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DESCENT applies transformer-based AI models to solve the specific problem of predicting aircraft trajectories on airport surfaces, addressing unique topological constraints distinct from standard autonomous driving tasks. By utilizing a Potential Reachable Set sampling mechanism, this research improves long-horizon forecasting accuracy for safety-critical ground operations, marking a significant step toward advanced automation in dense air traffic environments.
DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction
arXiv:2608.26002v1 Announce Type: new Abstract: 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.
Abstract: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.
IROS 2026. Project page at this https URL
Robotics (cs.RO)
arXiv:2608.26002 [cs.RO]
(or arXiv:2608.26002v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.26002 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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