Contemporary trends in Uncrewed Aircraft Systems Traffic Management (UTM) and Advanced Air Mobility (AAM) are redefining low-altitude airspace operations, particularly in urban and suburban settings where traditional airspace management approaches are inadequate to support the predicted air transport demands. To address these challenges, the development of an integrated Low-Altitude Airspace Management (LAAM) framework is seen as an essential next step, requiring new flight systems and infrastructure tailored to the distinct challenges of these environments. Cyber technologies, including automation and Artificial Intelligence (AI), play a crucial role in LAAM by integrating data from Communication, Navigation, and Surveillance (CNS) systems to support real-time and automated decision-making for separation assurance and flow management. While human operators and social interactions retain a very important role in LAAM collaborative decision-making processes, the reliance on automation is expected to continue growing, driven by the need to effectively manage the challenges arising from the increasing number and diversity of highly automated and uncrewed aircraft. Regulatory frameworks must adapt to accommodate the unique characteristics of AAM operations, ensuring the adequacy of safety standards and airspace regulations. In particular, airspace design is bound to evolve to accommodate Vertical/Short Take-off and Landing (V/STOL) aircraft’s distinct capabilities and requirements. The deployment of AI in safety-critical systems will require rigorous verification, validation, and certification processes to ensure reliability and trustworthiness. To address these complex and interrelated challenges, a harmonized LAAM Concept of Operations (CONOPS) is needed, which should encapsulate both UTM and emerging AAM requirements, while clearly specifying the role of human operators for various levels of automation. Additionally, new system functionalities should be developed to enhance human-machine teaming by focussing on CNS performance-based airspace modeling and dynamic airspace management. Based on these premises, an integrated approach to Multi-Domain Traffic Management (MDTM) is emerging, with promising future perspectives for the safe, efficient and sustainable operation of highly automated and autonomous flight systems in all present and likely future classes of airspace.

中文翻译：

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无人驾驶飞机的低空空域管理和先进空中机动性的进展


无人驾驶飞机系统交通管理 （UTM） 和高级空中交通 （AAM） 的当代趋势正在重新定义低空空域运营，尤其是在传统空域管理方法不足以支持预测的航空运输需求的城市和郊区环境中。为了应对这些挑战，开发集成的低空空域管理 （LAAM） 框架被视为必不可少的下一步，需要针对这些环境的独特挑战量身定制的新飞行系统和基础设施。包括自动化和人工智能 （AI） 在内的网络技术在 LAAM 中发挥着至关重要的作用，它集成了来自通信、导航和监视 （CNS） 系统的数据，以支持分离保证和流量管理的实时和自动化决策。虽然人类作员和社会互动在 LAAM 协作决策过程中仍然发挥着非常重要的作用，但随着需要有效管理高度自动化和无人驾驶飞机数量和多样性的增加所带来的挑战，预计对自动化的依赖将继续增长。监管框架必须适应 AAM 运营的独特特征，确保安全标准和空域法规的充分性。特别是，空域设计必将不断发展，以适应垂直/短距离起降 （V/STOL） 飞机的独特能力和要求。在安全关键系统中部署 AI 需要严格的验证、确认和认证流程，以确保可靠性和可信度。 为了应对这些复杂且相互关联的挑战，需要一个统一的 LAAM作概念 （CONOPS），它应该包含 UTM 和新兴的 AAM 要求，同时明确指定人工作员在各种自动化级别中的作用。此外，应开发新的系统功能，通过专注于基于 CNS 性能的空域建模和动态空域管理来增强人机协作。基于这些前提，一种多域交通管理 （MDTM） 的综合方法正在出现，为在所有当前和未来可能的空域类别中安全、高效和可持续地运行高度自动化和自主飞行系统带来了光明的未来前景。