Update README.md

README.md

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 **Driving like yourself: A Benchmark for Closed-Loop Personalized End-to-End Autonomous Driving**
 
+## Overview
+
+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.
+
+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.
+
+## Release Status
+
+This Hugging Face repository is the stable public access page for Person2Drive.
+
+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).
+
+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.
+
+## Dataset Statistics
+
+The full Person2Drive release contains:
+
+* 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.
+
+Each `driverXX/` folder corresponds to one anonymized human driver. Route-level data are stored as `.tar.zst` archives.
+
+## Dataset Contents
+
+The released data include the information required for personalized end-to-end driving research, including:
+
+* 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.
+
+The dataset can be used for both open-loop behavior analysis and closed-loop personalized driving evaluation.
+
+## Repository Structure
+
+The repository is organized as follows:
+
+```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
+```
+
+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.
+
+Please refer to [`RELEASE_MANIFEST.md`](RELEASE_MANIFEST.md) for the current upload status and [`DATASET_DETAILS.md`](DATASET_DETAILS.md) for detailed data organization.
+
+## Download and Extraction
+
+The released data are stored as `.tar.zst` archives. Each archive corresponds to a route-level driving record package for one anonymized driver.
+
+To clone the repository with Git LFS:
+
+```bash
+git lfs install
+git clone https://huggingface.co/datasets/dongxr7/Person2Drive
+```
+
+To extract an archive on Linux:
+
+```bash
+tar -I zstd -xvf Town04_drive_1.tar.zst
+```
+
+Alternatively, extraction can be performed in two steps:
+
+```bash
+zstd -d Town04_drive_1.tar.zst
+tar -xvf Town04_drive_1.tar
+```
+
+On Windows, `.tar.zst` archives can be extracted with archive tools that support Zstandard compression, such as 7-Zip, PeaZip, or Bandizip.
+
+## Benchmark Usage
+
+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.
+
+Typical evaluation settings include:
+
+* driver-level personalization;
+* route-level generalization;
+* trajectory prediction quality;
+* driving style similarity;
+* closed-loop driving performance.
+
+Benchmark scripts, evaluation protocols, and additional usage instructions will be released in this repository.
+
+## Privacy and Anonymization
+
+Driver identities are anonymized as `driverXX`. The dataset does not use real driver names in the public release.
+
+## Citation
+
+Citation information will be added after the ECCV 2026 camera-ready version is finalized.
+
+## Contact
+
+For questions about the dataset, please contact Xiaoru Dong at [xrdong@cs.hku.hk](mailto:xrdong@cs.hku.hk).
+
+