Add dataset card for NuSync
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by nielsr HF Staff - opened
README.md
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---
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task_categories:
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- robotics
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---
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# NuSync
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[**Project Page**](https://automot-website.github.io/) | [**Paper**](https://huggingface.co/papers/2603.14851) | [**GitHub**](https://github.com/OscarHuangWind/AutoMoT)
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NuSync is a dataset for end-to-end autonomous driving, introduced as part of the paper [AutoMoT: A Unified Vision-Language-Action Model with Asynchronous Mixture-of-Transformers for End-to-End Autonomous Driving](https://huggingface.co/papers/2603.14851).
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## Introduction
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AutoMoT is an end-to-end autonomous driving (AD) framework that unifies reasoning and action generation within a single vision-language-action (VLA) model. It leverages an asynchronous Mixture-of-Transformers (MoT) architecture with joint attention sharing, which preserves the general reasoning capabilities of pre-trained VLMs while enabling efficient fast-slow inference through execution at different task frequencies.
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The NuSync dataset is released alongside AutoMoT to support research in multi-task scene understanding and action-level tasks such as decision-making and trajectory planning in autonomous driving.
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## Citation
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If you find this dataset or the AutoMoT model useful, please cite the following:
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```bibtex
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@article{huang2026automot,
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title = {AutoMoT: A Unified Vision-Language-Action Model with Asynchronous Mixture-of-Transformers for End-to-End Autonomous Driving},
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author = {Wenhui Huang and Songyan Zhang and Qihang Huang and Zhidong Wang and Zhiqi Mao and Collister Chua and Zhan Chen and Long Chen and Chen Lv},
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journal = {arXiv preprint arXiv:2603.14851},
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year = {2026},
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url = {https://arxiv.org/abs/2603.14851}
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}
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@inproceedings{jia2024bench,
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title = {Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving},
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author = {Xiaosong Jia and Zhenjie Yang and Qifeng Li and Zhiyuan Zhang and Junchi Yan},
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booktitle = {NeurIPS 2024 Datasets and Benchmarks Track},
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year = {2024}
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}
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```
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