Recent aircraft have seen the implementation of touchscreens (TSCs) on the flight deck, as they enable more intuitive and direct human-machine interactions. However, biodynamic feedthrough (BDFT), i.e., the direct transmission of the aircraft's accelerations through the pilot's body to the control inputs, is a cause for concern, preventing safe and reliable use of TSCs in turbulence. This paper describes a simulator experiment evaluating the performance of model-based mitigation of BDFT in a TSC dragging task performed in turbulence. In the experiment, a total of nine different vertical (heave) motion perturbations were tested: multisine signals resembling turbulence, stationary (Gaussian) and variable (patchy) simulated turbulence, each at three intensity levels (RMS acceleration of 0.75, 0.5, and 0.25 m/s2). For the multisine turbulence signals, on average over 87% accuracy of the identified personalized BDFT models was achieved for the high and medium turbulence levels, reducing to 74% for the low-intensity turbulence due to degraded BDFT consistency. Furthermore, BDFT models fitted to the Gaussian turbulence data were found to achieve an accuracy comparable to that observed for the multisine motion disturbances, with only 3.5% lower performance on average. As expected, for the more time-varying patchy turbulence cases, model-based BDFT cancellation was found to be 4.7% lower than for the Gaussian turbulence data. Finally, models generalizing BDFT dynamics across participants or experimental runs were found to always be outperformed by individual participant and individual trial models, giving up to 10% higher identification performance. Overall, these findings show that a model-based approach to canceling the effects of BDFT mitigation for TSCs in turbulence is promising, but that real-time identification and time-varying BDFT models will be needed to achieve consistently high mitigation performance in realistic variable turbulence.

The interaction between humans and AI in safety-critical systems presents a unique set of challenges that remain partially addressed by existing frameworks. These challenges stem from the complex interplay of requirements for transparency, trust, and explainability, coupled with the necessity for robust and safe decision-making. A framework that holistically integrates human and AI capabilities while addressing these concerns is notably required, bridging the critical gaps in designing, deploying, and maintaining safe and effective systems. This paper proposes a holistic conceptual framework for critical infrastructures by adopting an interdisciplinary approach. It integrates traditionally distinct fields such as mathematics, decision theory, computer science, philosophy, psychology, and cognitive engineering and draws on specialized engineering domains, particularly energy, mobility, and aeronautics. Its flexibility is further demonstrated through a case study on power grid management.

Artificial Intelligence (AI) is transforming every aspect of modern society. It demonstrates a high potential to contribute to more flexible operations of safety-critical network infrastructures under deep transformation to tackle global challenges, such as climate change, energy transition, efficiency, and digital transformation, including increasing infrastructure resilience to natural and human-made hazards. The widespread adoption of AI creates the conditions for a new and inevitable interaction between humans and AI-based decision systems. In such a scenario, creating an ecosystem in which humans and AI interact healthily, where the roles and positions of both actors are well-defined, is a critical challenge for research and industry in the coming years. This perspective article outlines the challenges and requirements for effective human-AI interaction by taking an interdisciplinary point of view that merges computer science, decision-making sciences, psychological constructs, and industrial practices. The work focuses on three emblematic safety-critical scenarios from two different domains: energy (power grids) and mobility (railway networks and air traffic management).

Reinforcement Learning (RL) is rapidly becoming a mainstay research direction within Air Traffic Management and Control (ATM/ATC). Many international consortia and individual works have explored its applicability to different ATC and U-Space / Urban Aircraft System Traffic Management (UTM) tasks, such as merging traffic flows, with varying levels of success. However, to date there is no common basis on which these RL techniques are compared, with many research parties building their own simulator and scenarios from scratch. This can diminish the value of this research, as the performance of an algorithm cannot be easily verified, or compared to that of other implementations. This hampers development in the long run. The gymnasium library shows for other research domains that this can be solved by providing a set of standardised environments, which can be used to test different algorithms, and compare them to benchmark results. This paper proposes BlueSky-Gym: a library that provides a similar set of test environments for the aviation domain, building on the existing open-source air traffic simulator BlueSky. The current BlueSky-Gym environments range from vertical descent environments, to static obstacle avoidance and traffic flow merging. Built upon the Gymnasium API and the BlueSky air traffic simulator, it delivers an open-source solution for the ATC-specific RL performance benchmark. In the initial release of BlueSky-Gym, 7 functional environments are presented. Preliminary experiments with PPO, SAC, DDPG and TD3 are presented in this paper. Results show stable training is obtained on all of the environments with the default hyperparameters. On some environments, there is a large performance gap, with the on-policy PPO often trailing, but overall no clear algorithm that outperforms others across the board in terms of total reward.