license: mit
language:
- en
tags:
- autonomous-driving
- teleoperation
- robotics
- carla
- world-model
- video-prediction
- predictive-display
- future-action-prediction
- imitation-learning
- mile
TeleopWM-Dataset
TeleopWM-Dataset is a large-scale collection of CARLA driving rollouts used for training and evaluating TeleopWM, a predictive latent world model for latency-resilient vision-based teleoperation.
The dataset contains synchronized RGB observations, vehicle controls, speed measurements, and metadata used to construct short-horizon future prediction tasks for both visual rollout prediction and future-action forecasting.
Related Resources
- Project page: https://bimilab.github.io/paper-TeleopWM/
- Model checkpoint: https://huggingface.co/bimilab/TeleopWM
- GitHub repository: https://github.com/bimilab/paper-TeleopWM
- YouTube demo: https://youtu.be/WeKqqZuwBl0
Overview
TeleopWM-Dataset was designed for research on:
- latency-resilient teleoperation
- predictive display
- future observation prediction
- future action prediction
- world models for driving
- autonomous and teleoperated vehicle systems
The dataset follows a CARLA/MILE-style rollout format and contains driving data collected across multiple CARLA towns and driving scenarios.
Dataset Structure
mile_action_diverse/
├── train/
│ ├── Town01/
│ ├── Town03/
│ └── Town04/
│
├── val/
│ └── Town02/
│
└── test/
└── Town05/
The official TeleopWM experiments use:
| Split | Towns |
|---|---|
| Train | Town01, Town03, Town04 |
| Validation | Town02 |
| Test | Town05 |
This split was selected to evaluate generalization to previously unseen environments.
Data Contents
Each rollout contains:
RGB camera images
vehicle controls:
- throttle
- steering
- brake
vehicle speed
route metadata
rollout metadata stored in:
pd_dataframe.pkl
The TeleopWM pipeline constructs:
- 9 past frames
- 8 future frames
for predictive world-model training.
Control Representation
Raw controls are stored as:
[throttle, steer, brake]
TeleopWM internally converts them into:
[longitudinal, scaled_steer, speed]
where:
longitudinal = throttle - brake
This representation is used by the released TeleopWM model.
Dataset Statistics
Approximate release size:
| Split | Size |
|---|---|
| Train | 71 GB |
| Validation | 11 GB |
| Test | 9 GB |
| Total | ~90 GB |
MILE Acknowledgement
This dataset uses a CARLA rollout format derived from the MILE ecosystem.
We acknowledge the contributions of the MILE project and the associated CARLA data-collection framework. The rollout structure, metadata conventions, and driving-data organization are based on the MILE pipeline and were extended for TeleopWM research on predictive display and future-action forecasting.
If you use this dataset, please also consider citing the original MILE work.
Intended Use
This dataset is intended for:
- predictive world-model research
- future frame prediction
- future action prediction
- teleoperation research
- latency mitigation research
- autonomous driving research
- imitation learning research
Out-of-Scope Use
This dataset is not intended to:
- certify safety-critical driving systems
- validate real-world autonomous vehicles without additional testing
- represent all driving environments or traffic conditions
- serve as a benchmark for real-world safety evaluation
Citation
If you use TeleopWM-Dataset, please cite:
@misc{teleopwm_dataset2026,
title={TeleopWM-Dataset: A CARLA Dataset for Latency-Resilient Vision-Based Teleoperation},
author={Khalil, Aws and Kwon, Jaerock},
year={2026}
}
License
This dataset is released under the MIT License.