Eurocontrol

BACKGROUND/OBJECTIVES:The human performance envelope (HPE) is a multidimensional model that represents the range in which an individual operator's performance is acceptable or begins to become dangerous. Although several alternative models have been proposed, HPE currently remains primarily a theoretical concept. The goal of the study was therefore to translate this theoretical concept into practical applications, seeking to characterize and measure how HPE manifests itself in real-world contexts. METHODS:Multivariate Autoregressive (MVAR) models and conditional transfer entropy (cTE) have been used in the analysis of complex systems in which processes are interdependent and mutually influence their dynamics over time. Professional Air Traffic Controllers were involved in the study and asked to deal with realistic traffic scenarios while their behavioural, subjective and neurophysiological data were collected. MVAR-cTE models were then employed to estimate the interactions among controller human factors and to identify the most appropriate characterization of the HPE. RESULTS:The results showed high and significant correlations among each controller's performance and the corresponding neurophysiological-based HPE values. Furthermore, high-performance conditions (best) were characterized by significantly higher HPE values and higher inter-human factor connections compared to the low-performance (worst) status. This evidence suggested that a densely interconnected network of Human Factors is a prerequisite for operational resilience. CONCLUSIONS:The study provided the first application of a neurophysiological framework to model the directed interactions between human factors, translating the theoretical HPE into a quantifiable model validated against operator performance.

In industry and academia, the autonomy level of computerized systems is today being characterized by a singular dimension ranging from fully human labor (zero automation) to full automation with no human involvement. However, with more capable artificial intelligence supporting the human, it is increasingly relevant to understand what kind of cognitive control work that is automated. A system that can perform advanced cognitive tasks (e.g., adaptation to new control situations) is very different from one that can only perform basic tasks (e.g., lane keeping). These questions become relevant when designing or evaluating human-AI systems, namely how the cognitive work shall be distributed among the human and the AI agent, and, crucially, what kind of cognitive work they will perform together. Therefore, in this article, the aim is to support the inclusion of the cognitive dimension in systems assessment. As an analysis tool, we propose to include the Levels of Autonomy in Cognitive Control (LACC) jointly with the established and widely used Levels of Automation (LOA). We describe the approach and its bidimensional LACC-LOA matrix assessing six digital assistant concepts in the aviation domain with different levels of cognitive power. In our study, we considered one key scenario from each use case, through an online workshop format. In sum, the LACC-LOA assessment method granted considerable granularity to our understanding not simply of "who/what is in charge?," but "in charge of what?" Most of the use cases examined straddled not one but several LOAs, and the LACC added a useful dimension showing where the core "cognitive work" resided in these LOAs for human and AI. More generally, this extended mapping has implications for determining how the introduction of AI-based systems could affect human agency in the system. [GRAPHICS]

The expected rise in space operations challenges the European Air Traffic Management (ATM), as traditional static airspace segregation causes operational inefficiencies. To mitigate this, a new function within the European Network Manager Operations Centre (NMOC), supported by the novel Network Real-time Mission Monitoring (N-RMM) tool, and complemented by ad hoc Debris Response Areas (DRAs), are being developed. This paper introduces the safety assessment of this approach using the Expanded Safety Reference Material (E-SRM) methodology. By developing specialised Accident Incident Models (AIMs) for mid-air collisions with space debris, we quantify safety barrier efficiencies and define a Risk Classification Scheme (RCS). The results indicate that by developing dedicated AIMs for the proposed dynamic airspace-management concept, the derived safety criteria, under the stated assumptions, are compatible with the targeted safety thresholds. The potential reduction in segregated airspace volume and duration remains an expected operational benefit to be quantified in subsequent validation work.

This study presents a comprehensive analysis of the positional accuracy and anomalies in Automatic Dependent Surveillance-Broadcast (ADS-B) position reports using a high-resolution multilateration (MLAT) system as an independent reference. Leveraging a network of over 170 receivers deployed across Germany, we assess ADS-B accuracy across various aircraft types, transponder versions, and operational conditions. Our findings confirm that latency is the primary source of ADS-B positional errors, predominantly affecting along-track deviations. While older ADS-B versions exhibit significantly higher latency-induced errors, ADS-B version 2 transponders generally remain within the latency constraints set by the standard. Additionally, we investigate the impact of GNSS integrity parameters and avionics variability on ADS-B accuracy. Beyond air traffic surveillance, we provide recommendations for applications leveraging ADS-B as a reference signal in high-precision applications, such as system calibration and built-in self-testing. By characterizing ADS-B positional accuracy and its influencing factors, this study enhances the understanding of ADS-B integrity, contributing to improved aviation safety, high-precision localization, and future advancements in air traffic management.