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arXiv · 2606.26661

LAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction

Sangjin Han·Hoseong Jung·Jeongtae Her·Changhyun Choi·H. Jin Kim
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Abstract

Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios. While existing predictors excel at minimizing standard displacement errors, they often overlook the adherence to lane topology of multimodal predictions, particularly for lower-probability modes. Consequently, predicted trajectories may violate physical and logical constraints, making the prediction set unreliable for safety-critical planning. In this paper, we propose LAMP (Lane-Aligned Motion Primitives), a topology-aware forecasting framework that anchors multimodal prediction to structured motion primitives aligned with lane topology. Specifically, we use a VQ-VAE to learn shape-aware motion primitives as discrete intention queries, capturing spatiotemporal patterns beyond endpoint-based intentions. We further introduce a feasibility-aware intention selector trained with a lane-topology prior for filtering unreachable intention queries, guiding the decoder to prioritize topology-consistent intentions while preserving behavioral diversity. Extensive experiments on the Argoverse 2 dataset demonstrate that LAMP achieves prediction accuracy comparable to state-of-the-art baselines while outperforming them in feasibility and diversity metrics.

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Sangjin Han, Hoseong Jung, Jeongtae Her, Changhyun Choi, H. Jin Kim. 2026-06-25. LAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction. https://arxiv.org/abs/2606.26661

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