Datasets:
Tasks:
Question Answering
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
turing
License:
| license: cc-by-nc-sa-4.0 | |
| task_categories: | |
| - question-answering | |
| language: | |
| - en | |
| tags: | |
| - turing | |
| # STRIDE-QA-Dataset-Mini | |
| [](https://arxiv.org/abs/2508.10427) | |
| [](https://turingmotors.github.io/stride-qa/) | |
| [](https://github.com/turingmotors/STRIDE-QA-Dataset) | |
| [](https://huggingface.co/datasets/turing-motors/STRIDE-QA-Dataset) | |
| [](https://huggingface.co/datasets/turing-motors/STRIDE-QA-Bench) | |
| **STRIDE-QA** is a large-scale visual question answering (VQA) dataset for physically grounded spatiotemporal reasoning in autonomous driving. Constructed from 100 hours of multi-sensor driving data in Tokyo, it offers **16 M QA pairs** over **270 K frames** with dense annotations including 3D bounding boxes, segmentation masks, and multi-object tracks. | |
| ⚠️ **Note**: **STRIDE-QA-Dataset-Mini** is provided as a preliminary version and does not fully match the format of the final dataset. | |
| For the final dataset, please refer to: <https://huggingface.co/datasets/turing-motors/STRIDE-QA-Dataset>. | |
| ## 🔑 Key Features | |
| | Category | Description | | |
| | --- | --- | | |
| | **Object-centric Spatial QA** | Spatial relations between two surrounding agents (single frame). Includes qualitative (e.g., relative position) and quantitative (e.g., distance, angle) questions. | | |
| | **Ego-centric Spatial QA** | Spatial relations between the ego vehicle and a surrounding agent (single frame). Covers distance, direction, and size comparisons. | | |
| | **Ego-centric Spatiotemporal QA** | Short-term prediction using 4 context frames (2 Hz). Forecasts distance, heading angle, and velocity at t ∈ {1, 2, 3} s. | | |
| ## 🗂️ Data Fields | |
| | Field | Type | Description | | |
| | --- | --- | --- | | |
| | `id` | `str` | Unique sample ID. | | |
| | `image` | `str` | File name of the key frame used in the prompt. | | |
| | `images` | `list[str]` | File names for the four consicutive image frames. Only avaiable in Ego-centric Spatiotemporal QA category. | | |
| | `conversations` | `list[dict]` | Dialogue in VILA format (`"from": "human"` / `"gpt"`). | | |
| | `bbox` | `list[list[float]]` | Bounding boxes \[x₁, y₁, x₂, y₂] for referenced regions. | | |
| | `rle` | `list[dict]` | COCO-style run-length masks for regions. | | |
| | `region` | `list[list[int]]` | Region tags mentioned in the prompt. | | |
| | `qa_info` | `list` | Meta data for each message turn in dialogue. | | |
| ## 📊 Dataset Statistics | |
| | Category | Source file | QA pairs | | |
| | --- | --- | --- | | |
| | Object-centric Spatial QA | `object_centric_spatial_qa.json` | **19,895** | | |
| | Ego-centric Spatial QA | `ego_centric_spatial_qa.json` | **54,390** | | |
| | Ego-centric Spatio-temporal QA | `ego_centric_spatiotemporal_qa_short_answer.json` | **28,935** | | |
| | Images | `images/*.jpg` | **5,539** files | | |
| ## 🔗 Related Links | |
| - Project Page: <https://turingmotors.github.io/stride-qa> | |
| - GitHub: <https://github.com/turingmotors/STRIDE-QA-Dataset> | |
| - STRIDE-QA-Dataset: <https://huggingface.co/datasets/turing-motors/STRIDE-QA-Dataset> | |
| - STRIDE-QA-Bench: <https://huggingface.co/datasets/turing-motors/STRIDE-QA-Bench> | |
| ## 📚 Citation | |
| ```bibtex | |
| @misc{strideqa2025, | |
| title={STRIDE-QA: Visual Question Answering Dataset for Spatiotemporal Reasoning in Urban Driving Scenes}, | |
| author={Keishi Ishihara and Kento Sasaki and Tsubasa Takahashi and Daiki Shiono and Yu Yamaguchi}, | |
| year={2025}, | |
| eprint={2508.10427}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV}, | |
| url={https://arxiv.org/abs/2508.10427}, | |
| } | |
| ``` | |
| ## 📄 License | |
| STRIDE-QA-Bench is released under the [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en). | |
| ## 🤝 Acknowledgements | |
| This benchmark is based on results obtained from a project, JPNP20017, subsidized by the New Energy and Industrial Technology Development Organization (NEDO). | |
| We would like to acknowledge the use of the following open-source repositories: | |
| - [SpatialRGPT](https://github.com/AnjieCheng/SpatialRGPT?tab=readme-ov-file) for building dataset generation pipeline | |
| - [SAM 2.1](https://github.com/facebookresearch/sam2) for segmentation mask generation | |
| - [dashcam-anonymizer](https://github.com/varungupta31/dashcam_anonymizer) for anonymization | |
| ## 🔏 Privacy Protection | |
| To ensure privacy protection, human faces and license plates in the images were anonymized using the [Dashcam Anonymizer](https://github.com/varungupta31/dashcam_anonymizer). | |