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

Abstract

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.

Figures (4)

• Figure 1: Comparison between a CVAE-based decoder, widely applied in aircraft trajectory prediction, and our ASCENT model employing a query-based decoder. The scenario shows an aircraft take-off: our model, leveraging learnable mode queries, predicts a diverse set of trajectories that also capture the actual aircraft movement, while the baseline only predicts the common traffic pattern at the data-collection airport.
• Figure 2: Overview of our ASCENT architecture. For clarity of presentation, visualizations are reduced to 2D. First, we normalize the historical 3D flight path with respect to the current aircraft pose. Next, we encode this local history using attention blocks and combine it with global context through positional embeddings. To generate multi-modal future trajectories, we employ learnable mode queries that capture potential actions, such as landing or turning.
• Figure 3: Comparison of different coordinate system normalization approaches on trajectories from the TrajAir dataset patrikar2022trajair. We show the current aircraft position (circular markers), along with historical (11 s) and future (120 s) trajectories. We compare global coordinates (left), object-centered normalization (middle), and our combined positional–angular normalization (right). For each method, the bird’s-eye view is shown on top, with vertical trajectories below. The global coordinate visualization shows aircraft approaching and departing from the runway, which is aligned with the x-axis. Normalizing with respect to the aircraft position moves all centers to the origin, while angular normalization aligns orientations, producing structured clusters of similar maneuvers, such as left and right turns. The top-right plot highlights a higher frequency of left turns, reflecting the left-traffic pattern at the KBTP airport.
• Figure 4: Qualitative results on scenarios from the TrajAir patrikar2022trajair111Days validation set. Visualizations show 11 s of historical aircraft movement (black), 120 s of ground truth future (gray) and predictions from our model (colored). For clearer visualization we exclude predictions with scores below 0.1.