发表机构
Graz University of Technology; Institute of Visual Computing（格拉茨工业大学; 视觉计算研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对机场地面运动预测领域，提出基于Transformer的DESCENT架构，结合PRS上下文采样与检测Transformer解码器，在Amelia-10基准上较现有最优基线实现显著性能提升，尤其在安全关键场景表现突出。
AI 中文摘要
在商业空中交通密度不断增加的背景下，高级自动化是提升地面运行安全性的关键技术。运动预测是自动驾驶领域已深入研究的任务，但其在机场地面运动中的应用仍未得到充分探索。为实现该领域高效且准确的预测，我们提出DESCENT，一种基于Transformer的架构，旨在处理异构动态性与严格拓扑约束。该方法具备潜在可达集（PRS）上下文采样机制，可在不同运行阶段自适应收集机场环境上下文；结合基于检测Transformer的解码器，DESCENT生成准确的轨迹预测。在Amelia-10基准上的大量评估表明，其性能较现有最优基线有显著提升，在安全关键场景中增益尤为突出，其领域感知采样提供了安全导航所需的关键长程上下文。
英文摘要
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.
CommentsIROS 2026. Project page at https://a-pru.github.io/descent
