| --- |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - question-answering |
| - visual-question-answering |
| language: |
| - en |
| tags: |
| - autonomous-driving |
| - traffic-rules |
| - multimodal |
| - driving-knowledge |
| size_categories: |
| - 100K<n<1M |
| --- |
| # DriveQA: Passing the Driving Knowledge Test |
|
|
| ## Dataset Summary |
|
|
| DriveQA is a comprehensive multimodal benchmark that evaluates driving knowledge through text-based and vision-based question-answering tasks. The dataset simulates real-world driving knowledge tests, assessing LLMs and MLLMs on traffic regulations, sign recognition, and right-of-way reasoning. |
|
|
| ## Supported Tasks |
|
|
| - **Text-based QA**: Traffic rules, safety regulations, right-of-way principles |
| - **Vision-based QA**: Traffic sign recognition, intersection scene understanding |
| - **Multimodal Reasoning**: Combined visual and textual reasoning for driving scenarios |
|
|
| ## Dataset Structure |
|
|
| ### DriveQA-T (Text-based QA) |
| - **Samples**: 26,143 QA pairs + 1,254 challenging samples |
| - **Categories**: 19 subcategories grouped into 5 major domains (Basic Safety, Lane Rules, Special Cases, Road Signs, Emergencies) |
| - **Format**: Multiple-choice questions with explanations |
|
|
| ### DriveQA-V (Vision-based QA) |
| - **Samples**: 448K image-text QA pairs |
| - **Image Sources**: CARLA simulator + Mapillary real-world data |
| - **Coverage**: 220 US traffic signs, diverse environmental conditions (weather, lighting, perspective, distance) |
| - **Task Types**: Traffic sign recognition, right-of-way judgment |
|
|
| ## Usage |
|
|
| ### Organize the Data Structure |
|
|
| After downloading the dataset files, organize them as follows: |
|
|
| 1. Place all `.jsonl` files in your working directory |
| 2. Extract the image archives to the same directory level: |
| ```bash |
| tar -xzf Intersections_images.tar.gz |
| tar -xzf TrafficSigns_CARLA_images.tar.gz |
| tar -xzf TrafficSigns_Mapillary_images.tar.gz |
| ``` |
|
|
| Your final directory structure should look like: |
| ``` |
| DriveQA/ |
| ├── DriveQA_T.jsonl |
| ├── DriveQA_T_HardSet.jsonl |
| ├── DriveQA_V_Intersections_CARLA.jsonl |
| ├── DriveQA_V_TrafficSigns_CARLA.jsonl |
| ├── DriveQA_V_TrafficSigns_Mapillary.jsonl |
| ├── Intersections_images/ |
| ├── TrafficSigns_CARLA_images/ |
| └── TrafficSigns_Mapillary_images/ |
| ``` |
|
|
| ## License and Citation |
|
|
| This language dataset is licensed under [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). If you use this dataset, please cite our work: |
|
|
| ```bibtex |
| @inproceedings{wei2025driveqa, |
| title={Passing the Driving Knowledge Test}, |
| author={Wei, Maolin and Liu, Wanzhou and Ohn-Bar, Eshed}, |
| booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, |
| year={2025} |
| } |
| ``` |
|
|
| Paper dataset for ICCV 2025 [DriveQA: Passing the Driving Knowledge Test](https://arxiv.org/abs/2508.21824). |
|
|
| For more information and updates, please [visit our website](https://driveqaiccv.github.io/). |
