LAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction

The Gap Between Accuracy and Feasibility in Motion Prediction

A new preprint from arXiv (2606.26661v1) introduces LAMP—Lane-Aligned Motion Primitives—a trajectory prediction method that prioritizes feasibility over raw displacement error. The core insight is that many state-of-the-art predictors achieve low average displacement error (ADE) and final displacement error (FDE) by generating trajectories that, while numerically close to ground truth, violate basic kinematic or road geometry constraints. LAMP addresses this by explicitly anchoring predictions to lane structures and using motion primitives that respect vehicle dynamics.

Why This Matters

The autonomous driving industry has long grappled with a disconnect between prediction metrics and real-world deployability. A model that scores well on standard benchmarks may still produce trajectories that cut across lanes, exceed acceleration limits, or ignore traffic rules—errors that can be catastrophic when fed into a planning module. LAMP’s approach directly tackles this by:

• Enforcing lane alignment as a structural prior rather than a soft penalty
• Using motion primitives (e.g., constant velocity, lane change, turn) that are physically realizable
• Reducing the search space to only feasible maneuvers, which also improves inference speed

This shift from “what trajectory is closest to ground truth” to “what trajectory is both accurate and drivable” represents a maturing of the field. It acknowledges that autonomous systems operate under hard constraints—tires have friction limits, steering has mechanical bounds, and roads have geometry.

Implications for AI Practitioners

For engineers building prediction systems, LAMP suggests several practical lessons:

• Metric design matters more than model architecture. If your evaluation rewards numerical closeness but ignores feasibility, your model will optimize for the wrong objective. Practitioners should consider adding constraint-violation rates or kinematic feasibility scores to their validation pipelines.

• Explicit structure beats implicit learning for safety-critical constraints. While end-to-end learning can implicitly capture lane geometry from data, LAMP’s explicit lane-aligned primitives guarantee constraint satisfaction—a crucial property when edge cases are rare but dangerous.

• Inference efficiency gains are a bonus. By limiting predictions to a discrete set of feasible primitives, LAMP likely reduces computational overhead compared to dense trajectory sampling methods. This matters for real-time deployment on resource-constrained vehicle hardware.

• The gap between research benchmarks and production remains wide. Standard datasets like nuScenes or Waymo Open Motion Dataset evaluate on displacement errors, but production systems need additional layers of filtering. LAMP-type approaches may become a prerequisite for safe deployment.

Key Takeaways

• LAMP addresses a critical blind spot in motion forecasting: generating trajectories that are numerically accurate but physically infeasible
• By using lane-aligned motion primitives, the method enforces kinematic and road geometry constraints by design, not by post-hoc filtering
• For AI practitioners, this work underscores the importance of aligning evaluation metrics with real-world safety requirements
• The approach suggests that explicit structure (motion primitives) can outperform purely learned representations in safety-critical autonomous driving applications