AeroIntent: A Synchronized Multimodal Benchmark for UAV Mission Intent Recognition

Key points

• Existing multimodal UAV datasets primarily support detection, tracking, and localization, leaving limited support for evaluating mission-intent recognition across held-out trajectory programs and incomplete radar histories.1
• We introduce AeroIntent, a simulation-based benchmark comprising 990 synchronized multimodal sequences generated by 22 trajectory programs across five mission intents.1
• Building on this benchmark, we propose AeroMIR, a structured Multimodal Intent Reasoner that integrates radar trajectories, derived behavioral features, selected visual evidence, and task knowledge for candidate-based UAV intent recognition.1
• Under a strict program-disjoint protocol evaluating unseen tactical realizations, AeroMIR achieves a mean accuracy of 71.56% and a mean Macro-F1 of 69.84% across three seeds, outperforming the strongest trajectory-only baseline by 7.79 Macro-F1 points and a conventional radar-RGB late-fusion baseline by 19.32 points.1

Sources (1)

• [1]AeroIntent: A Synchronized Multimodal Benchmark for UAV Mission Intent Recognition
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 06:23 AM

  Reliable recognition of mission-level UAV intent from multimodal observations is essential for low-altitude airspace surveillance and security.

  Existing multimodal UAV datasets primarily support detection, tracking, and localization, leaving limited support for evaluating mission-intent recognition across held-out trajectory programs and incomplete radar histories.

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