Driving on Registers
Ellington Kirby, Alexandre Boulch, Yihong Xu, Yuan Yin, Gilles Puy, Éloi Zablocki, Andrei Bursuc, Spyros Gidaris
Abstract
We present DrivoR, a simple transformer-based architecture for end-to-end autonomous driving. Our approach builds on pretrained Vision Transformers (ViTs) and introduces camera-aware register tokens that compress multi-camera features into a compact scene representation, significantly reducing downstream computation without sacrificing accuracy. These tokens drive two lightweight transformer decoders that generate and then score candidate trajectories. The scoring decoder learns to mimic an oracle and predicts interpretable sub-scores e.g., safety or efficiency, enabling behavior-conditioned driving at inference. Despite its minimal design, DrivoR outperforms or matches strong baselines across NAVSIM-v1/v2, and closed-loop HUGSIM benchmarks. Our results show that a pure-transformer architecture, combined with targeted token compression, is sufficient for accurate, efficient, and adaptive end-to-end driving. Code and checkpoints are available via the project page.
BibTeX
@inproceedings{cvpr2026_drivingonregiste,
title = {Driving on Registers},
author = {Ellington Kirby and Alexandre Boulch and Yihong Xu and Yuan Yin and Gilles Puy and Éloi Zablocki and Andrei Bursuc and Spyros Gidaris and Renaud Marlet and Florent Bartoccioni and Anh-Quan Cao and Nermin Samet and Tuan-Hung VU and Matthieu Cord},
booktitle = {CVPR 2026},
year = {2026}
}