用强化学习统一管理城市空运的噪音与安全，实现高效协同决策。
Integrated Noise and Safety Management in UAM via A Unified Reinforcement Learning Framework
- 基于强化学习构建分布式框架，联合优化飞行高度以兼顾噪音与安全。
- 高密度交通下三者权衡明显：安全优先，噪音与能耗依地区政策调整。
- 适用于政策制定者与空管系统设计者，推动绿色低噪城市空中交通落地。
城市空中交通（UAM）旨在通过小型飞行器改变密集城市地区的出行方式。然而，UAM面临关键运营挑战，即在降低噪音暴露与保持低空城市空域安全间距之间取得平衡，这两个目标常被分开处理。本文提出一种基于强化学习（RL）的空管系统，将噪音与安全纳入统一、去中心化的框架中。在此可扩展的协调方案下，代理在分层空域中运行，并学习调整飞行高度的策略，以共同管理噪音影响和间距约束。系统在双目标上表现优异，并揭示了在高密度交通下间距、噪音暴露与能效之间的权衡关系。其中，安全间距被赋予最高优先级；而噪音与能耗的重要性则因地理位置不同，受财政与公共政策影响。研究结果表明，强化学习与多目标协调策略在提升UAM安全性、静音性与效率方面具有巨大潜力。
原文摘要 · Abstract (English)
Urban Air Mobility (UAM) envisions the widespread use of small aerial vehicles to transform transportation in dense urban environments. However, UAM faces critical operational challenges, particularly the balance between minimizing noise exposure and maintaining safe separation in low-altitude urban airspace, two potentially conflicting objectives that are often addressed separately. We propose a reinforcement learning (RL)-based air traffic management system that integrates both noise and safety considerations within a unified, decentralized framework. Under this scalable air traffic coordination solution, agents operate in a structured, multi-layered airspace and learn altitude adjustment policies to jointly manage noise impact and separation constraints. The system demonstrates strong performance across both objectives and reveals tradeoffs among separation, noise exposure, and energy efficiency under high traffic density. Among the three objectives, safe separation is accorded the highest priority, whereas the relative significance of noise and energy varies by location and is contingent upon financial and public policy considerations. The findings highlight the potential of RL and multi-objective coordination strategies in enhancing the safety, quietness, and efficiency of UAM operations.
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