Improve dataset card and add paper link

README.md

@@ -1,3 +1,50 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- robotics
+tags:
+- autonomous-driving
+- carla
+---
+
+# MVAdapt Dataset
+
+This repository contains the dataset for the paper [MVAdapt: Zero-Shot Multi-Vehicle Adaptation for End-to-End Autonomous Driving](https://huggingface.co/papers/2604.11854).
+
+MVAdapt is a physics-conditioned adaptation framework for multi-vehicle end-to-end (E2E) autonomous driving. The dataset includes driving data for 27 different vehicles in the CARLA simulator, covering a wide range of physical properties such as size, mass, and drivetrain characteristics.
+
+[GitHub Repository](https://github.com/hae-sung-oh/MVAdapt)
+
+## Dataset Description
+
+The dataset provides annotations and sensor data used to train and evaluate the MVAdapt model. It includes the following fields:
+
+- `vehicle_id`: Vehicle model ids
+- `gt_waypoint`: Ground truth waypoint for vehicle model
+- `bs_waypoint`: Predicted waypoint from baseline model for default vehicle model
+- `gt_control`: Ground truth control for vehicle model
+- `bs_control`: Predicted control from baseline model for default vehicle model
+- `scene_features`: Features that extracted by backbone model (TransFuser)
+- `physics_params`: Physical properties for vehicle model
+- `gear_params`: Gear properties for vehicle model
+- `rgb`: RGB image 
+- `lidar_bev`: LiDAR BEV image
+- `target_point`: Target heading point
+- `ego_vel`: Speed for ego vehicle
+- `command`: Command for ego vehicle
+
+## Citation
+
+If you find this work or dataset useful, please consider citing:
+
+```bibtex
+@misc{oh2026mvadaptzeroshotmultivehicleadaptation,
+      title={MVAdapt: Zero-Shot Multi-Vehicle Adaptation for End-to-End Autonomous Driving}, 
+      author={Haesung Oh and Jaeheung Park},
+      year={2026},
+      eprint={2604.11854},
+      archivePrefix={arXiv},
+      primaryClass={cs.RO},
+      url={https://arxiv.org/abs/2604.11854}, 
+}
+```