Predictive Sectorization and Bayesian Optimized Consensus for Admission Control in Autonomous Airspace Operations

2026-04-18

AI Summary

This study addresses the limitations of traditional static airspace partitioning in accommodating growing air traffic, which leads to poor scalability and uneven workload distribution. To overcome these challenges, the authors propose a scalable, human-in-the-loop autonomous airspace access control framework. First, an XGBoost classifier leverages 23-dimensional FAA SWIM traffic features to predict optimal 3D grid configurations with 91.38% accuracy. Second, a leaderless Paxos consensus protocol enables decentralized coordination among aircraft for airspace entry, achieving over 96% entry success rates and low near-mid collision probabilities. Finally, Gaussian process-based Bayesian optimization automatically tunes airport-specific parameters within 50 trials. This work is the first to integrate predictive airspace partitioning, decentralized consensus, and automated parameter adaptation, significantly enhancing system adaptability and operational efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Conventional air traffic control divides airspace into specific regions, creating a scaling bottleneck as traffic grows. Choosing how to partition airspace is not straightforward because grid size affects workload, handoff frequency, and the capacity of whatever coordination mechanism operates within each sector. We present a three stage pipeline that automates sectorization and sector coordination while preserving human oversight. First, a two stage XGBoost classifier predicts the optimal 3D grid configuration from 23 location-agnostic traffic features, achieving 91.38% accuracy on a 65,000 sample dataset derived from Federal Aviation Administration System Wide Information Management replays. Second, a leaderless Paxos consensus protocol lets aircraft coordinate sector entries among themselves, maintaining above 96% entry success with low near mid-air collision rates across all tested configurations. Third, Bayesian Optimization with a Gaussian Process surrogate tunes eight protocol parameters per airport in 50 trials, revealing that each traffic environment requires a qualitatively different configuration. The resulting pipeline offers a practical path toward scalable, autonomous airspace management as traffic demand outpaces controller capacity.

Problem

• airspace sectorization
• admission control
• scalability
• autonomous airspace operations
• traffic management

Innovation

• Predictive Sectorization
• Bayesian Optimization
• Leaderless Paxos Consensus
• Autonomous Airspace Management
• XGBoost Classification