aceA risk-informed urban low-altitude air network planning method under large language model guidance
The rapid expansion of urban air mobility calls for safe low-altitude corridor networks across complex urban airspace, where heterogeneous positioning uncertainties caused by navigation and communication errors elevate collision risk and constrain both route feasibility and network-wide connectivity. To address this issue, this study proposes a risk-informed network planning framework that integrates three-dimensional collision risk modeling with language-model-guided multi-agent learning for the construction of…
In a Nanjing case study, language-model-derived priors steered learning agents toward safer altitude regimes, reducing training effort by 50.3% while producing risk-compliant low-altitude air corridor networks.
The demonstration rests on one case study of Nanjing's low-altitude airspace, so generalization to other urban environments is not established by the supplied text.
Evidence
- Peer-reviewedAccident Analysis & Prevention2026-10-17
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Truvace Impact Record TRV-2026-1813, v1: “A risk-informed urban low-altitude air network planning method under large language model guidance.” Truvace, 2026-10-18. /record/TRV-2026-1813 (accessed at citation time). sha256 55896a57078a56b8…
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