Integrated Noise and Safety Management in UAM via A Unified Reinforcement Learning Framework
This work addresses critical operational challenges for UAM in dense urban environments, though it appears incremental as it integrates existing RL methods for known bottlenecks.
The paper tackles the problem of balancing noise minimization and safety in Urban Air Mobility by proposing a reinforcement learning-based air traffic management system, which demonstrates strong performance in managing both objectives and reveals tradeoffs under high traffic density.
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.