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[CVPR 2026 Oral] Argoverse 2 scenario mining dataset and baselines

RefAV 최신버젼 다운로드

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<h2 align="center">RefAV: Towards Planning-Centric Scenario Mining</h2>

<p align="center">
  <a href="https://arxiv.org/abs/2505.20981"><img src="https://img.shields.io/badge/arXiv-2505.20981-b31b1b.svg" alt="arXiv"></a>
  <a href="https://huggingface.co/datasets/CainanD/RefAV"><img src="https://img.shields.io/badge/HuggingFace-Dataset-FFD21E.svg?logo=huggingface" alt="dataset"></a>
  <a href="https://eval.ai/web/challenges/challenge-page/2662/overview"><img src="https://img.shields.io/badge/EvalAI-Competition-3b82f6.svg" alt="competition"></a>
  <a href="https://cainand.github.io/RefAV/"><img src="https://img.shields.io/badge/Project-Website-8A2BE2.svg" alt="website"></a>
</p>

### 🚨 Competition Update 🚨

We are hosting a new competition in 2026! We have annotated over 5x the number of visual events (120 → 600) and given all scenario descriptions positive and negative instances. Submissions must be made before May 31st, 2026. Winners will be announced at the CVPR 2026 Workshop on Autonomous Driving. Top performing teams can win cash prizes, generously sponsored by Uber.

⏱️ Temporal Localization Winner: $2500

📍 Spatio-Temporal Localization Winner: $2500

💡 Efficiency Innovation Award: $2500

### About

A single autonomous vehicle will stream about ~4TB of data per hour with a full stack of camera and lidar sensors. The vast majority of this data comes from uninteresting scenarios - the ego vehicle driving straight down a lane, possibly with another car in front of it. It can be prohibitively expensive to retrieve and label specific scenarios for ego-behaivor evaluation, safety testing, or active learning at scale.

RefAV serves as the baseline for the 2026 Argoverse2 Scenario Mining Challenge. It utilizes an LLM to construct composable function calls from a set of hand-crafted atomic functions such as "turning" or "has_objects_in_front". Given a prompt, the LLM outputs a composable function that narrows down a set of bounding box track predictions to the set that best corresponds to the prompt. Our paper describing the dataset and baseline in detail is available on [arXiv](https://arxiv.org/pdf/2505.20981).

### Installation

Using [Conda](https://anaconda.org/anaconda/conda) is recommended for environment management
```
conda create -n refAV python=3.10
conda activate refAV
```

All of the required libaries and packages can be installed with

```
pip install -r requirements.txt
export PYTHONPATH=.
```

### Downloading the Dataset

Running this code requires downloading the Argoverse2 test and val splits. Run the commands below to download the entire sensor dataset. The entire dataset is roughly 1TB, but you should be able to avoid downloading the large train split if space is limited. For more information check out [Argoverse User Guide](https://argoverse.github.io/user-guide/getting_started.html#downloading-the-data).
```
conda install s5cmd -c conda-forge

export DATASET_NAME="sensor"  # sensor, lidar, motion_forecasting or tbv.
export TARGET_DIR="$HOME/data/datasets"  # Target directory on your machine.

s5cmd --no-sign-request cp "s3://argoverse/datasets/av2/$DATASET_NAME/*" $TARGET_DIR
```
The RefAV scenario mining dataset can be downloaded from Huggingface using:
```
pip install huggingface_hub
hf auth login

export TARGET_DIR="$(pwd)/scenario_mining_downloads"
hf download CainanD/RefAV --repo-type dataset --local-dir=$TARGET_DIR
```
Alternatively, you may download the scenario-mining add on from Argoverse without having to sign-in to Huggingface with:
```
export TARGET_DIR="$(pwd)/scenario_mining_downloads"
s5cmd --no-sign-request cp "s3://argoverse/tasks/scenario_mining/*" $TARGET_DIR
```

### Generating Detections and Tracks
See the [LT3D repository](https://github.com/neeharperi/LT3D) for information on training a baseline detector and tracker on the Argoverse 2 dataset.

We provide additional tracking outputs from previous winning Argoverse submissions on the test set [here](https://drive.google.com/file/d/1X19D5pBBO56eb_kvPOePLLhHDCsY0yql/view)

### Running the Code

All of the code necessary for unpacking the dataset, generating referred track predictions,
and evaluating the predictions against the ground truth can be found in the `tutorial.ipynb` file.
It also includes some basic tutorials about how to define and visualize a scenario.

Our experimental results and test/val submissions can be reproduced directly by running `python run_experiment.py --exp_name <exp_name>`. All experiments are found in the `experiments.yml` file.

