Value-Decomposed Reinforcement Learning Framework for Taxiway Routing with Hierarchical Conflict-Aware Observations
Researchers present CaTR, a reinforcement learning framework that optimizes real-time taxiway routing and conflict avoidance for multiple aircraft at airports. The system uses hierarchical traffic representation and value-decomposed learning to balance safety and efficiency, demonstrating superior performance compared to traditional planning and optimization methods while maintaining practical computational speed.
CaTR addresses a critical operational challenge in airport management where taxiway routing and collision avoidance must occur simultaneously under strict real-time constraints. Traditional optimization approaches struggle with computational overhead during peak traffic periods, while conventional reinforcement learning models often fail to adequately represent downstream safety risks. This research bridges that gap by introducing a hierarchical observation model that captures both immediate and future traffic conflicts, enabling the AI system to make safer routing decisions without excessive computational delay.
The framework's innovation lies in its value-decomposed approach, which prioritizes safety-critical objectives while maintaining efficiency gains. By using grid-based environment modeling with action masking, CaTR constrains the decision space to physically feasible routes, reducing the learning burden. Testing on a realistic simulation of Changsha Huanghua International Airport across varying traffic densities demonstrates the system's robustness and practical applicability.
For airport operators and aviation technology developers, this work represents meaningful progress in automating surface operations management, a sector increasingly focused on capacity optimization and safety enhancement. The practical runtime performance suggests viability for deployment in actual air traffic management systems. The research could influence how major airports implement autonomous surface operations, potentially reducing ground delays, fuel consumption, and operational costs while improving safety metrics.
Future development should focus on integration with existing airport infrastructure, testing across diverse airport configurations, and validation with real operational data. The framework's success could catalyze broader adoption of AI-driven surface management systems globally, particularly at congested international airports.
- →CaTR uses hierarchical foresight representation to model both current and future traffic conflicts for safer routing decisions
- →Value-decomposed reinforcement learning prioritizes safety-critical objectives while maintaining operational efficiency
- →System achieves superior safety-efficiency trade-offs compared to planning and optimization baselines with practical runtime performance
- →Grid-based environment with action masking reduces computational complexity while constraining decisions to feasible routes
- →Testing on realistic airport simulation demonstrates potential for real-world deployment in air traffic management systems