---
license: cc-by-nc-sa-4.0
task_categories:
- question-answering
language:
- en
tags:
- turing
---


# STRIDE-QA-Dataset-Mini

[![AAAI 2026](https://img.shields.io/badge/AAAI%202026-Oral-red)](https://arxiv.org/abs/2508.10427)
[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://turingmotors.github.io/stride-qa/)
[![GitHub](https://img.shields.io/badge/GitHub-Code-black?logo=github)](https://github.com/turingmotors/STRIDE-QA-Dataset)
[![Dataset](https://img.shields.io/badge/🤗%20HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/turing-motors/STRIDE-QA-Dataset)
[![Benchmark](https://img.shields.io/badge/🤗%20HuggingFace-Benchmark-yellow)](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).