Trend-Enhanced Variate Transformer for Vessel Trajectory Prediction by Exploiting Short-Term Behavior Distribution Differences at Intersections

The automatic identification system (AIS) extends traditional measurement methods, enabling real-time situational awareness at intersections. However, most methods rely on extensive historical data from multiple intersections, which conflicts with the limited sensing range of onboard measurement. Moreover, state-of-the-art (SOTA) models suffer from error accumulation, high prediction uncertainty, and inadequate global variate modeling, limiting trend awareness and generalization across measurement scales. To overcome these, we propose iTentformer, an encoder-only model focusing on short-term vessel behavior at intersection waterways. Key innovations include: 1) a variate-centered encoder (VE) to capture distribution differences before intersections and 2) integration of the predicted course as a trend prior, highlighting pattern changes before intersections. These components are optimized through variate representation fusion and multitask learning. Extensive experiments show that iTentformer reduces average displacement error (ADE) by 35% and final displacement error (FDE) by 30% compared to SOTA Transformer-based models, improving trajectory abstraction and reducing estimation uncertainty through strong trend awareness. Notably, vessel navigational status at intersections significantly impacts measurement distribution. Additionally, its improved remaining useful life (RUL) prediction highlights its potential for broader measurement applications. The code is available at github.com/dengfa02/iTentformer.