Tokyo-based driving data provides 16 million question-answer pairs over 270,000 frames. The STRIDE-QA dataset is a large-scale visual question answering resource for physically grounded spatiotemporal reasoning in autonomous driving. It was constructed from 100 hours of multi-sensor driving data and includes dense annotations such as 3D bounding boxes, segmentation masks, and multi-object tracks.
Use Cases
- Benchmarking spatiotemporal reasoning models based on the 16 million QA pairs
- Training visual question answering systems for autonomous driving scenarios based on multi-sensor data
- Developing object tracking algorithms based on the multi-object track annotations
- Evaluating 3D scene understanding models based on the 3D bounding box and segmentation mask annotations
Strengths
- Large scale with 16 million QA pairs
- Dense annotations include 3D bounding boxes, segmentation masks, and multi-object tracks
- Constructed from 100 hours of multi-sensor driving data
Limitations
- Column-level documentation is absent; field semantics must be inferred after download
- Row count is unknown, which may limit suitability assessment
Provenance
- Source
- turing-motors
- Collection Method
- Constructed from 100 hours of multi-sensor driving data
- Freshness
- Last updated 2026-01-23 08:31:01; freshness should be verified
- Geography
- Tokyo