Generalized Trajectory Scoring for End-to-end Multimodal Planning
Journal:
arXiv
Published Date:
Jun 7, 2025
Abstract
End-to-end multi-modal planning is a promising paradigm in autonomous
driving, enabling decision-making with diverse trajectory candidates. A key
component is a robust trajectory scorer capable of selecting the optimal
trajectory from these candidates. While recent trajectory scorers focus on
scoring either large sets of static trajectories or small sets of dynamically
generated ones, both approaches face significant limitations in generalization.
Static vocabularies provide effective coarse discretization but struggle to
make fine-grained adaptation, while dynamic proposals offer detailed precision
but fail to capture broader trajectory distributions. To overcome these
challenges, we propose GTRS (Generalized Trajectory Scoring), a unified
framework for end-to-end multi-modal planning that combines coarse and
fine-grained trajectory evaluation. GTRS consists of three complementary
innovations: (1) a diffusion-based trajectory generator that produces diverse
fine-grained proposals; (2) a vocabulary generalization technique that trains a
scorer on super-dense trajectory sets with dropout regularization, enabling its
robust inference on smaller subsets; and (3) a sensor augmentation strategy
that enhances out-of-domain generalization while incorporating refinement
training for critical trajectory discrimination. As the winning solution of the
Navsim v2 Challenge, GTRS demonstrates superior performance even with
sub-optimal sensor inputs, approaching privileged methods that rely on
ground-truth perception. Code will be available at
https://github.com/NVlabs/GTRS.
