Datasets:
You need to agree to share your contact information to access this dataset
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
Crash-VQA access is gated to document noncommercial research use and attribution/citation agreement.
Log in or Sign Up to review the conditions and access this dataset content.
Crash-VQA
Crash-VQA: Visual Question Answering for Real-World Crash Understanding
Nine canonical views of one damaged vehicle. Missing views are represented as null.
Overview
Crash-VQA is a multi-image benchmark for structured post-crash vehicle interpretation. Each QA instance contains one task annotation, a normalized label, structured vehicle metadata, and up to nine canonical views of the same vehicle. The natural configuration defines the complete benchmark, while balanced is a derived configuration for controlled task and label balancing.
At a glance
| Metric | Value |
|---|---|
| QA instances | 48,871 |
| Unique images across the release | 97,323 |
| Unique cases | 9,913 |
| Unique vehicles | 12,734 |
| Tasks | 5 |
| Canonical image views | 9 |
The natural configuration defines the complete Crash-VQA benchmark and
contains 48,871 QA instances. The balanced
configuration is a derived view of the corresponding train and test partitions
for controlled task and label balancing. It overlaps with natural and must not
be added to the natural configuration when reporting dataset size.
What makes Crash-VQA useful
- Multi-view evidence: each sample can expose front, rear, side, corner, and top views independently.
- Structured targets: all five tasks use compact normalized label spaces suitable for reproducible evaluation.
- Benchmark and derived view:
naturaldefines the complete benchmark, whilebalancedis an overlapping derived configuration for controlled task and label comparisons. - Vehicle context: optional vehicle type, model year, and curb weight are available as structured metadata.
- Case-isolated splits: public identifiers support analysis without exposing source filesystem paths.
Example task rows
The montage shows one representative image for each task. A model receives the available multi-view image set and predicts the task-specific label.
Quick start
from datasets import load_dataset
natural = load_dataset("oValach/Crash-VQA", "natural")
balanced = load_dataset("oValach/Crash-VQA", "balanced")
row = natural["train"][0]
print(row["task"], row["label"], row["metadata"])
available_images = {
name: row[name]
for name in ['image_front', 'image_rear', 'image_right', 'image_left', 'image_front_right', 'image_front_left', 'image_rear_right', 'image_rear_left', 'image_top']
if row[name] is not None
}
Explore the dataset
Tasks
| Task | Prediction target | Allowed labels | QA instances |
|---|---|---|---|
plane_atomic |
Principal impact plane | front, rear, left, right |
12,563 |
clock_atomic |
Impact direction on a clock face | 1 through 12 |
12,459 |
extent_atomic |
Damage extent | minor, moderate, severe |
11,455 |
deltav_atomic |
Delta-V interval | 0-10, 10-20, 20-30, 30+ |
7,745 |
ais2_atomic |
AIS 2+ injury indicator | true, false |
4,649 |
Configurations and splits
| Configuration | Split | Rows |
|---|---|---|
natural |
train |
36,632 |
natural |
validation |
4,902 |
natural |
test |
7,337 |
balanced |
train |
13,202 |
balanced |
test |
2,414 |
naturalis the default benchmark configuration and contains 48,871 QA instances across train, validation, and test splits.balancedis a derived configuration with 13,202 training rows and 2,414 test rows. It overlaps withnaturaland is intended for controlled task and label comparisons.
