LLVM-AD/maplm_v2

MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene Understanding

Version 2.0 - WACV 2025 LLVM-AD Challenge

Developed by:
Tencent, University of Illinois at Urbana-Champaign, Purdue University, University of Virginia

License

This dataset is released under the CC-BY-NC-3.0 License.

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Dataset Structure

data/
├── images/
│   ├── FR1/
│   │   ├── photo_forward.jpg
│   │   ├── photo_lef_back.jpg
│   │   ├── photo_rig_back.jpg
│   │   ├── point_cloud_bev.jpg
│   ├── FR2/
│   │   ├── photo_forward.jpg
│   │   ├── photo_lef_back.jpg
│   │   ├── photo_rig_back.jpg
│   │   ├── point_cloud_bev.jpg
│   └── ...
├── train_v2.json
├── val_v2.json
└── test_v2.json

Input Data

The dataset includes:

1. Image Views:

   • Forward View: A forward-facing photo of the road scene.
   • Back Left/Right Views: Photos capturing the back left and back right perspectives.

   Examples:
   
   
   

2. Point Cloud (BEV):

   • A Bird's Eye View (BEV) image generated from the 3D point cloud data.

   Example:
   

Note:
Participants can choose which inputs to use for the challenge. HD map annotations are not included in this dataset version. All data adhere to standards for producing HD maps.

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Challenge QAs

1. Scene Type (SCN): Identify the type of road scene depicted in the images.
2. Point Cloud Quality (QLT): Assess the quality of the point cloud data for the current road area.
3. Intersection Detection (INT): Determine if the main road features a crossroad, intersection, or lane change zone.
4. Lane Count (LAN): Count the number of lanes on the current road. (May not apply to all cases)
5. Lane Description (DES): Describe the attributes of the lanes on the current road. (May not apply to all cases)
6. Scene Captioning (CAP): Provide a detailed description of the current driving scene. (May not apply to all cases)
7. Unusual Object Detection (OBJ): Identify any unusual objects visible in the images. (May not apply to all cases)
8. Lane Change Prediction (MOVE): Predict the ego vehicle's lane change behavior. (May not apply to all cases)
9. Speed Prediction (SPEED): Predict the ego vehicle's speed behavior. (May not apply to all cases)

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Citation

If you use this dataset, please cite the following works:

Main Dataset Paper:

@inproceedings{cao2024maplm,
  title={MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene Understanding},
  author={Cao, Xu and Zhou, Tong and Ma, Yunsheng and Ye, Wenqian and Cui, Can and Tang, Kun and Cao, Zhipeng and Liang, Kaizhao and Wang, Ziran and Rehg, James M and others},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={21819--21830},
  year={2024}
}

HD Map Annotation System Reference:

@inproceedings{tang2023thma,
  title={Thma: Tencent hd map ai system for creating hd map annotations},
  author={Tang, Kun and Cao, Xu and Cao, Zhipeng and Zhou, Tong and Li, Erlong and Liu, Ao and Zou, Shengtao and Liu, Chang and Mei, Shuqi and Sizikova, Elena and others},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  number={13},
  pages={15585--15593},
  year={2023}
}