subject drives, the more the system profiles him/her and the more is able to make precise detection of anomalies.
The FitDrive system will also allow roadside patrol officers to interrogate the vehicle wirelessly and thus to focus on those vehicles that have shown recent abnormal behaviour. Roadside inspections for commercial vehicles, and in particular heavy vehicles, involve an appropriate amount of space and a relatively long time, which can be considerably reduced thanks to the system that the FitDrive project is developing, making inspections more efficient (because vehicles with potential problems are detected immediately) and reducing the time that vehicles remain stationary (those without potential problems undergo a much faster inspection). A further reduction of the controls' time will be achieved through a new and faster drugs screening method.
The FitDrive project was conceived to identify and prevent driving stress states for professional drivers (and consequently driving fitness) with artificial intelligence and machine learning techniques able to build a "normal" driving profile of each driver after some thousands of KMs driven. Once the "normal" profile of a specific driver has been defined, the AI system is able to detect abnormal behaviour (outside the "normal" profile) and associate them with the most probable causes, such as fatigue, or other cognitive disorders. The behavioural function consists of a set of indicators representing the range of normal driving behaviour for each specific driver: such “normality” will be defined by the self-learning system during a period of ordinary driving. Deviations are determined by the comparison between daily driving results and the personal “normal” profile.
The FitDrive system will provide a continuous screening of the driver's psychophysical capabilities, alerting him or her to potential illness on the way: in fact, the abnormal variations detected by the Artificial Intelligence can be associated with early situations of sickness that are not yet apparent to the subject but are about to manifest themselves. The system will continuously learning ad adapting itself to the driver: this means that the more a subject drives, the more the system profiles him/her and the more is able to make precise detection of anomalies.
The FitDrive system will also allow roadside patrol officers to interrogate the vehicle wirelessly and thus to focus on those vehicles that have shown recent abnormal behaviour. Roadside inspections for commercial vehicles, and in particular heavy vehicles, involve an appropriate amount of space and a relatively long time, which can be considerably reduced thanks to the system that the FitDrive project is developing, making inspections more efficient (because vehicles with potential problems are detected immediately) and reducing the time that vehicles remain stationary (those without potential problems undergo a much faster inspection). A further reduction of the controls' time will be achieved through a new and faster drugs screening method.
FitDrive started with a study on factors that influence fatigue, based on literature and a diary that was filled in by professional drivers. Outcomes were used to define the functional requirements of the FitDrive system to be installed in vehicles, in turn used for technical requirements.
The first of four pilot cycles started in the first period: tests in a van- and truck simulator, respectively in Italy and Spain, designed to provoke fatigue in the participants. The tests in Italy finalised while preliminary data analyses have started. Preparations for the second cycle have started; these consist of identical exercises as in the simulator but performed on a test track in a vehicle.
The first steps towards Artificial Intelligence modelling initiated, making the obtained data usable for these technologies.
In the pilots, a set of devices are used for data collection. Some of these are existing, like an EEG (brain activity) and environmental sensor, other are developed or adapted specifically for FitDrive: a smart tachograph, an on-board vehicle data reader and an innovate drug screening device.
An initial exploitation strategy has been developed. Several dissemination activities have taken place, including a workshop at the Transport Research Arena TRA2022 in Lisbon, organised together with the PANACEA project.
The first of four pilot cycles started in the first period: tests in a van- and truck simulator, respectively in Italy and Spain, designed to provoke fatigue in the participants. The tests in Italy finalised while preliminary data analyses have started. Preparations for the second cycle have started; these consist of identical exercises as in the simulator but performed on a test track in a vehicle.
The first steps towards Artificial Intelligence modelling initiated, making the obtained data usable for these technologies.
In the pilots, a set of devices are used for data collection. Some of these are existing, like an EEG (brain activity) and environmental sensor, other are developed or adapted specifically for FitDrive: a smart tachograph, an on-board vehicle data reader and an innovate drug screening device.
An initial exploitation strategy has been developed. Several dissemination activities have taken place, including a workshop at the Transport Research Arena TRA2022 in Lisbon, organised together with the PANACEA project.
The diary study showed that especially long-distance drivers have irregular working habits and times, and are more prone to suffer from fatigue while working. Issues because of driving under influence of substances appear to be relatively few compared to impact from fatigue.
Unlike currently commercially available fatigue detection systems, the FitDrive system will detect the onset of fatigue at an early stage, e.g. before the driver
noticing it. Also, the detection focuses on personal parameters and values, specific for the driver; these differ from person to person. By using the system the detection will learn and keep on improving.
Unlike currently commercially available fatigue detection systems, the FitDrive system will detect the onset of fatigue at an early stage, e.g. before the driver
noticing it. Also, the detection focuses on personal parameters and values, specific for the driver; these differ from person to person. By using the system the detection will learn and keep on improving.