Integrated Noise and Safety Management in UAM via A Unified Reinforcement Learning Framework

2025-08-22

AI Summary

Coordinating noise exposure mitigation and safety separation assurance remains challenging in urban air mobility (UAM) operations. Method: This paper proposes a decentralized air traffic management framework based on multi-objective reinforcement learning. It introduces a hierarchical airspace structure enabling cooperative decision-making under local observations, and— for the first time—jointly optimizes noise suppression, minimum safe separation, and energy efficiency within a unified modeling framework. Contribution/Results: By explicitly characterizing the trade-offs among these three objectives in high-density scenarios, the system significantly reduces ground-level noise exposure while guaranteeing safety and maintaining high energy efficiency. Experimental results demonstrate strong robustness and scalability of the intelligent coordination strategy in complex low-altitude urban environments, establishing a novel paradigm for UAM operational management.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

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 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. The findings highlight the potential of RL and multi-objective coordination strategies in enhancing the safety, quietness, and efficiency of UAM operations.

Problem

• Balancing noise minimization and safety in urban air mobility
• Integrating noise and safety management via reinforcement learning
• Addressing separation constraints and noise exposure tradeoffs

Innovation

• Reinforcement learning-based decentralized air traffic management
• Unified framework integrating noise and safety objectives
• Multi-layered airspace with altitude adjustment policies