Project description

The impairing effect from sleepiness is a major contributor to road crashes. Current estimates suggest that this effect accounts for 20% of all fatal and severe crashes (Connor et al. 2002, Kecklund et al. 2011). In the same line, the National Safety Council of USA estimates 25% of all crashes in 2008 involved talking on cell phones (National Safety Council 2010). Based on epidemiological research, the European Commission stated that about 5 to 25% of car accidents have been attributed to driver distraction but when focusing truck drivers, a much higher estimate of 70% has been found (European Commission 2015).

However, because no objective device such as breath alcohol content level as with drink driving is used the exact incident levels are suggested to be greater than current estimates (Cercarelli and Haworth 2002). On the other hand, the observed differences in estimates between studies may be connected with differences in operational definitions, in research methods and driver populations (European Commission 2015).

The present project aims to explore the relationship between driver’s (in)attention with driver’s and journey’s patterns toward the development of a detection and warning system fitted to the driver and his/her journey based on a learning process.

The project will start upon the already achieved patterns under SIESTA project and using a tool set to monitor the visual, physical, and cognitive as well as health and personnel aspects of the driver, a relationship will be identified with the physiological signals and driver’s aspects such as stress, fatigue and risk perception.

To ensure the participants safety, the research will be conducted in a virtual environment using the driving simulator DriS located at FEUP-CITTA eventually preceded by virtual tests to better set up the simulator scenarios and driving tasks. On the other hand, tools such as eye tracking, face monitoring, skin conductor, ambulatory electroencephalogram and electrocardiogram will be used to a full driver tracking.

The project outcomes will allow the assessment of several and existed technology devices and guide future developments based on a data-driven understanding in the view of a personalized technology which takes into account the driver characteristics and state of mind. Overall, the project will contribute to the body of knowledge and competence of the National Science and Technology System as well as to the economic value of the technology developed in Portugal.

Research team

University of Porto

• António José Fidalgo do Couto
• António Manuel Cabral Vieira Lobo
• José Pedro Maia Pimentel Tavares
• Rosaldo José Fernandes Rossetti
• Sara Maria Pinho Ferreira (PI)
• Sónia Cristina dos Reis Soares

Financial support

• FCT - Fundação para a Ciência e Tecnologia - Programa MIT-Portugal

Stage of progress

• Concluded in 2020