DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction

Alexander Prutsch; David Schinagl; Horst Possegger

cs.ROAug 26, 2026

DESCENT: Directed Edge Scene Encoding for Airport Surface Movement Prediction

Authors: Alexander Prutsch, David Schinagl, Horst Possegger

Organizations: Institute of Visual Computing, Graz University of Technology, Austria

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.

Figures & tables

Fig. 1: We illustrate two aircraft at Logan International Airport (KBOS) with future movements indicated by colored paths. Runways are shown in dark gray and taxiways in light gray. Standard map-sampling strategies – such as top- $k$ nearest elements or fixed circular regions of interest – fail to account for the heterogeneous dynamics of airport operations. While effective in autonomous driving, a fixed region in the aviation domain may either capture insufficient context for high-speed maneuvers (dark circles) or include an excessive number of irrelevant segments (bright circles), hindering context learning and increasing computational overhead.

Fig. 2: Overview of our DESCENT architecture. We employ a potential reachable set-based airfield map sampling to obtain the map context based on the current focal agent position. Then, all agent observations

O

and airfield segments

S

are encoded using individual encoders

f_{M}

and

f_{A}

. Positional

P_{\text{emb}}

and type

T_{\text{emb}}

embeddings are added to the tokens, to form a scene context

C

. The scene context is encoded using a scene encoder

f_{S}

, lastly a decoder using learnable queries

Q

create output trajectories

T

and probability scores

P

Panel of Figure 3 — Fig. 3: Illustration of our Potential Reachable Set-based context sampling (on the KSFO airport). The process begins by locating the agent’s current airfield segment (top left), followed by a graph traversal to identify reachable pathways (bottom left). We generate a sparse context (right) comprising local segments and distant but reachable long-term structures. This selection reflects the agent’s operational constraints, prioritizing high-velocity paths like runways over distant, unreachable taxiways.

Panel of Figure 4 — Fig. 4: Comparison of random vs. critical focal agent sampling in the Amelia-10 benchmark. The top row displays the distribution of future trajectory endpoints, highlighting a more diverse distribution for critical agents. The bottom row illustrates the relationship between current agent speed and future trajectory length. Notably, critical agents show higher dynamic changes: agents with low initial speed but long future trajectories (acceleration) and high initial speed with short future paths (braking)–compared to the more linear correlation seen in random agents. Best viewed digitally with zoom.

Method	KMDW	KEWR	KBOS	KSFO*	KSEA*	KDCA	PANC	KLAX	KMSY	KJFK	Avg †	Metric
Amelia-TF [ 17 ]	66.69	91.18	81.61	-	-	105.60	170.48	137.40	87.46	102.77	105.40	mFDE@50s
DESCENT (Ours)	59.64	77.74	64.49	56.60	61.45	0 54.04	0 67.13	0 64.50	0 68.98	0 78.67	0 66.90	mFDE@50s
Amelia-TF [ 17 ]	26.41	32.88	30.79	-	-	0 39.24	0 61.35	0 50.74	0 31.41	0 39.23	0 39.01	mADE@50s
DESCENT (Ours)	24.88	31.69	26.80	23.22	24.79	0 25.46	0 28.41	0 27.43	0 27.39	0 33.56	0 28.20	mADE@50s
Amelia-TF [ 17 ]	13.35	15.78	14.74	-	-	0 20.20	0 28.08	0 25.79	0 14.65	0 19.82	0 19.05	mFDE@20s
DESCENT (Ours)	13.60	17.20	14.25	12.40	12.68	0 15.76	0 15.44	0 15.21	0 14.88	0 19.29	0 15.70	mFDE@20s

TABLE I: Trajectory prediction results on the Amelia-10-bench test set for forecasting horizons of 50 s and 20 s. Experiments are done on the test sets, evaluating on the most critical agent per scenario. Errors reported in meters.

Method	KMDW	KEWR	KBOS	KSFO*	KSEA*	KDCA	PANC	KLAX	KMSY	KJFK	Avg	Metric
Amelia-TF [ 17 ]	27.52	52.06	51.01	40.23	65.82	47.75	86.24	89.46	29.84	55.32	54.52	mFDE@50s
DESCENT (Ours)	25.23	48.80	42.23	37.11	39.56	27.92	44.14	45.17	24.63	46.27	38.11	mFDE@50s
Amelia-TF [ 17 ]	11.80	20.96	20.47	17.05	29.94	18.93	34.90	35.84	11.42	22.43	22.37	mADE@50s
DESCENT (Ours)	11.37	21.34	18.35	15.81	17.51	13.21	20.34	20.24	10.38	20.51	16.91	mADE@50s

TABLE II: Trajectory prediction results on the Amelia-10-bench test set for a forecasting horizon of 50 s. Experiments are done on the test sets, evaluating a random agent per scenario. Errors reported in meters.

Method	KMDW	KEWR	KBOS	KSFO	KSEA	KDCA	PANC	KLAX	KMSY	KJFK	Avg	Metric
DESCENT (Ours)	29.73	50.37	47.94	44.23	46.50	29.99	53.46	51.08	23.31	41.48	41.81	mFDE@50s
DESCENT (Ours)	13.01	21.57	20.39	18.83	20.35	14.01	24.08	22.75	10.10	17.85	18.28	mADE@50s

TABLE III: Multi-airport trajectory prediction results on the Amelia-10-bench test set for a forecasting horizon of 50 s. Experiments are done on the test sets, evaluating a random agent per scenario. Errors reported in meters.

Panel of Figure 8 — Fig. 5: We visualize $50\,\text{s}$ DESCENT predictions (colored lines) alongside the $10\,\text{s}$ historical focal agent movement (black), surrounding agent histories (light gray), the ground truth future trajectory (thick, dark gray), and airfield map context. For clarity, predictions with confidence scores below 0.15 are omitted. The scenarios are sampled from the Amelia-10 test set showing scenes from KBOS. Best viewed digitally with zoom.

Scene Context Sampling	KBOS	KSFO	PANC	Avg
Top-K closest (50 segments)	129.82	122.75	169.92	140.83
Radius-based (0.3 km)	0 86.95	0 80.44	107.69	0 91.70
PRS-based	0 64.49	0 56.60	0 67.13	0 62.74

TABLE IV: Ablation study on scene context sampling methods. We report the

mFDE@50s

on the respective test sets using the most critical agent per scenario. Errors reported in meters.

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ASCENT: Transformer-Based Aircraft Trajectory Prediction in Non-Towered Terminal Airspace

Alexander Prutsch, David Schinagl, Horst Possegger

Institute of Visual Computing, Graz University of Technology, Austria

Trajectory Prediction Multimodal Trajectory Prediction Air Traffic Management Transformer

Open paper AskPDF arXiv

Accurate trajectory prediction can improve General Aviation safety in non-towered terminal airspace, where high traffic density increases accident risk. We present ASCENT, a lightweight transformer-based model for multi-modal 3D aircraft trajectory forecasting, which integrates domain-aware 3D coordinate normalization and parameterized predictions. ASCENT employs a transformer-based motion encoder and a query-based decoder, enabling the generation of diverse maneuver hypotheses with low latency. Experiments on the TrajAir and TartanAviation datasets demonstrate that our model outperforms prior baselines, as the encoder effectively captures motion dynamics and the decoder aligns with structured aircraft traffic patterns. Furthermore, ablation studies confirm the contributions of the decoder design, coordinate-frame modeling, and parameterized outputs. These results establish ASCENT as an effective approach for real-time aircraft trajectory prediction in non-towered terminal airspace.

Abstract

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