DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning

TL;DR

DriveSuprim introduces a novel selection-based trajectory planning method for autonomous vehicles, employing a coarse-to-fine filtering, rotation augmentation, and self-distillation to enhance safety and robustness in complex scenarios.

Contribution

It presents a new framework that improves trajectory selection accuracy and robustness, achieving state-of-the-art results without extra data in autonomous driving benchmarks.

Findings

Achieves 93.5% PDMS in NAVSIM v1

Attains 87.1% EPDMS in NAVSIM v2

Secures 83.02 Driving Score and 60.00 Success Rate on Bench2Drive

Abstract

Autonomous vehicles must navigate safely in complex driving environments. Imitating a single expert trajectory, 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. However, they face optimization challenges in precisely selecting the best option from thousands of candidates and distinguishing subtle but safety-critical differences, especially in rare and challenging 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, with 83.02 Driving Score and 60.00 Success Rate on the Bench2Drive benchmark, demonstrating superior planning capabilities in various driving scenarios.