Necessity of Dynamic Trajectory Generation vs. Sufficiently Dense Static Vocabularies

Determine whether dynamic trajectory generation methods for end-to-end autonomous driving planning are fundamentally necessary for high-performance planning, or whether static trajectory vocabularies—when scaled to be sufficiently dense to cover the action space and paired with effective scoring—can achieve comparable performance.

Background

End-to-end autonomous driving planners often adopt multi-modal planning that scores candidate trajectories, with two main paradigms: scoring a static trajectory vocabulary or generating dynamic proposals. Dynamic generation methods (e.g., regression- or diffusion-based) have shown strong empirical performance but introduce additional complexity.

The paper investigates whether dynamic proposal generation is inherently required, or if a sufficiently dense static trajectory vocabulary, combined with an efficient scoring mechanism, can already achieve comparable outcomes. This uncertainty motivates their scaling study and the design of a factorized trajectory vocabulary and scalable scoring in SparseDriveV2.