Introducing systems that execute tasks formerly executed by human operators and leaving the operator with a monitoring role often leads to unintended results: Operators are not able to perform as expected. MINIMA is working on a solution for this. Brain activity will be measured to infer the operator’s vigilance and the task will be adapted to the needs of the operator in real-time.
The MINIMA (Mitigating Negative Impacts of Monitoring high levels of Automation) Project is an SESAR Exploratory Research project founded by the European Union. DLR is coordinator of the project and is working together with the University of Bologna, ONERA, and the third party Brain Signs. The two-year project started in May 2016.
MINIMA will develop solutions to mitigate the negative impacts that higher levels of automation in ATC have on the air traffic controller. The aim of many ongoing projects is to increase the automation in ATC in order to increase the performance of the system. As the absence of automation errors can often not be guaranteed, a human operator is required to monitor the automation and to intervene in the rare cases of automation errors. This monitoring role of human operators results in negative effects like lack of attention, loss of situation awareness and – in the long term – skill degradation. MINIMA will first apply neurophysiological measurements like EEG to measure vigilance and attention level and, second, develop a task environment which will adapt to the measured state of the Air Traffic Controller in order to prevent the problems mentioned above. An experimental evaluation of the concept is planned for the end of 2017, to take place at University of Bologna premises.
The project aims at developing a human-centred concept for the application of higher levels of automation to complex systems while mitigating the negative effects of monitoring tasks on air traffic controllers. This will be a key enabler for the safe and efficient application of highly automated systems in ATC.
This project has received funding from the SESAR Joint Undertaking under grant agreement No 699282 under European Union’s Horizon 2020 research and innovation programme.