LAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction

2026-06-25

AI Summary

Existing multimodal trajectory prediction methods often neglect lane topology constraints, leading to the generation of infeasible trajectories under low-probability modes and thereby compromising autonomous driving safety. To address this issue, this work proposes the LAMP framework, which innovatively integrates lane topology into motion primitive learning. Specifically, a VQ-VAE is employed to construct shape-aware discrete intention representations, and a feasibility-aware intention selection mechanism is designed by incorporating lane priors. Trajectories are then generated via an attention-based decoder. Evaluated on Argoverse 2, the proposed method achieves state-of-the-art prediction accuracy while significantly improving both the physical feasibility and diversity of predicted trajectories.

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.

Problem

• trajectory prediction
• lane topology
• motion primitives
• feasibility
• autonomous driving

Innovation

• Lane-Aligned Motion Primitives
• VQ-VAE
• topology-aware forecasting
• feasibility-aware intention selection
• multimodal trajectory prediction