| --- |
| license: mit |
| task_categories: |
| - robotics |
| tags: |
| - CARLA |
| - Robotics |
| - autonomous-driving |
| - end-to-end-driving |
| - multi-modal |
| - 123D |
| pretty_name: LEAD 123D |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| <p align="center"> |
| <picture> |
| <source media="(prefers-color-scheme: dark)" srcset="https://kesai.eu/py123d/_static/123D_logo_transparent_white.svg"> |
| <source media="(prefers-color-scheme: light)" srcset="https://kesai.eu/py123d/_static/123D_logo_transparent_black.svg"> |
| <img alt="123D" src="https://kesai.eu/py123d/_static/123D_logo_transparent_black.svg" width="320"> |
| </picture> |
| </p> |
| |
| <h2 align="center">LEAD 123D</h2> |
|
|
| <p align="center"> |
| Five hours of CARLA expert driving logs in the unified <a href="https://github.com/kesai-labs/py123d">123D</a> Apache Arrow format. |
| </p> |
|
|
| <p align="center"> |
| <a href="https://ln2697.github.io/lead">Project</a> · <a href="https://github.com/kesai-labs/lead">Code</a> · <a href="https://github.com/kesai-labs/py123d">py123d</a> |
| </p> |
|
|
| ## Overview |
|
|
| **LEAD 123D** is a CARLA Leaderboard 2.0 driving dataset collected with LEAD's rule-based privileged expert and stored in the [123D](https://github.com/kesai-labs/py123d) unified driving-data format. Each route is a self-contained directory of Apache Arrow IPC files, one per modality. |
|
|
| ## Data structure |
|
|
| | File | Content | |
| | :--- | :--- | |
| | `ego_state_se3.arrow` | Ego vehicle pose, velocity, acceleration | |
| | `camera.pcam_{f,b,l,r}{0,1}.arrow` | RGB camera streams (front, back, left, right) | |
| | `lidar.lidar_top.arrow` | Top LiDAR point clouds | |
| | `box_detections_se3.arrow` | 3D bounding boxes for all dynamic actors | |
| | `traffic_light_detections.arrow` | Per-frame traffic light states | |
| | `sync.arrow` | Cross-modality synchronization timestamps | |
| | `maps/carla/*.arrow` | HD map: lanes, intersections, crosswalks, road edges, road lines (WKB geometry) | |
|
|
| - **Coordinate conventions:** ISO 8855 (vehicle / body), OpenCV (cameras) |
|
|
| ## Usage |
|
|
| Install [py123d](https://github.com/kesai-labs/py123d) and pull the dataset: |
|
|
| ```bash |
| pip install py123d |
| |
| git lfs install |
| git clone https://huggingface.co/datasets/ln2697/lead123d |
| ``` |
|
|
| See the [py123d documentation](https://kesai.eu/py123d/) for loading, visualization, and Scene/Map API usage. |
|
|
| ## License |
|
|
| Released under the [MIT License](https://github.com/kesai-labs/lead/blob/main/LICENSE). |
| CARLA assets and OpenDRIVE maps remain under their respective upstream licenses. |
|
|
| ## Citation |
|
|
| If you use LEAD 123D, please cite the papers: |
|
|
| ```bibtex |
| @inproceedings{Nguyen2026CVPR, |
| author = {Long Nguyen and Micha Fauth and Bernhard Jaeger and Daniel Dauner and |
| Maximilian Igl and Andreas Geiger and Kashyap Chitta}, |
| title = {LEAD: Minimizing Learner-Expert Asymmetry in End-to-End Driving}, |
| booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| year = {2026}, |
| } |
| |
| @software{Contributors123D, |
| title = {123D: A Unified Library for Multi-Modal Autonomous Driving Data}, |
| author = {123D Contributors}, |
| year = {2026}, |
| url = {https://github.com/kesai-labs/py123d}, |
| license = {Apache-2.0} |
| } |
| ``` |
|
|
