具有空域意识的应急着陆规划
Airspace-aware Contingency Landing Planning
AI总结：
本文提出了一种实时应急着陆规划器，通过结合空域和地面风险评估，减少交通干扰并提高着陆效率。
摘要：
本文开发了一种实时、基于搜索的飞机应急着陆规划器，该规划器在考虑地面风险的同时最小化交通干扰。空域模型捕捉了密集的航空交通出发和到达流量、直升机走廊以及禁飞区，并通过华盛顿特区案例研究进行演示。历史自动依赖监视-广播（ADS-B）数据被处理以估计空域密度。一种低延迟的计算几何算法生成基于接近度的热图，用于高风险走廊和受限区域周围。空域风险被量化为着陆轨迹在拥挤区域内的累计暴露时间，而地面风险则从飞越的人口密度来共同指导轨迹选择。一个着陆地点选择模块进一步减轻了对常规航空交通运营的干扰。与最小风险的杜宾斯解法相比，所提出的规划器在保持实时性能的同时，实现了更低的联合风险和减少的空域干扰。在仅考虑空域风险的情况下，规划器在笔记本电脑上生成轨迹的平均时间为2.9秒。未来的工作将纳入动态空域更新，以实现减少实时交通重新路由需求的空间时间应急着陆规划。
英文摘要：
This paper develops a real-time, search-based aircraft contingency landing planner that minimizes traffic disruptions while accounting for ground risk. The airspace model captures dense air traffic departure and arrival flows, helicopter corridors, and prohibited zones and is demonstrated with a Washington, D.C., area case study. Historical Automatic Dependent Surveillance-Broadcast (ADS-B) data are processed to estimate air traffic density. A low-latency computational geometry algorithm generates proximity-based heatmaps around high-risk corridors and restricted regions. Airspace risk is quantified as the cumulative exposure time of a landing trajectory within congested regions, while ground risk is assessed from overflown population density to jointly guide trajectory selection. A landing site selection module further mitigates disruption to nominal air traffic operations. Benchmarking against minimum-risk Dubins solutions demonstrates that the proposed planner achieves lower joint risk and reduced airspace disruption while maintaining real-time performance. Under airspace-risk-only conditions, the planner generates trajectories within an average of 2.9 seconds on a laptop computer. Future work will incorporate dynamic air traffic updates to enable spatiotemporal contingency landing planning that minimizes the need for real-time traffic rerouting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
