LAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction

2026-06-25 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

LAMP (Lane-Aligned Motion Primitives) is a new topology-aware forecasting framework designed for autonomous driving systems to improve the safety and reliability of decision-making. Existing motion predictors often fail to ensure that lower-probability multimodal predictions adhere to lane topology, leading to physically and logically infeasible trajectories. LAMP addresses this by anchoring multimodal predictions to structured motion primitives that are aligned with lane topology. It employs a VQ-VAE to learn shape-aware motion primitives as discrete intention queries, capturing complex spatiotemporal patterns. Furthermore, LAMP introduces a feasibility-aware intention selector, trained with a lane-topology prior, to filter out unreachable intention queries. This guides the decoder to prioritize topology-consistent intentions while maintaining behavioral diversity. Experiments on the Argoverse 2 dataset show LAMP achieves prediction accuracy comparable to leading baselines, while significantly outperforming them in feasibility and diversity metrics.

Key takeaway

For autonomous driving system developers focused on motion forecasting, integrating topology-aware frameworks like LAMP is crucial. Your planning systems will benefit from predictions that not only minimize displacement errors but also strictly adhere to lane topology, significantly enhancing the feasibility and diversity of multimodal outputs. This approach improves the reliability of prediction sets, directly contributing to safer decision-making in complex scenarios.

Key insights

LAMP enhances autonomous driving motion forecasting by ensuring predictions adhere to lane topology, improving feasibility and diversity.

Principles

• Anchor multimodal predictions to lane-aligned motion primitives.
• Use VQ-VAE for learning shape-aware motion primitives.
• Filter intentions with a feasibility-aware selector and lane-topology prior.

Method

LAMP uses a VQ-VAE to learn shape-aware motion primitives as discrete intention queries. A feasibility-aware intention selector, trained with a lane-topology prior, filters unreachable queries, guiding the decoder for topology-consistent predictions while preserving diversity.

In practice

• Enhance safety in autonomous driving planning.
• Improve reliability of prediction sets.
• Generate diverse, yet feasible, trajectories.

Topics

• Motion Forecasting
• Autonomous Driving
• Trajectory Prediction
• Lane Topology
• VQ-VAE
• Argoverse 2

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.