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

TL;DR

This paper presents a reinforcement learning framework for urban air mobility that simultaneously manages noise and safety, demonstrating effective multi-objective coordination in dense urban airspace.

Contribution

It introduces a unified RL-based decentralized air traffic management system that integrates noise and safety considerations for UAM operations.

Findings

System effectively balances noise and safety objectives.

Demonstrates tradeoffs among separation, noise, and energy efficiency.

Performs well under high traffic density conditions.

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.