Kinematics-Constrained Transformer with Spatiotemporal Repulsive Force for Vehicle Trajectory Prediction

Accurate vehicle trajectory prediction remains critical for autonomous driving systems. However, accurately representing interaction-rich and time-varying behaviors is still challenging for many existing predictors, often resulting in reduced predictive accuracy. To address these limitations, we propose a kinematics-constrained Transformer with spatiotemporal repulsive force. First, we model interactions between the target vehicle and its surroundings using a spatiotemporal repulsive force model, enhancing the network’s environmental perception. Next, a Transformer encoder extracts temporal features from the observed motion sequence. A ST-RF attention module captures interaction dynamics, while an adaptive gating mechanism fuses these cues with the encoded features. The Transformer decoder outputs a sequence of control commands, which are integrated through a vehicle kinematics network layers to generate continuous, physically grounded trajectories. Furthermore, we introduce a multi-objective physical constraint loss function enforcing kinematic and dynamic constraints across all predicted agents. Extensive evaluations on the HighD and NGSIM datasets demonstrate that our model achieves significant reductions in RMSE across all prediction horizons, with average decreases of 1.63% and 7.69%, respectively. Additionally, in diverse challenging traffic scenarios, our approach exhibits exceptional robustness, producing trajectories that are both physically plausible and readily interpretable.