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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},
}
``` |