Interaction-Aware Neighbor Representation for Vehicle Trajectory Prediction

Accurate trajectory prediction in autonomous driving requires not only expressive model architectures but also informative representations of surrounding vehicles. However, raw kinematic features do not explicitly encode the structure or relative importance of inter-vehicle interactions. We propose a model-agnostic neighbor representation framework that abstracts raw kinematics into discrete longitudinal and lateral interaction states and integrates them into a unified importance score. Slot-semantic weighting and importance-based filtering further refine the representation according to driving context. Evaluated on real-world highway driving datasets across diverse single- and multi-agent prediction models, the proposed representation improves prediction accuracy across most evaluated backbones and metrics. It achieves up to a 28.47% reduction in ADE with negligible forward-pass runtime overhead on an embedded platform.