Revealing Safety-Critical Scenarios for UTM via Transformer
Quick Answer
This study presents a transformer-based reinforcement learning approach for identifying vulnerabilities in Unmanned Traffic Management (UTM) systems, achieving an 8x improvement in discovery efficiency over expert-guided testing.
Quick Take
The proposed framework utilizes attention mechanisms to model system states and generate targeted test scenarios, effectively uncovering critical edge cases missed by traditional methods.
Key Points
- Proposed a transformer-based RL framework for UTM vulnerability discovery.
- Achieved 8x improvement in discovery efficiency over expert-guided methods.
- Introduced a Policy Model for generating targeted test scenarios.
- Utilized a risk-based reward function to guide exploration.
- Identified critical edge cases previously missed by traditional testing.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Unmanned Traffic Management (UTM) systems are cloud-based platforms designed to manage and coordinate multiple aerial vehicles remotely. UTM systems are safety-critical which cannot tolerate failures like crash or collision. To reveal latent vulnerabilities, there are neither optimal failure-exposing demonstrations nor clear reward signals. Additionally, UTM's self-healing capability introduces the ``long-tail effect'' of critical failures. We propose framing UTM vulnerability discovery as a sequence modeling problem amenable to transformer-based RL architectures. Our approach leverages attention mechanisms to directly model the relationship among system states, and predict optimal actions. Our framework introduces a Policy Model that generates targeted test scenarios and an Action Sampler that enforces domain constraints. We use a risk-based reward function to guide exploration. Through extensive evaluation on a 700-hour simulation study, we demonstrate an 8$\times$ improvement in vulnerability discovery efficiency compared to expert-guided testing. It also discovers critical edge cases that traditional methods have missed.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.31114 [cs.AI] |
| (or arXiv:2606.31114v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.31114 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Huaze Tang [view email]
[v1]
Tue, 30 Jun 2026 04:21:20 UTC (10,654 KB)
— Originally published at arxiv.org
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