| ## 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](https://creativecommons.org/licenses/by-nc/3.0/). | |
| --- | |
| ### 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. | |
| --- | |
| ### 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*) | |
| --- | |
| ### 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} | |
| } | |
| ``` | |