OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Human-Machine Interfaces;Trusted AI and Autonomy OBJECTIVE: Research, develop, and validate advanced training techniques and technologies that leverage cutting-edge psychological and physiological insights to improve human performance and psychological well-being within naval aviation training environments. DESCRIPTION: The Navy seeks a multi-disciplinary approach to the design development of novel manned and unmanned pilot operation training solutions drawing upon insights from high-performance domains such as Drone Racing League's [Refs 1 5, and 7]. This civilian population demonstrates exceptional hand-eye coordination, reaction times, and resilience to simulation sickness. These are highly useful traits and skills in naval aviation. Thorough research and examination of the unique physiological and psychological characteristics of individuals excelling in such environments, this effort aims to identify key factors contributing to their success including, but not limited to binocular vision, psychological resilience, and physiological adaptations [Refs 6 and 8]. To address these objectives, the Navy seeks a detailed investigation into the physiological and psychological profiles that distinguish high performers in extreme stress and high-demand environments in the initial phase. This exploration will not only encompass the intrinsic traits and capabilities that facilitate exceptional performance, but also the training methodologies and environmental factors that contribute to knowledge and skill development. The Navy seeks isolation of core components to identify transferable techniques and principles that can be adapted and integrated into naval aviation training programs. The investigation should include advanced data collection methods, such as eye-tracking technology and physiological monitoring, to gain insights into the cognitive and physical processes underlying peak performance and resilience to motion/simulation sickness. The second phase of the effort will involve the development of a comprehensive training and intervention program, tailored to the specific needs and challenges of naval aviation training. Efforts should consider leveraging digital technologies, including mobile apps and websites, to deliver customized training modules focused on enhancing cognitive flexibility, stress tolerance and resilience, increased human performance in extreme environments, and physiological readiness [Refs 6 and 8]. The interventions will be designed to improve overall human performance and well-being, with a particular focus on mitigating the effects of motion/simulation sickness, a common challenge in aviation training in a manner that addresses human factors considerations [Ref 9]. Efforts should incorporate evidence-based techniques from existing relevant fields such as positive psychology and sports psychology, adapted for the unique context of naval aviation, to foster a positive training environment that supports mental health and peak performance [Refs 10 and 11]. In parallel, efforts should explore methods to establish a predictive model for individual performance variation, integrating a wide range of data points from physiological metrics to lifestyle factors such as diet, sleep, and physical activity. This model will serve as a foundational tool for customizing training approaches to the individual, optimizing learning and performance outcomes. By identifying key predictors of success and areas of potential improvement, the model will enable trainers to tailor interventions more effectively, ensuring that each aviator receives the support and guidance needed to reach their full potential. Findings should be considered for recommendations for selection and training. Finally, the initiative will explore the commercialization potential of the developed training program, considering its applicability not only within the military aviation context, but also in commercial and civilian aerospace sectors. As space travel and commercial aviation continue to evolve, there is a growing demand for training solutions that can prepare individuals for the challenges of these environments, from motion sickness to the psychological stresses of long-duration flights. The project's outcomes could thus have broad implications, offering valuable tools and insights for a wide range of high-stress professions and activities. This multi-faceted approach, combining rigorous research with practical application and commercial potential, positions the initiative to make a significant impact on the effectiveness and well-being of naval aviators and beyond. PHASE I: Research, develop, and validate the taxonomy detailing the knowledge, skills, attitudes, and individual factors that contribute to high resilience against motion/simulation sickness and peak performance in high-stress environments. This Phase will also investigate the feasibility and alignment of existing interventions from sports psychology and positive psychology for adaptation or integration into naval aviation training solutions. Initial considerations