Dataset structure
The public representation is deliberately compact: one materialized row represents one QA instance.
| Column | Type | Description |
|---|---|---|
case_id |
string | Stable public case identifier |
split |
string | Train, validation, or test |
task |
string | One of the five task names |
label |
string | Normalized task label |
metadata |
struct | Vehicle type, model year, and curb weight |
image_front |
Image() or null |
Front vehicle view |
image_rear |
Image() or null |
Rear vehicle view |
image_right |
Image() or null |
Right vehicle view |
image_left |
Image() or null |
Left vehicle view |
image_front_right |
Image() or null |
Front-right vehicle view |
image_front_left |
Image() or null |
Front-left vehicle view |
image_rear_right |
Image() or null |
Rear-right vehicle view |
image_rear_left |
Image() or null |
Rear-left vehicle view |
image_top |
Image() or null |
Top vehicle view |
vehicle_id |
string | Stable public vehicle identifier |
sample_id |
string | Stable QA-instance identifier |
The nine image columns are Hugging Face Image() features. Path-backed cells use repository-relative paths; the raw Parquet representation may contain a nullable bytes member, while the Dataset Viewer renders the image itself.
Image views
| Column | Canonical view |
|---|---|
image_front |
Front |
image_rear |
Rear |
image_right |
Right |
image_left |
Left |
image_front_right |
Front-right |
image_front_left |
Front-left |
image_rear_right |
Rear-right |
image_rear_left |
Rear-left |
image_top |
Top |
Vehicle metadata
| Field | Type | Meaning |
|---|---|---|
vehicle_type |
string or null | Source vehicle body/type description |
model_year |
int64 or null | Vehicle model year |
curb_wt_kg |
float64 or null | Curb weight in kilograms |
Nulls represent genuinely unavailable source values.
Source and provenance
The source imagery and vehicle/crash metadata originate from the National Highway Traffic Safety Administration Crash Investigation Sampling System (CISS). CISS collects detailed information from a representative sample of crashes to support vehicle-safety research. Crash-VQA adds the public task formulation, normalized labels, split definitions, identifiers, packaging, and documentation.
See SOURCE_NOTICE.md for source attribution, source terms, and the non-endorsement notice.
Intended uses
Crash-VQA is intended for:
- multi-image visual question answering research;
- post-crash vehicle damage understanding;
- structured crash-analysis benchmarking;
- evaluation of multimodal models across image and vehicle-metadata inputs;
- controlled comparison using the natural benchmark and derived balanced configuration.
Out-of-scope uses
Crash-VQA should not be used as the sole basis for:
- medical, legal, insurance, or accident-liability decisions;
- identifying people, owners, or specific crash locations;
- operational vehicle-safety certification;
- claims that exceed the observable evidence or supplied metadata.
Limitations and responsible use
The dataset may reflect sampling, reporting, geography, vehicle-fleet, crash-severity, image-quality, and missing-metadata biases from the source collection. Damage appearance may be ambiguous across views, and labels simplify complex crash phenomena into discrete benchmark targets. Users should report performance separately by task and should not interpret benchmark accuracy as real-world accident-reconstruction competence.
License
Crash-VQA contains two licensing and provenance layers:
- Underlying NHTSA/CISS source material: source imagery and vehicle/crash metadata originate from NHTSA CISS. These source materials are not relicensed by Crash-VQA and remain subject to applicable NHTSA/DOT terms, disclaimers, and source notices. See
SOURCE_NOTICE.md. - Crash-VQA original contributions: original annotations, normalized labels, task definitions, split definitions, public identifiers, organization, packaging, dataset card content, and documentation are licensed under CC BY-NC 4.0, to the extent those rights are controlled by the Crash-VQA authors. See
LICENSE.
Copies validly obtained under the earlier CC BY 4.0 release remain governed by CC BY 4.0. This license change is not retroactive.
Paper
The accompanying manuscript is titled “Crash-VQA: Visual Question Answering for Real-World Crash Understanding.”
The current intended venue is the MARS² Workshop at ECCV 2026. This wording indicates a planned submission only and must not be changed to “accepted,” “published,” or “presented” unless that status is confirmed.
Citation
A dedicated Crash-VQA citation will be added after the manuscript has an official public record. Until then, cite this dataset repository and include the manuscript title above. Any existing citation file must contain only Crash-VQA-specific or source-specific entries and must not cite unrelated datasets as the primary Crash-VQA reference.
- Downloads last month
- 1,094