# Driving on Registers

arXiv: https://arxiv.org/abs/2601.05083 · HTML: https://mlsanity.com/paper/2601.05083/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving.
- Builds on 55 of its 62 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Placed in

- End-to-end autonomous driving / [End-to-end system architecture](https://mlsanity.com/driving/end-to-end-system-architecture/)

## Builds on (most cited first, 30 of 55)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [Token Merging: Your ViT But Faster](https://mlsanity.com/paper/2210.09461/) (2022)
- [Vision Transformers Need Registers](https://mlsanity.com/paper/2309.16588/) (2023)
- [Big Transfer (BiT): General Visual Representation Learning](https://mlsanity.com/paper/1912.11370/) (2019)
- [EVA: Exploring the Limits of Masked Visual Representation Learning at Scale](https://mlsanity.com/paper/2211.07636/) (2022)
- [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://mlsanity.com/paper/2107.14795/) (2021)
- [VAD: Vectorized Scene Representation for Efficient Autonomous Driving](https://mlsanity.com/paper/2303.12077/) (2023)
- [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021)
- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021)
- [TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2205.15997/) (2022)
- [NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking](https://mlsanity.com/paper/2406.15349/) (2024)
- [DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving](https://mlsanity.com/paper/2411.15139/) (2024)
- [An Image is Worth 32 Tokens for Reconstruction and Generation](https://mlsanity.com/paper/2406.07550/) (2024)
- [VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning](https://mlsanity.com/paper/2402.13243/) (2024)
- [AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning](https://mlsanity.com/paper/2506.13757/) (2025)
- [Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation](https://mlsanity.com/paper/2406.06978/) (2024)
- [Parting with Misconceptions about Learning-based Vehicle Motion Planning](https://mlsanity.com/paper/2306.07962/) (2023)
- [ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.08052/) (2025)
- [PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving](https://mlsanity.com/paper/2112.12610/) (2021)
- [An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection](https://mlsanity.com/paper/1904.09730/) (2019)
- [End-to-End Driving with Online Trajectory Evaluation via BEV World Model](https://mlsanity.com/paper/2504.01941/) (2025)
- [Pseudo-Simulation for Autonomous Driving](https://mlsanity.com/paper/2506.04218/) (2025)
- [Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail](https://mlsanity.com/paper/2511.00088/) (2025)
- [Unified Vision-Language-Action Model](https://mlsanity.com/paper/2506.19850/) (2025)

## Built on by (most cited first, 30 of 44)

- [Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving](https://mlsanity.com/paper/2601.22032/) (2026)
- [DriveFine: Refining-Augmented Masked Diffusion VLA for Precise and Robust Driving](https://mlsanity.com/paper/2602.14577/) (2026)
- [DriveFuture: Future-Aware Latent World Models for Autonomous Driving](https://mlsanity.com/paper/2605.09701/) (2026)
- [EponaV2: Driving World Model with Comprehensive Future Reasoning](https://mlsanity.com/paper/2605.14696/) (2026)
- [CLOVER: Closed-Loop Value Estimation and Ranking for End-to-End Autonomous Driving Planning](https://mlsanity.com/paper/2605.15120/) (2026)
- [Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation](https://mlsanity.com/paper/2606.15869/) (2026)
- [BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Driving](https://mlsanity.com/paper/2604.10856/) (2026)
- [Test-Time Trajectory Optimization for Autonomous Driving](https://mlsanity.com/paper/2606.07170/) (2026)
- [MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems](https://mlsanity.com/paper/2605.10904/) (2026)
- [DA-WAM: Decision-Aligned Future Latents for Driving World Models](https://mlsanity.com/paper/2608.19085/) (2026)
- [ChainFlow-VLA: Causal Flow Planning with Vision-Language Models](https://mlsanity.com/paper/2605.23270/) (2026)
- [Scaling Self-Play for End-to-End Driving](https://mlsanity.com/paper/2606.19641/) (2026)
- [DriveZero: End-to-End Driving Beyond Human Demonstrations](https://mlsanity.com/paper/2609.06055/) (2026)
- [NTR: Neural Token Reconstruction for Scene Token Bottleneck in End-to-End Driving](https://mlsanity.com/paper/2605.31116/) (2026)
- [CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving](https://mlsanity.com/paper/2606.06219/) (2026)
- [DriveReward: A Comprehensive Dataset and Generative Vision-Language Reward Model for Autonomous Driving](https://mlsanity.com/paper/2606.08525/) (2026)
- [PriorEye: Geospatial Visual Priors for End-to-End Autonomous Driving](https://mlsanity.com/paper/2606.31830/) (2026)
- [Pictura: Perspective-View Self-Play at Scale for Driving](https://mlsanity.com/paper/2607.26005/) (2026)
- [From Representational Complementarity to Dual Systems: Synergizing VLM and Vision-Only Backbones for End-to-End Driving](https://mlsanity.com/paper/2602.10719/) (2026)
- [FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning](https://mlsanity.com/paper/2606.24231/) (2026)
- [DriveVer: Lightweight Trajectory Evaluator as Test-Time Verifier for Autonomous Driving](https://mlsanity.com/paper/2607.00399/) (2026)
- [Teaching Vision-Language-Action Models What to See and Where to Look](https://mlsanity.com/paper/2607.01658/) (2026)
- [Large Discrete Policy: Advancing Explicit Behavior Modeling with Stochastic Iterative Scoring](https://mlsanity.com/paper/2609.07049/) (2026)
- [SimVTA: Simulation-Grounded Video-Trajectory Alignment for Driving World Action Models](https://mlsanity.com/paper/2610.13781/) (2026)
- [Threat-guided Policy-aware Scene Perturbation for Safe Autonomous Driving with Online Reinforcement Learning](https://mlsanity.com/paper/2608.10403/) (2026)
- [Plug-and-Play Traffic Element Awareness for End-to-End Autonomous Driving](https://mlsanity.com/paper/2608.18035/) (2026)
- [Planning-Oriented End-to-End Autonomous Driving: Architectures, Evaluation, and Emerging Paradigms](https://mlsanity.com/paper/2608.20111/) (2026)
- [SkyDrive: Learning to Drive in a New City from Aerial Traffic Monitoring](https://mlsanity.com/paper/2608.25142/) (2026)
- [Driving on Memory](https://mlsanity.com/paper/2608.31029/) (2026)
- [Designing Versatile Samples for Learned Trajectory Scoring](https://mlsanity.com/paper/2609.01799/) (2026)

## Abstract (arXiv)

We present DrivoR, a simple and efficient 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 representing aspects such as safety, comfort, and efficiency, enabling behavior-conditioned driving at inference. Despite its minimal design, DrivoR outperforms or matches strong contemporary baselines across NAVSIM-v1, NAVSIM-v2, and the photorealistic closed-loop HUGSIM benchmark. 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 will be made available via the project page.