IMPACT: Behavioral Intention-Aware Multimodal Trajectory Prediction With Adaptive Context Trimming

Abstract

This paper presents a unified framework that jointly predicts behavioral intentions and vectorized occupancy, leveraging them as priors to dynamically prune context information during trajectory decoding, thereby enhancing prediction accuracy, interpretability, and efficiency. While most prior work has focused on boosting the precision of multimodal trajectory prediction, explicit modeling of behavioral intentions (e.g., yielding, overtaking) remains underexplored. To this end, we employ a shared context encoder for both intention and trajectory predictions, thereby reducing structural redundancy and information loss. Moreover, we address the lack of ground-truth behavioral intention labels in mainstream datasets (Waymo, Argoverse) by auto-labeling these datasets, thus advancing the community's efforts in this direction. We further introduce a vectorized occupancy prediction module that infers the probability of each map polyline being occupied by the target vehicle's future trajectory. By leveraging these intention and occupancy predictions priors, our method conducts dynamic, modality-dependent pruning of irrelevant agents and map polylines in the decoding stage, effectively reducing computational overhead and mitigating noise from non-critical elements. Our approach ranks first among LiDAR-free methods on the Waymo Motion Dataset and achieves SOTA performance on the Waymo Interactive Prediction Dataset. Remarkably, even without model ensembling, our single-model framework improves the softmAP by 10% compared to the previous SOTA method, BETOP, in Waymo Interactive Prediction Leaderboard. Furthermore, the proposed framework has been successfully deployed on real vehicles, demonstrating its practical effectiveness in real-world applications.