Since the project has deployed an agile approach, there are several interim results already available. An initial version of human-AI teaming concept of operations tailored for aerospace domain and aligned with EASA guidelines for trustworthy and reliable AI has been drafted, implemented through an interactive demonstrator, and validated with pilots. The results are now being integrated into a new version of the human-AI teaming concept. Upon completion, this work will lay a foundation and establish guidelines for effective interaction and collaboration between humans and AI.
The Trustworthy Machine Reasoning Platform (TMRP) has been further matured and successfully tested in several situations. The TMRP represents an important enabler for incorporating trustworthy and explainable AI to the cockpit. With this technology, the pilots will gain insights into the actions of the automation and the rationale behind them, thereby enhancing cooperation and trust with AI.
Additionally, a successful validation of obvious pilot incapacitation detection has been conducted. Once implemented in aircraft, this detector can further improve safety by e.g. facilitating responses to incapacitation incidents, especially during single pilot operations and extended minimum crew scenarios, where one pilot may be resting during the cruise phase of flight.
The Task Load Monitor (TLM) can already predict the task load along a planned route with a high degree of accuracy. To further improve the system, it is necessary to consider not only the new route, but also the impact of rescheduling on the overall system. Areas for improvement include understanding the dynamics of task load, in particular the effects of multitasking in high demand situations and the cognitive load of switching between unrelated tasks. Investigating these factors will improve task prediction and task allocation.
In addition, improving the temporal accuracy of task predictions is crucial. A more accurate determination of the temporal distribution of tasks in flight will enable better analysis of task distribution and management strategies. Further development of TLM requires research into the effects of multitasking, cross-domain task switching, and precise task timing. Addressing these challenges will improve task allocation and promote safer and more efficient flight operations.
The project is going to focus on the further maturation of these results, their integration into an aircraft, and validating them during a real flight.
