DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning
Journal:
arXiv
Published Date:
Jun 7, 2025
Abstract
In complex driving environments, autonomous vehicles must navigate safely.
Relying on a single predicted path, as in regression-based approaches, usually
does not explicitly assess the safety of the predicted trajectory.
Selection-based methods address this by generating and scoring multiple
trajectory candidates and predicting the safety score for each, but face
optimization challenges in precisely selecting the best option from thousands
of possibilities and distinguishing subtle but safety-critical differences,
especially in rare or underrepresented scenarios. We propose DriveSuprim to
overcome these challenges and advance the selection-based paradigm through a
coarse-to-fine paradigm for progressive candidate filtering, a rotation-based
augmentation method to improve robustness in out-of-distribution scenarios, and
a self-distillation framework to stabilize training. DriveSuprim achieves
state-of-the-art performance, reaching 93.5% PDMS in NAVSIM v1 and 87.1% EPDMS
in NAVSIM v2 without extra data, demonstrating superior safetycritical
capabilities, including collision avoidance and compliance with rules, while
maintaining high trajectory quality in various driving scenarios.
