Decentralized Autonomous Traffic Management through Corridor Networks
For autonomous aircraft traffic management, this work addresses the scalability challenge of decentralized coordination in corridor networks, but the approach is incremental as it extends existing MARL methods.
This work extends a multi-agent reinforcement learning approach to manage decentralized traffic flows in Advanced Air Mobility corridor networks. Policies trained in single-corridor settings transfer zero-shot to complex multi-corridor networks with merges and splits, maintaining performance across varying traffic density, geometry, and heterogeneous vehicle performance without centralized coordination.
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.