Improving Operational Safety in High-Density Airspace

Operational safety in high-density airspace depends on the timely detection of trajectory conflicts, a task constrained by architectures that cannot represent direct statistical dependencies between observed trajectory deviations and structured flight plan intent. This paper presents a cross-modal kinematic-intent Transformer framework that projects both modalities into a shared embedding space and processes them jointly through multi-head self-attention, enabling the model to learn pairwise attention weights between any kinematic time step and any downstream waypoint constraint in a single operator pass. A Cholesky-parameterized probabilistic decoder produces calibrated bivariate Gaussian distributions over horizontal position and univariate Gaussian distributions over altitude for each of up to 1,800 future one-second steps, supporting analytically grounded separation violation probability computation without fixed geometric safety buffers. A normalized, bounded Conflict Risk Index aggregates peak loss-of-separation probability, temporal proximity, and geometric closing speed severity into a scalar with guaranteed support in [0, 1]. Evaluated on a held-out corpus of 4.2 million aircraft-hours across six Air Route Traffic Control Centers, the framework achieves a conflict detection rate of 94.7% at a false alert rate of 2.8% at the 20-minute horizon under default parameterization. Federated gradient aggregation with threshold secret sharing sustains prediction calibration across procedural distribution shifts without transmitting raw surveillance data between facilities.