Decentralized Autonomous Traffic Management through Corridor Networks

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Jasmine Jerry Aloor, Aadarsh Govada, Hamsa Balakrishnan
• Venue: arXiv: cs.MA
• Published: 2026-06-22
• Collected: 2026-06-23
• URL: http://arxiv.org/abs/2606.23585v1
• PDF: https://arxiv.org/pdf/2606.23585v1
• DOI: UNKNOWN

Plain-Language Brief

Researchers propose a decentralized approach to managing traffic in Advanced Air Mobility (AAM) corridors using multi-agent reinforcement learning (MARL). The method is tested on various multi-corridor networks and shows good performance in terms of adhering to corridor boundaries, completing flights, average speeds, and maintaining safe distances.

What the Document Says

• Autonomous aircraft are increasing in scale and traffic density, making centralized management insufficient.
• Dedicated AAM corridors have been proposed for high-density autonomous traffic.
• A decentralized approach to traffic management is being developed using MARL.
• The method is tested on complex multi-corridor networks with merges and splits.
• Policies trained in a single-corridor setting perform well in various scenarios without centralized coordination or model retraining.
• Performance metrics include conformance to corridor boundaries, completion rates, average speeds, distance traveled, and maintenance of inter-aircraft separation.
• The policies require only locally coordinated entry, traversal, and exit behaviors but produce desirable traffic flows.

Key Concepts Mentioned

• autonomous aircraft
• Advanced Air Mobility (AAM)
• corridors
• multi-agent reinforcement learning (MARL)
• traffic management

Claims Made by the Document

• Centralized management becomes insufficient for autonomous aircraft.
• Decentralized approaches to traffic management in AAM corridors are being developed using MARL.
• The method shows good performance in various scenarios without centralized coordination or model retraining.

Evidence Used

• Increasing scale and density of autonomous aircraft
• Proposal of dedicated AAM corridors for high-density autonomous traffic
• Testing policies on complex multi-corridor networks

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

As autonomous aircraft are introduced at scale and traffic density increases, centralized management becomes insufficient to coordinate the large numbers of crewed and uncrewed aircraft. Dedicated Advanced Air Mobility (AAM) corridors have therefore been proposed for organizing high-density autonomous traffic flows. The desire to scalably provide autonomous aircraft flexibility in trajectory planning motivates the development of decentralized approaches to traffic management in AAM corridors. In this work, we extend a multi-agent reinforcement learning (MARL) approach to address the challenge of decentralized traffic flow management in air corridor networks. We test policies trained in a single-corridor setting on increasingly complex multi-corridor networks with combinations of merges and splits in a zero-shot manner. Experimental results demonstrate that learned behaviors transfer well to scenarios with varying traffic density, network geometry, and heterogeneous vehicle performance, without needing centralized coordination or model retraining. We evaluate system-level performance in terms of conformance to corridor boundaries, completion rates, average speeds, distance traveled, and maintenance of inter-aircraft separation. We find that although our policies require only locally coordinated entry, traversal, and exit behaviors, they collectively produce desirable traffic flows through the corridor network.