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README.md

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+---
+license: mit
+language:
+- en
+tags:
+- autonomous-driving
+- teleoperation
+- robotics
+- carla
+- world-model
+- video-prediction
+- predictive-display
+- future-action-prediction
+- imitation-learning
+- mile
+task_categories:
+- image-to-video
+---
+
+# 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
+
+```text
+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:
+
+```text
+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:
+
+```text
+[throttle, steer, brake]
+```
+
+TeleopWM internally converts them into:
+
+```text
+[longitudinal, scaled_steer, speed]
+```
+
+where:
+
+```text
+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:
+
+```bibtex
+@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.
+