| --- |
| license: apache-2.0 |
| size_categories: |
| - 100B<n<1T |
| --- |
| # **PDM-Lite Dataset for CARLA Leaderboard 2.0** |
|
|
| ## Description |
| [PDM-Lite](https://github.com/OpenDriveLab/DriveLM/tree/DriveLM-CARLA/pdm_lite) is a state-of-the-art rule-based expert system for autonomous urban driving in [CARLA Leaderboard 2.0](https://leaderboard.carla.org/get_started/), and the first to successfully navigate all scenarios. This dataset was used to create the QA dataset for [DriveLM-Carla](https://github.com/OpenDriveLab/DriveLM/tree/DriveLM-CARLA), a benchmark for evaluating end-to-end autonomous driving algorithms with Graph Visual Question Answering (GVQA). DriveLM introduces GVQA as a novel approach, modeling perception, prediction, and planning through interconnected question-answer pairs, mimicking human reasoning processes. Additionally, this dataset was used for training [Transfuser++](https://kashyap7x.github.io/assets/pdf/students/Zimmerlin2024.pdf) with imitation learning, which achieved 1st place (map track) and 2nd place (sensor track) in the [CARLA Autonomous Driving Challenge 2024](https://opendrivelab.com/challenge2024/#carla). This dataset builds upon the [PDM-Lite](https://github.com/OpenDriveLab/DriveLM/tree/DriveLM-CARLA/pdm_lite) expert, incorporating enhancements from "[Tackling CARLA Leaderboard 2.0 with End-to-End Imitation Learning](https://kashyap7x.github.io/assets/pdf/students/Zimmerlin2024.pdf)". |
|
|
| For more information and a script for downloading and unpacking visit our [GitHub](https://github.com/OpenDriveLab/DriveLM/tree/DriveLM-CARLA). |
|
|
| ## Dataset Features |
|
|
| - **High-Quality Data:** 5,134 routes with 100 % route completion and zero infractions on 8 towns, sampled at 4 Hz, totaling 581,662 frames |
| - **Diverse Scenarios:** Covers 38 complex scenarios, including urban traffic, participants violating traffic rules, and high-speed highway driving |
| - **Focused Evaluation:** Short routes averaging 160 m in length |
|
|
| ## Data Modalities |
|
|
| - **BEV Semantics Map:** 512x512 pixels, centered on ego vehicle, 2 pixels per meter resolution |
| - **Image Data:** 1024x512 pixels, RGB images, semantic segmentation, and depth information |
| - **Lidar Data:** Detailed lidar point clouds with 600,000 points per second |
| - **Augmented Data:** Augmented versions of RGB, semantic, depth, and lidar data |
| - **Simulator Data:** Comprehensive information on nearby objects |
|
|
| ## License and Citation |
| Apache 2.0 license unless specified otherwise. |
|
|
| ```bibtex |
| @inproceedings{sima2024drivelm, |
| title={DriveLM: Driving with Graph Visual Question Answering}, |
| author={Chonghao Sima and Katrin Renz and Kashyap Chitta and Li Chen and Hanxue Zhang and Chengen Xie and Jens Beißwenger and Ping Luo and Andreas Geiger and Hongyang Li}, |
| booktitle={European Conference on Computer Vision}, |
| year={2024}, |
| } |
| @misc{Beißwenger2024PdmLite, |
| title = {{PDM-Lite}: A Rule-Based Planner for CARLA Leaderboard 2.0}, |
| author = {Bei{\ss}wenger, Jens}, |
| howpublished = {\url{https://github.com/OpenDriveLab/DriveLM/blob/DriveLM-CARLA/docs/report.pdf}}, |
| year = {2024}, |
| school = {University of Tübingen}, |
| } |
| ``` |
