| --- |
| license: apache-2.0 |
| viewer: false |
| tags: |
| - autonomous-driving |
| - carla |
| - benchmark |
| - end-to-end-driving |
| - personalized-driving |
| --- |
| |
|
|
| # Person2Drive |
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| **Person2Drive** is the dataset and benchmark repository for the ECCV 2026 paper: |
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| **Driving like yourself: A Benchmark for Closed-Loop Personalized End-to-End Autonomous Driving** |
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| ## Overview |
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| Person2Drive is a CARLA-based benchmark for studying personalized end-to-end autonomous driving. It contains human driving records collected in closed-loop simulation environments and is organized at the driver level. |
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| The goal of the benchmark is to support research on human driving style modeling, driver-level personalization, route-level generalization, and closed-loop evaluation of end-to-end driving models. Unlike conventional driving datasets that mainly capture generic driving behavior, Person2Drive provides multiple driving records from anonymized human drivers under shared or comparable simulation settings. |
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| ## Release Status |
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| This Hugging Face repository is the stable public access page for Person2Drive. |
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| The dataset is being uploaded and organized progressively. Files already available in this repository are part of the public release. The current upload status is summarized in [`RELEASE_MANIFEST.md`](RELEASE_MANIFEST.md). |
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| Additional metadata, benchmark documentation, and usage instructions will be added to this repository as the release is finalized for the ECCV 2026 camera-ready version. |
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| ## Dataset Statistics |
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| The full Person2Drive release contains: |
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| * 50 anonymized human drivers; |
| * 4 driving routes in closed-loop CARLA environments; |
| * driver-level folders under `drivers/`; |
| * route-level compressed archives for each driver; |
| * metadata files for driver-level and map-level organization. |
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| Each `driverXX/` folder corresponds to one anonymized human driver. Route-level data are stored as `.tar.zst` archives. |
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| ## Dataset Contents |
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| The released data include the information required for personalized end-to-end driving research, including: |
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| * ego-vehicle states; |
| * control signals such as steering, throttle, and brake; |
| * ego trajectories; |
| * route and navigation information; |
| * selected sensor data used by end-to-end driving models; |
| * metadata for driver-level and route-level organization. |
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| The dataset can be used for both open-loop behavior analysis and closed-loop personalized driving evaluation. |
|
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| ## Repository Structure |
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| The repository is organized as follows: |
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| ```text |
| Person2Drive/ |
| ├── drivers/ |
| │ ├── driver01/ |
| │ │ ├── Town04_drive_1.tar.zst |
| │ │ ├── ... |
| │ │ ├── Town04_drive_8.tar.zst |
| │ │ ├── Town05_drive_1.tar.zst |
| │ │ ├── ... |
| │ │ ├── Town05_drive_8.tar.zst |
| │ │ └── b2d_infos_train.pkl |
| │ ├── driver02/ |
| │ │ ├── Town04_drive_1.tar.zst |
| │ │ ├── ... |
| │ │ ├── Town05_drive_8.tar.zst |
| │ │ └── b2d_infos_train.pkl |
| │ └── ... |
| ├── b2d_map_infos.pkl |
| ├── RELEASE_MANIFEST.md |
| ├── DATASET_DETAILS.md |
| └── README.md |
| ``` |
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| The file `b2d_infos_train.pkl` stores metadata associated with the corresponding driver data. The file `b2d_map_infos.pkl` contains map-level metadata used by the benchmark and evaluation pipeline. |
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| Detailed data organization and field descriptions are provided in [`DATASET_DETAILS.md`](DATASET_DETAILS.md). |
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| ## Download and Extraction |
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| The released data are stored as `.tar.zst` archives. Each archive corresponds to a route-level driving record package for one anonymized driver. |
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| To clone the repository with Git LFS: |
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| ```bash |
| git lfs install |
| git clone https://huggingface.co/datasets/dongxr7/Person2Drive |
| ``` |
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| To extract an archive on Linux: |
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| ```bash |
| tar -I zstd -xvf Town04_drive_1.tar.zst |
| ``` |
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| Alternatively, extraction can be performed in two steps: |
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| ```bash |
| zstd -d Town04_drive_1.tar.zst |
| tar -xvf Town04_drive_1.tar |
| ``` |
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| On Windows, `.tar.zst` archives can be extracted with archive tools that support Zstandard compression, such as 7-Zip, PeaZip, or Bandizip. |
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| ## Benchmark Usage |
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| Person2Drive is intended for evaluating whether an end-to-end driving model can adapt to individual human driving styles while maintaining safe closed-loop performance. |
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| Typical evaluation settings include: |
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| * driver-level personalization; |
| * route-level generalization; |
| * trajectory prediction quality; |
| * driving style similarity; |
| * closed-loop driving performance. |
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| Benchmark scripts, evaluation protocols, and additional usage instructions will be released in this repository. |
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| ## Privacy and Anonymization |
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| Driver identities are anonymized as `driverXX`. The dataset does not use real driver names in the public release. |
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| ## Citation |
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| Citation information will be added after the ECCV 2026 camera-ready version is finalized. |
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| ## Contact |
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| For questions about the dataset, please contact Xiaoru Dong at [xrdong@cs.hku.hk](mailto:xrdong@cs.hku.hk). |
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