2026-08-26 · IROS 2026

DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction

Alexander Prutsch, David Schinagl, Horst Possegger

Navigation & mobility Safety & evaluation

Published at IROS 2026.

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.

arXiv comment: IROS 2026. Project page at https://a-pru.github.io/descent

Ten-minute slide kit

Six slides is the whole talk: what was broken, what people tried, what these authors did, what the numbers say, where it falls over, and the sentence people should remember.

SLIDE 1

The problem

The continuous growth of commercial air traffic has led to a substantial increase in airport surface movements . This rise has resulted in higher runway occupancy rates and increased complexity in traffic management. Consequently, the frequency of critical situations such as runway incursions , has increased, posing significant safety concerns ,

SLIDE 2

What came before

Not recoverable from the parsed text. Read this section in the paper yourself.

SLIDE 3

The method

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SLIDE 4

What they measured

We report results for single-airport experiments, assessing performance on both safety-critical agents and randomly selected focal agents. As a baseline, we re-run the Amelia-TF model using the official codebase and pretrained weights<sup>2</sup> , since the framework has undergone several refactoring and the currently reproducible results differ from those reported in the original paper. This ensures a fair comparison on identical data splits. In addition to the single-airport setting, we provide a cross-airport…

SLIDE 5

Where it breaks

Not recoverable from the parsed text. Read this section in the paper yourself.

SLIDE 6

One-line takeaway

We present DESCENT, a trajectory prediction architecture that adapts advancements in autonomous driving to the unique requirements of airport surface operations. The core of our approach is a novel PRS-based scene context sampling mechanism, which enables the model to effectively process heterogeneous airfield map context across the varying spatial scales of

Assembled from the paper's own PDF, parsed with its layout intact so tables and equations survive, 46,216 characters of it, then split on the paper's own section headings. Extractive, not generated: every sentence here is lifted from the paper. Check it before you present it.

DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction

Abstract

Ten-minute slide kit

The problem

What came before

The method

What they measured

Where it breaks

One-line takeaway

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