README.md

metadata

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.

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

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:

@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}, 
}