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Dataset Card for Dataset Name
KITTI-QA is a multimodal (2D/3D) video question-answering benchmark for spatio-temporal reasoning in autonomous driving. Derived from the KITTI tracking dataset [3], it comprises 21 training and 29 testing scenes, with 3D depth maps generated via BridgeDepth [4]. Each scene features 40 safety-centric, handcrafted QA pairs that require models to integrate temporal dynamics with traffic safety knowledge.
Dataset Details
Dataset Description
- Language(s) (NLP): English (US)
- License: CC-BY-NC-SA 3.0
As a derivative work of the KITTI tracking dataset [3], KITTI-QA inherits and complies with the CC BY-NC-SA 3.0 licensing agreement.
Dataset Sources
- Webpage: https://automotivesafety-lvlm.github.io
- Paper: [IEEE IV 2026] D3VL: Understanding Driving Scenes from 3D Time Series Data and Video with Language Models
[IEEE IV 2024] Semantic Understanding of Traffic Scenes with Large Vision Language Models
Dataset Structure
Dataset is divided into train and test datasets. Each row represents (multimodal) video-question-answer triplet.
| column | definition |
|---|---|
| video_name: | name of the scene |
| left: | relative path of RGB video from left camera (i.e. KITTI camera #3) |
| right: | relative path of RGB video from left camera (i.e. KITTI camera #4) |
| depth: | relative path of Depth video from stereo camera and BridgeDepth |
| question: | question |
| answer: | answer |
Dataset Example
Curation Rationale
[More Information Needed]
Source Data
- Raw stereo videos: KITTI Object Tracking Benchmark [3]
- Stereo Depth Estimation: Done by authors [1] using BridgeDepth [4]
- Annotation: Done by authors [1], [2]
[More Information Needed]
Citation [optional]
If you find this work is helpful, please consider citing the followings:
BibTeX:
[1] D3VL
@article{han2026d3vl,
title={D3VL: Understanding Drive Scenes from 3D Time Series Data and Video with Language Models},
author={Han, Heesang and Abbott, A. Lynn and Sarkar, Abhijit},
journal={2026 IEEE Intelligent Vehicle Symposium (IV)},
year={2026}
}
[2] Semantic Understanding of Traffic Scenes with Large Vision Language Models
@INPROCEEDINGS{10588373,
author={Jain, Sandesh and Thapa, Surendrabikram and Chen, Kuan-Ting and Abbott, A. Lynn and Sarkar, Abhijit},
booktitle={2024 IEEE Intelligent Vehicles Symposium (IV)},
title={Semantic Understanding of Traffic Scenes with Large Vision Language Models},
year={2024},
pages={1580-1587},
keywords={Location awareness;Visualization;Laser radar;Semantics;Transportation;Cameras;Cognition;large vision language models (LVLM);scene analysis;automated perception},
doi={10.1109/IV55156.2024.10588373}}
[3] KITTI Tracking Dataset
@article{Geiger2013IJRR,
author = {Andreas Geiger and Philip Lenz and Christoph Stiller and Raquel Urtasun},
title = {Vision meets Robotics: The KITTI Dataset},
journal = {International Journal of Robotics Research (IJRR)},
year = {2013}
}
[4] BridgeDepth Stereo Depth Estimation (Depth Videos)
@article{guan2025bridgedepth,
author = {Guan, Tongfan and Guo, Jiaxin and Wang, Chen and Liu, Yun-Hui},
title = {BridgeDepth: Bridging Monocular and Stereo Reasoning with Latent Alignment},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27681-27691}
}
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