A Single-Stage Spectrum-Domain Network for Trajectory Prediction

Abstract

Trajectory prediction is a fundamental yet challenging task in intelligent systems. Existing methods are mainly categorized as single-stage time-domain, two-stage time-domain, or two-stage spectrum-domain approaches, while single-stage spectrum-domain methods have been relatively underexplored. In the frequency domain, low-frequency components reflect global motion trends, while high-frequency components capture fine-grained local variations. Most existing spectrum-domain approaches process these components independently, overlooking their intrinsic complementarity. Inspired by the success of bilinear models in explicitly capturing cross-factor interactions, we propose S^{3}-Net, a single-stage spectrum-domain trajectory prediction network with a bilinear fusion module that integrates low- and high-frequency dynamics. This design yields richer spectral representations and enables accurate, socially compliant, and multimodal predictions. Experiments on the ETH-UCY and Stanford Drone Datasets demonstrate that S^{3}-Net achieves up to 16.8%/15.1% ADE/FDE reduction over spectrum-domain baselines while maintaining a compact model size and low inference latency, making it suitable for real-time scenarios.