Datasets:

uclanlp
/

STRIDE

pretty_name: STRIDE
license: mit
task_categories:
  - visual-question-answering
language:
  - en
size_categories:
  - 1K<n<10K
tags:
  - autonomous-driving
  - spatiotemporal-reasoning
  - nuscenes
  - waymo
  - vqa
configs: []

STRIDE

STRIDE: Evaluating Spatiotemporal Reasoning in Driving Edge Cases

Paper · Code · Project page

This Hub repo contains question annotations (and nuScenes group sidecars). Raw camera images are not included; download them from nuScenes and Waymo Open Dataset under their terms, then follow the GitHub prepare scripts.

Dataset summary

Split	Questions	Source images	Files
nuScenes	1,150	nuScenes trainval	`nuScenes/questions.json`, `nuScenes/formatted_metadata/`
Waymo	1,200	Waymo Open Dataset val	`Waymo/questions.json`
Mini	7 (demo only)	same as above	`Mini/`

39 templates in six families: spatial perception (SP), spatial understanding (SU), temporal memory (TM), temporal extrapolation (TE), trajectory prediction (TRJ), scene-context awareness (SC). Formats: MCQ, open-ended (OEQ), and waypoint regression (TRJ-5 / TRJ-6). Each question uses five frames.

Load

from huggingface_hub import hf_hub_download
import json

path = hf_hub_download(
    repo_id="uclanlp/STRIDE",
    filename="nuScenes/questions.json",
    repo_type="dataset",
)
payload = json.load(open(path))
tasks = payload["tasks"]
print(payload["meta"]["n_questions"], tasks[0]["id"], tasks[0]["question"][:80])

For inference and scoring, clone the code repo and run scripts/prepare_*_media.py / python -m stride.cli score.

License

STRIDE annotation files in this repository are released under MIT. nuScenes and Waymo images remain under their original licenses and must be obtained from those datasets. Do not redistribute those images through this Hub repo.