Secure Coordination for Vertiport Sequencing in Advanced Air Mobility

2026-05-19 · Source: cs.MA updates on arXiv.org · Field: Transportation & Mobility — Autonomous Vehicles & Smart Transportation, Robotics & Autonomous Systems, Cybersecurity & Data Privacy · Depth: Expert, long

Summary

Advanced Air Mobility (AAM) operations face significant challenges in managing dense traffic near vertiports, particularly due to vulnerabilities in sequencing decisions based on self-reported information. Self-interested vehicles may misreport their estimated time of arrival (ETA) to gain favorable landing priority, while malicious actors could spoof data to disrupt operations or induce congestion. This research proposes a secure coordination mechanism where a central coordinator integrates self-reported Remote-ID data with independent surveillance measurements. Since surveillance data inherently contains uncertainty, falsified reports might still appear consistent with the sensing uncertainty region. To address this, the problem is framed as a robust design problem. The study differentiates between strategic misreporting, where vehicles aim to improve their own sequencing outcome, and malicious spoofing, where an attacker seeks to degrade the overall system-level objective. The full paper will detail robust sequencing rules and evaluate their effectiveness in representative vertiport scenarios.

Key takeaway

For AI Security Engineers designing Advanced Air Mobility (AAM) vertiport systems, you must integrate robust coordination mechanisms that account for both self-interested misreporting and malicious spoofing. Your systems should combine Remote-ID with independent surveillance to identify and mitigate falsified arrival times, even when reports fall within sensing uncertainty. This approach ensures reliable sequencing, reduces congestion, and prevents operational disruption by proactively addressing vulnerabilities in shared air traffic management.

Key insights

Vertiport sequencing requires robust coordination against self-interested or malicious false reporting, integrating surveillance with self-reported data.

Principles

• Combine self-reported data with independent surveillance.
• Model false reporting as strategic or adversarial.
• Formulate sequencing as a robust design problem.

Method

A coordinator combines Remote-ID and surveillance to infer arrival times, then formulates robust sequencing rules over surveillance-consistent uncertainty sets, distinguishing self-interested misreporting from malicious spoofing.

In practice

• Use surveillance to validate self-reported ETAs.
• Implement robust sequencing rules for AAM.
• Differentiate between types of false reporting.

Topics

• Advanced Air Mobility
• Vertiport Sequencing
• Remote-ID Security
• Surveillance Systems
• Robust Control
• Adversarial Spoofing

Best for: Research Scientist, AI Scientist, AI Security Engineer, Robotics Engineer

Related on AIssential

• Decentralized Coordination of Autonomous Traffic Through Advanced Air Mobility Corridors — Autonomous aircraft can self-organize efficiently in decentralized AAM corridors, challenging prior assumptions.
• Goal-Autopilot: A Verifiable Anti-Fabrication Firewall for Unattended Long-Horizon Agents — Goal-Autopilot structurally prevents LLM agent fabrication by enforcing verifiable state transitions and terminal conditions.
• TRACE: Trajectory Reasoning through Adaptive Cross-Step Evidence Aggregation for LLM Agents — TRACE adaptively aggregates cross-step evidence to detect hidden malicious objectives in long LLM agent trajectories.
• Think Like a Pilot: Fine-Grained Long-Horizon UAV Navigation — An asynchronous VLM and diffusion model architecture enables fine-grained, long-horizon UAV navigation with real-time continuous control.
• A Trace-Based Assurance Framework for Agentic AI Orchestration: Contracts, Testing, and Governance — A trace-based framework enhances Agentic AI reliability through contracts, stress testing, fault injection, and runtime governance.
• Dynamic Coordination Strategy Selection for Enterprise Multi-Agent Systems — Dynamic strategy selection for enterprise multi-agent systems is a near-best routing heuristic, not a deterministic winner-selection law.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.