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.