should be made for any software/technology solutions to be in compliance with Risk Management Framework policy and requirements. The Phase I effort will include prototype plans to be developed under Phase II. PHASE II: Leverage insights from Phase I to develop a comprehensive training program, integrating mobile app or website-based interventions, tailored to enhance the psychological well-being and performance of naval aviators. Include specific training techniques aimed at improving resilience to motion/simulation sickness, enhancing cognitive and physical performance, and promoting psychological health. Considerations should be made for any software/technology solutions to be in compliance with Risk Management Framework policy and requirements. PHASE III DUAL USE APPLICATIONS: Conduct architecture hardening and engineering documentation to facilitate an Authority to Operate (ATO) approval for software/technology solutions as a standalone package or coordinate with a program to become part of a larger system approved ATO. Transition focused training effectiveness evaluations will offer input for data driven decision making associated with future acquisitions. Continued enhancements to technology may include automated processes for scenario generation, content conversion, sustainment of the system infrastructure, as well as commercialization of the capability to other stakeholders and domains. Performance in high-stress environments or dynamic individual/team jobs that require increased interaction with remote and/or automated technology continue to increase. As technology adoption in a variety of fields increase, the potential utility of these types of training solutions will increase. In the near-term, commercial aviation offers a highly related domain for consideration of commercialization. The increased feasibility of remote and automated technology integration that impacts the complexity of jobs in homeland security or transportation security may benefit from similar technology. As a variety of commercial applications and domains consider automated human-in-the loop unmanned system integration (e.g., logistics, commercial goods transportation, food delivery, local area transit), considerations will be needed for the selection and training of personnel involved thereby increasing the commercialization applicability of the technology developed under this effort. REFERENCES: 1. Pfeiffer, C. and Scaramuzza, D. Human-piloted drone racing: Visual processing and control. IEEE Robotics and Automation Letters, 6(2), 2021, pp. 3467-3474. IEEE. https://ieeexplore.ieee.org/abstract/document/9372809 2. Song, Y.; Steinweg, M.; Kaufmann, E. and Scaramuzza, D. Autonomous drone racing with deep reinforcement learning. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2021, pp. 1205-1212. https://ieeexplore.ieee.org/abstract/document/9636053 3. Pfeiffer, C. andScaramuzza, D. Expertise affects drone racing performance. arXiv preprint arXiv:2109.07307, 2021. https://doi.org/10.48550/arXiv.2109.07307 4. Barin, A.; Dolgov, I. and Toups Dugas, P. O. Understanding dangerous play: A grounded theory analysis of high-performance drone racing crashes. Proceedings of the annual symposium on computer-human interaction in play, October 2017, pp. 485-496. https://doi.org/10.1145/3116595.3116611 5. Pfeiffer, C;, Wengeler, S.; Loquercio, A. and Scaramuzza, D. Visual attention prediction improves performance of autonomous drone racing agents. Plos one, 17(3), e0264471, 2022. https://doi.org/10.1371/journal.pone.0264471 6. Fraser, W. D. Stress, cognition, drones, and adaptive tasks. Aerospace Medicine and Human Performance, 91(4), 2020, pp. 376-378. https://doi.org/10.3357/AMHP.5584.2020 7. Lochtefeld, J.; Schlager, S.; Bryan, S.; Harbour, S. and Colter, J. Human Vs. Autonomous Agents: Drone racing and Obstacle Avoidance. 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), September 2022, pp. 1-5. https://ieeexplore.ieee.org/abstract/document/9925887 8. Memar, A. H. and Esfahani, E. T. Physiological measures for human performance analysis in human-robot teamwork: Case of tele-exploration. IEEE access, 6, 2018, pp. 3694-3705. https://ieeexplore.ieee.org/abstract/document/8248746/ 9. Stark, B.; Coopmans, C. and Chen, Y. A framework for analyzing human factors in unmanned aerial systems. 2012 5th international symposium on resilient control systems, August 2012, pp. 13-18. https://ieeexplore.ieee.org/abstract/document/6309286/ 10. Huang, H. M.; Messina, E. and Jacoff, A. Performance measures framework for unmanned systems (PerMFUS) initial perspective. Proceedings of the 9th workshop on performance metrics for intelligent systems, September 2009, pp. 65-72. https://dl.acm.org/doi/abs/10.1145/1865909.1865923 11. Freedy, A.; DeVisser, E.; Weltman, G. and Coeyman, N. Measurement of trust in human-robot collaboration. 2007 International symposium on collaborative technologies and systems, May 2007, pp. 106-114. https://ieeexplore.ieee.org/abstract/document/4621745/ KEYWORDS: Training; Human Performance; Psychological Well-Being; High-Stress Domains; Resilience; Motion/Simulation Sickness
