Single-frame vehicle trajectory prediction via neural ODE-based motion-state forecasting

Abstract

Accurate short-horizon vehicle trajectory prediction is critical for autonomous driving and vehicle-to-everything (V2X) communication. Most existing deep learning-based prediction methods rely on fixed-length historical trajectories and multi-agent context, which may be unavailable when a vehicle is newly observed. This paper studies single-frame short-horizon trajectory prediction at signalized intersections, where only one enriched observation frame is used for future motion-state forecasting. We propose a Neural Ordinary Differential Equation (Neural ODE)-based framework that formulates single-frame prediction as continuous-time motion-state forecasting. The proposed model encodes instantaneous kinematic variables, road-context information, and training-set-derived spatial priors into a latent representation, evolves the latent state in continuous time, and decodes future state variables. The predicted motion states are converted into future vehicle coordinates through kinematic integration. Experiments on the CitySIM-Intersection A dataset, evaluated by ADE and DE, show that the proposed method achieves competitive short-horizon prediction accuracy under the single-frame setting. The experiments include baseline comparisons with classical motion models and single-frame neural baselines, input ablation, coordinate and spatial-cell analyses, runtime evaluation, and supplementary studies on multi-frame sequence baselines, longer-horizon rollout, robustness, maneuver-specific performance, and statistical significance. The results clarify the applicability and limitations of single-frame short-horizon prediction on both straight and curved driving subsets.