用注意力机制预测机场地面飞机轨迹，提升安全导航能力
DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction
- 基于变压器架构，结合可达区域采样动态获取环境上下文
- 在Amelia-10数据集上显著优于现有方法，尤其在长时预测中表现突出
- 适合研究机场自动化、智能交通系统的开发者与研究人员
高级自动化是应对商业航班密度上升、提升地面运行安全的关键技术。尽管自主驾驶中的运动预测已较为成熟，但其在机场地勤移动中的应用仍不充分。为实现该领域的高效精准预测，我们提出DESCENT——一种基于变压器的架构，可处理异质动态与严格拓扑约束。该方法引入潜在可达集（PRS）上下文采样机制，自适应地在不同运行阶段收集机场环境信息。结合检测式变压器解码器，生成高精度轨迹预测。在Amelia-10基准上的大量评估表明，相比当前最优基线，性能显著提升，尤其在安全关键场景下，其领域感知采样提供了长时程预测所需的关键上下文。
原文摘要 · Abstract (English)
Advanced automation is a key technology for enhancing the safety of ground operations amidst the increasing density of commercial air traffic. While motion forecasting is a well-studied task in autonomous driving, its application to airport surface movements remains underexplored. To enable efficient and accurate prediction in this domain, we propose DESCENT, a transformer-based architecture designed to handle heterogeneous dynamics and strict topological constraints. Our approach features a Potential Reachable Set (PRS) context sampling mechanism that adaptively collects airfield environment context across diverse operational phases. Combined with a detection transformer-based decoder, DESCENT generates accurate trajectory forecasts. Extensive evaluations on the Amelia-10 benchmark demonstrate significant performance improvements over state-of-the-art baselines. These gains are especially pronounced in safety-critical scenarios, where our domain-aware sampling provides critical long-horizon context necessary for safe navigation.
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