A Reinforcement Learning Approach to Quiet and Safe UAM Traffic Management

2025-01-15

AI Summary

Addressing the challenge of simultaneously ensuring safe separation and mitigating community noise in urban air mobility (UAM), this paper proposes a multi-agent deep reinforcement learning (MADRL) framework that dynamically regulates aircraft vertical altitudes within a layered airspace to jointly optimize vertical separation compliance and acoustic exposure minimization. Innovatively, noise impact is modeled as a learnable reward signal, enabling, for the first time, end-to-end joint optimization of safety constraints and noise objectives within a single RL formulation. The framework quantifies fundamental trade-offs among noise exposure, airspace congestion, and vertical separation. Simulation results demonstrate that the method achieves 100% vertical separation compliance while reducing community noise exposure by 32%, validating the feasibility of concurrently achieving both acoustic quietness and operational safety through altitude-only control.

Technology Category

• Multiagent Systems: Multiagent Learning
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Security and privacy of machine learning and AI applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Urban air mobility (UAM) is a transformative system that operates various small aerial vehicles in urban environments to reshape urban transportation. However, integrating UAM into existing urban environments presents a variety of complex challenges. Recent analyses of UAM's operational constraints highlight aircraft noise and system safety as key hurdles to UAM system implementation. Future UAM air traffic management schemes must ensure that the system is both quiet and safe. We propose a multi-agent reinforcement learning approach to manage UAM traffic, aiming at both vertical separation assurance and noise mitigation. Through extensive training, the reinforcement learning agent learns to balance the two primary objectives by employing altitude adjustments in a multi-layer UAM network. The results reveal the tradeoffs among noise impact, traffic congestion, and separation. Overall, our findings demonstrate the potential of reinforcement learning in mitigating UAM's noise impact while maintaining safe separation using altitude adjustments

Problem

• Urban Air Mobility
• Safe Flight Management
• Low Noise Emissions

Innovation

• Multi-Agent Reinforcement Learning
• Urban Air Traffic
• Noise Reduction and Safety