---
pretty_name: OccStress
viewer: false
license: other
license_name: occstress-source-dependent-terms
license_link: https://huggingface.co/datasets/insailab/OccStress/blob/main/DISTRIBUTION.md
task_categories:
- other
tags:
- 3d
- timeseries
- occupancy-forecasting
- robustness
- autonomous-driving
- nuscenes
- waymo
- carla
---
OccStress
Stress-Testing the 4D Occupancy Forecasting Chain
NeurIPS 2026 · Evaluations and Datasets Track
Download ·
Coverage ·
Data Format ·
Licenses and Use
**How do perception errors affect future occupancy forecasts?** OccStress
evaluates robustness across the occupancy forecasting chain, from sensor errors
to 3D occupancy states and multi-horizon predictions.
3 datasets · 10,827 unique anchors · 975 protocols · 6 future horizons
This repository provides **evaluation protocols, derived occupancy assets and
supporting metadata**. All **75 archives are available**, totaling **40.95 GB
compressed** and **143.67 GB extracted** including metadata. Download only the
dataset, track or upstream source you need.
Original camera images, LiDAR, clean GT and model checkpoints are **not bundled**.
See [external prerequisites](EXTERNAL_DATA.md) and [source-dependent terms](DISTRIBUTION.md).
## Dataset Coverage
| Dataset | Source | Scenes | Anchors per protocol |
| --- | --- | ---: | ---: |
| **OccStress-nuScenes** | nuScenes / Occ3D | 150 | 4,519 |
| **OccStress-Waymo** | Waymo / Occ3D | 202 | 5,978 |
| **OccStress-CARLA** | UniOcc CARLA simulation | 3 | 330 |
All datasets use **2 Hz observations**, a canonical window of four historical
states plus the current state, and six future targets at **+0.5 to +3.0 seconds**.
The selected frames total 6,019 / 7,998 / 360 for nuScenes / Waymo / CARLA.
| Published protocols | nuScenes | Waymo | CARLA |
| --- | ---: | ---: | ---: |
| Manual | 38 | 38 | 38 |
| Upstream | 364 | 219 | 218 |
| Position sweep | 60 | 0 | 0 |
Counts include clean references. **Protocols reuse anchors and assets**;
975 protocols do not represent 975 independent datasets or new sets of samples.
Waymo's two EFFOcc sensor-stress branches retain separate clean protocol files.
## What Can You Evaluate?
- **Upstream robustness:** use source-specific occupancy predictions under
camera or LiDAR stressors to measure error propagation into forecasting.
- **Manual robustness:** test semantic replacement, dropout, holes,
misalignment and traffic mirroring directly in occupancy states.
- **Temporal sensitivity:** compare Current-only, Recent-burst and History-only
failure regimes, or use the fixed-budget, single-state position sweep to
isolate input-position effects.
Traffic mirroring transforms inputs, targets and motion metadata consistently
and is reported separately from non-traffic robustness. Four-state models do
not observe the earliest canonical input; that position is marked unobserved,
not robust. See [format and evaluation conventions](FORMAT.md).
## Download
### Start With One Dataset and Track
For example, download the **Waymo manual track** and its metadata:
```bash
python -m pip install -U huggingface_hub
hf download insailab/OccStress --repo-type dataset \
--local-dir /datasets/OccStress-download \
--include README.md DOWNLOAD.md FORMAT.md EXTERNAL_DATA.md DISTRIBUTION.md packages.json \
'metadata/common.tar.zst' 'metadata/OccStress-Waymo.tar.zst' \
'archives/manual/OccStress-Waymo/*.tar.zst'
```
Use [DOWNLOAD.md](DOWNLOAD.md) for other tracks, source-specific selection,
pinned revisions, checksum verification and extraction commands.
The [package index](packages.json) lists every archive's size and SHA256.
Extract selected archives into one shared `OccStress/` root, then prepare
[external GT and metadata](EXTERNAL_DATA.md) before running a model.
Reserve space for both downloaded archives and their extracted contents;
original dependencies need additional storage.
What is inside the extracted dataset?
```text
OccStress/
meta// Dataset metadata and controls
protocols/manual// Manual evaluation protocols
protocols/upstream// Source-specific evaluation protocols
protocols/position_sweep/ Single-state temporal diagnostics
occ/manual// Shared manual corruption assets
occ/upstream// Exported upstream occupancy
events/manual// Corruption event metadata
```
`` is `OccStress-nuScenes`, `OccStress-Waymo` or `OccStress-CARLA`.
Protocol paths are relative to this root. Position sweeps reuse the existing
occupancy assets; they do not need duplicate voxel packs. See [FORMAT.md](FORMAT.md).
## Before Evaluation
- **Use the matching ground truth.** Native Waymo labels require the supplied
mapping; CARLA requires the canonical right-handed view. Already converted
occupancy arrays must not be transformed twice.
- **Keep the model's input and control policy.** Use all six genuine future
targets; do not substitute a current-frame reconstruction.
- **Keep metric conventions explicit.** Historical paper metrics and the
released present-class convention are not interchangeable. Paper averages
use the 1, 2 and 3 second horizons.
- **Use the assets directly when applicable.** Occupancy-input forecasting
does not require upstream weights or original sensors. Camera-direct methods
and upstream re-export do require the relevant sensor inputs.
This is an **archive-based benchmark**, not an Arrow table for
`datasets.load_dataset`. Only unpickle protocol files from trusted sources.
## Scope and Limitations
OccStress is a controlled diagnostic stress test, **not a deployment-risk
estimate or safety certification**. Its corruptions are reproducible stress
conditions, not a calibrated distribution of real-world failures.
CARLA contains only three simulated validation scenes. The datasets differ in
class support and sensor configuration; report cross-dataset and zero-shot
settings explicitly. Published protocols do not imply that every model supports
every track or that every model/protocol combination has been evaluated.
Preparation checks and their scope are recorded in [FORMAT.md](FORMAT.md#validation-scope)
and the published manifests. Source-dataset registration, attribution and use
conditions still apply; see [DISTRIBUTION.md](DISTRIBUTION.md).
## Croissant Metadata
De-anonymized [Croissant 1.1 metadata](croissant/README.md) is available for
all three datasets, including Responsible AI documentation and provenance.
[Camera-ready ZIP](croissant/OccStress-Croissant.zip) ·
[nuScenes JSON](croissant/OccStress-nuScenes.json) ·
[Waymo JSON](croissant/OccStress-Waymo.json) ·
[CARLA JSON](croissant/OccStress-CARLA.json)
The metadata links the original packages and provides machine-readable anchor,
protocol and label-map indexes. It does not replace the voxel loaders or the
[external ground-truth prerequisites](EXTERNAL_DATA.md).
## Citation
```bibtex
@inproceedings{zheng2026occstress,
title = {{OccStress}: Stress-Testing the {4D} Occupancy Forecasting Chain},
author = {Zheng, Yu and Hu, Jie and Xiong, Jiaqi and Liu, Ruiping and Zheng, Junwei and Yang, Kailun and Zhang, Jiaming},
booktitle = {Advances in Neural Information Processing Systems},
year = {2026},
url = {https://arxiv.org/abs/2512.15621}
}
```
Please also cite the source datasets, corruption operators and upstream models
used in your evaluation. Source references are listed in [FORMAT.md](FORMAT.md#sources).
For dataset issues, open a [discussion](https://huggingface.co/datasets/insailab/OccStress/discussions)
with the dataset, protocol path, revision and error message. Do not post tokens
or private source-data links.