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AUTOPACE Report Summary

Project ID: 699238
Funded under: H2020-EU.

Periodic Reporting for period 1 - AUTOPACE (AUTOMATION PACE)

Reporting period: 2016-03-01 to 2016-08-31

Summary of the context and overall objectives of the project

AUTOPACE is a research project funded by the SESAR Joint Undertaking within the European Union’s Horizon 2020 research and innovation programme under grant agreement No 699238. It performs fundamental research on psychological modelling to predict how future automation would impact on air traffic controller (ATCo) performance and to identify competences and training to cope with the effects of automation on humans.
The air traffic management (ATM) system moves towards an increasingly high level of automation which will provide outstanding performance benefits. High automation shall bring higher capacity at high standards of efficiency and predictability whilst ensuring proper levels of safety.
Automation will unavoidably change the ATCo work environment and the human role will focus on more complex, high-value tasks. In such a role, humans must maintain the situational awareness and understanding of the automated systems’ decisions. For this reason, only the deep understanding of the impact of automation on human performance will facilitate the evolution of the ATC to make the most of the automation benefits whilst ensuring safety. A well-defined human machine interaction must be developed fostering human confidence in the system functions while maintaining the ATCo in the loop of the operations.
To address this need, AUTOPACE Consortium assembles five organisations with a large experience on the field of ATM psychological modelling and ATM system operations. AUTOPACE is led by CRIDA, the R&D+i Centre of the Spanish Air Navigation Service Provider (ENAIRE) and formed by the University of Granada – Faculty of Psychology, the Polytechnic University of Madrid - School of Controllers; the University of Bologna; and the University of Belgrade - Faculty of Transport and Traffic Engineering.
AUTOPACE will perform analytical studies to estimate cognitive demanded resources in a 2050 environment based on the multiple resource theory. Later on, AUTOPACE Psychologists, ATM Experts, Controllers and Training experts will set the hypothesis to build a Psychological Model of the ATCo cognitive resources based on the attentional theories. These Theories predict different impact of automation on the demanded and available cognitive resources:
- The classical theory considers that automation only affects the task complexity and hence reduces the demanded resources. Therefore, automation might present a risk of lack of attention due to an excess of available resources.
- Alternative theories foresee that automation would also affect the pool of available resources depending on controller´s expectations: when the ATCo expects that the task is easy she/he will get bored and out-of-the-loop effect might appear. On the contrary, fears of automation failing would increase stress causing disorientation or erratic behaviour.
Finally, AUTOPACE will look at the future use of this Psychological Model to support the identification of future competences and training strategies.
As a result of its research activity, AUTOPACE will produce an experimental plan to validate the psychological model that determines the automation impact and it will provide understanding on how new competences and training strategies will help future controller to cope with automation. These two main outputs will enable the definition of a methodology for the analysis of the automation impact on Human Performance.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

"During the first semester of AUTOPACE, the framework upon which AUTOPACE research will be built has been set up, that is the definition of AUTOPACE Concept of Operations (ConOps) and Scenarios were automation features and cognitive resources will be assessed.
To do that, the state of the art with regards to 2050 foreseen developments in Air Traffic Management (ATM) and automation has been identified and reviewed. Inconsistencies between different sources have been addressed to achieve a consistent concept where the traffic characteristics and operational procedures, the main ATC systems functionalities and the ATM actors and their responsibilities are described.
2050 expected level of automation and its effect on procedures, system and personnel has been also identified. In fact, AUTOPACE has focused on two levels of automation to address the high uncertainty with regards to the overlapping of responsibilities between the ATC System and the ATCo:
- A high degree of automation where the ATC System develops the necessary actions for the orderly and safely traffic management, informing the ATCo.
- A medium degree of automation where the ATC System proposes actions and the ATCo decides which action to apply from the set of proposals displayed.
Two AUTOPACE Operational Scenarios have been identified describing the execution of the ATCo activities during En-route phase of flight for a high and a medium degree of automation. AUTOPACE Scenarios specify the expected responsibilities of the ATCo and the allocation of functions between the ATCo and the ATC system. Moreover, a detailed description of the processes and the system requirements is presented focusing on the ATCo participation.
Upon these two scenarios, three non-nominal situations for each scenario, have been identified to support the assessment of automation failures. For each non-nominal situation, the re-allocation of ATC functions that should take place to keep the Air traffic Service provision has been identified.
Finally, a preliminary identification of skills, aptitudes and knowledge required to operate in AUTOPACE scenarios constitutes the germ of the following AUTOPACE activities, which will be in charge of researching on the competences and training strategies to operate in 2050 automation.
AUTOPACE Concept of Operations and Scenarios are described in AUTOPACE deliverable D2.1 Future Automation Scenarios.
AUTOPACE dissemination activities have also been launched following AUTOPACE Dissemination and Exploitation Plan. AUTOPACE Project was presented at UPM Summer Course 2016 "ADDRESSING AVIATION AND ATM SAFETY CHALLENGES" in July; AUTOPACE webpage and Linkedin group have been prepared and are expected to be at full performance along September; and the preparation of AUTOPACE ‘ATCo Psychological Model with Automation’ Workshop in November has been launched."

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

Human operation in highly automated environments has been extensively researched finding out that the human-automation interaction presents serious performance drawbacks due to the risk of the “out of the loop” effect especially in case of automation fail. Several research paths are open to solve this issue and AUTOPACE is oriented to two main areas pending to be explored: Psychological Modelling to quantitatively predict optimal states of human-machine symbiosis; and new competences and training to cope with the effects of automation on humans.
AUTOPACE experimental plan will establish the hypothesis on how the ATCo cognition will be impacted. This Experimental Plan will serve to future research on human-automation interaction.
The identification of competences and training to operate in highly automated environments will allow the European Community to effectively implement advanced automation features, ensuring adequate safety level and optimized implementation costs in terms of training and technology adaptation. Moreover, it will accelerate the training path and increase the productivity of the ATCo operating in the foreseen environment.
The knowledge gained in the assessment of the automation features will also offer to the industry some advice about the areas to be researched when developing new technology to support the future ATM system.
Record Number: 193877 / Last updated on: 2017-01-27
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