Can Vision Models Read the Radar Display? On the Feasibility of Radar Imagery for Air Traffic Complexity Estimation

2026-08-12

AI Summary

This study investigates whether radar imagery can serve as an effective input modality for deep vision models to estimate air traffic complexity. The traffic situation is encoded into a five-channel image incorporating positional, heading, speed, and altitude information. For the first time, a Vision Transformer is employed to perform regression on four intrinsic complexity components derived from geometric relationships among aircraft. Experimental results demonstrate that the proposed approach achieves R² values exceeding 0.96 across all complexity components. Perturbation analysis further reveals the model’s sensitivity to key aircraft, with its responses closely aligning with those aircraft’s actual contributions to overall complexity, thereby validating the efficacy of sparse, self-similar radar images for modeling air traffic complexity.

Abstract

Air traffic controllers perceive traffic complexity through the radar display, suggesting that a computer vision model operating on the same imagery may provide a natural architecture for modeling controller-perceived complexity; however, whether radar imagery is a viable input format for deep learning vision models remains unclear. Unlike natural images, radar images are extremely sparse and self-similar, consisting primarily of a black background and a few visually identical aircraft blobs, while small changes in aircraft positions can substantially alter sector-level complexity. To test whether a vision model can capture these operationally important differences, we encode each traffic situation as a position image supplemented by five channels representing aircraft state variables, including heading, speed, and altitude, and train a Vision Transformer (ViT) to regress four intrinsic complexity components derived from pairwise geometric relations among aircraft. The model achieves $R^2 > 0.96$ for all four components, and a one-aircraft-removal perturbation study shows that its response changes proportionally to how much the removed aircraft contributed to sector complexity rather than treating every removal as equivalent. These results demonstrate that, despite its atypical visual characteristics, radar imagery is a viable input format for air traffic complexity modeling.

Problem

• radar imagery
• air traffic complexity
• computer vision
• deep learning
• traffic situation awareness

Innovation

• radar imagery
• Vision Transformer
• air traffic complexity
• multi-channel encoding
• computer vision