### Submission Format

Submissions can be made through through the [EvalAI](https://eval.ai/) CLI. To submit to the to the temporal and spatiotemporal challenge tracks respectively, create an EvalAI profile and run
```bash
pip install evalai
evalai set_token <EvalAI_account_token>
evalai challenge 2662 phase 5284 submit --file path/to/temporal_submission_test.pkl --large
evalai challenge 2662 phase 5282 submit --file path/to/spatiotemporal_submission_test.pkl --large
```

The spatio-temporal track expects a dictionary of lists of dictionaries
```python
{
      <(log_id,prompt)>: [
            {
                  "timestamp_ns": <timestamp_ns>,
                  "track_id": <track_id>
                  "score": <score>,
                  "label": <label>,
                  "name": <name>,
                  "translation_m": <translation_m>,
                  "size": <size>,
                  "yaw": <yaw>,
            }
      ]
}
```

The temporal track expects the same format, but only requires a single boolean entry per timestamp. You may also submit files with the spatio-temporal format.
```python
{
      <(log_id,prompt)>: [
            {
                  "timestamp_ns": <timestamp_ns>,
                  "is_positive": <is_positive>
            }
      ]
}
```

* log_id: Log id associated with the track, also called seq_id.  
* prompt: The prompt/description string that describes the scenario associated with the log.  
* timestamp_ns: Timestamp associated with the synced lidar/video frames.
* is_positive: Whether the video timestamp includes a referred object or action.  
* track_id: Unique id assigned to each track, this is produced by your tracker.  
* score: Track confidence.  
* label: Integer index of the object class. This is 0 for REFERRED_OBJECTs, 1 for RELATED_OBJECTs, and 2 for OTHER_OBJECTs  
* name: Object class name.  
* translation_m: xyz-components of the object translation in the city reference frame, in meters.  
* size: Object extent along the x,y,z axes in meters.  
* yaw: Object heading rotation along the z axis.

### Example Submission

Spatio-Temporal Track:
```python
example_tracks = {
  ('02678d04-cc9f-3148-9f95-1ba66347dff9','vehicle turning left at stop sign'): [
    {
       'timestamp_ns': 315969904359876000,
       'translation_m': array([[6759.51786422, 1596.42662849,   57.90987307],
             [6757.01580393, 1601.80434654,   58.06088218],
             [6761.8232099 , 1591.6432147 ,   57.66341136],
             ...,
             [6735.5776378 , 1626.72694938,   59.12224152],
             [6790.59603472, 1558.0159741 ,   55.68706682],
             [6774.78130127, 1547.73853494,   56.55294184]]),
       'size': array([[4.315736  , 1.7214599 , 1.4757565 ],
             [4.3870926 , 1.7566483 , 1.4416479 ],
             [4.4788623 , 1.7604711 , 1.4735452 ],
             ...,
             [1.6218852 , 0.82648355, 1.6104599 ],
             [1.4323177 , 0.79862624, 1.5229694 ],
             [0.7979312 , 0.6317313 , 1.4602867 ]], dtype=float32),
      'yaw': array([-1.1205611 , ... , -1.1305285 , -1.1272993], dtype=float32),
      'name': array(['REFERRED_OBJECT', ..., 'REFERRED_OBJECT', 'RELATED_OBJECT'], dtype='<U31'),
      'label': array([ 0, 0, ... 0,  1], dtype=int32),
      'score': array([0.54183, ..., 0.47720736, 0.4853499], dtype=float32),
      'track_id': array([0, ... , 11, 12], dtype=int32),
    },
    ...
  ],
  ...
}
```

Temporal Track:
```python
example_tracks = {
  ('02678d04-cc9f-3148-9f95-1ba66347dff9','vehicle turning left at stop sign'): [
    {
       'timestamp_ns': 315969904359876000,
       'is_positive': True
    },
    ...
  ],
  ...
}
```

### Benchmark Evaluation

| **Metric** | **Description** |
|------------|-----------------|
| HOTA-Temporal | HOTA on temporally localized tracks |
| HOTA-Track | HOTA on the full length of a track |
| Timestamp Balanced Accuracy | Timestamp level classification metric |
| Log Balanced Accuracy | Data log/scenario level classification metric |

All metrics are computed by taking the average value over each of the unique prompts in the scenario mining dataset. In this way, each prompt can be viewed as its own class.

### Additional Competition Details

* Language queries are object-centric -- all correspond to some set of objects.
* A REFERRED_OBJECT is the object referenced first in the prompt (e.g. "pedestrians" for the prompt "pedestrians in front of the ego vehicle"). In this example, "ego vehicle" would be a RELATED_OBJECT.
* Most language queries are given from the third person persective (such as "ego vehicle turning left"). The language queries given from the first-person perspective (such as "the pedestrian on the right") describe objects from the point of view of the ego vehicle.
* Scenarios only involve objects within 50 meters from the ego vehicle and within 5 meters of a mapped road.
* Interacting objects within a scenario are at most 50 meters away from each other. 
* All referred object tracks persist for at least 3 evaluation timestamps (1.5s).
  
The ego vehicle has the following bounding box across all logs and timestamps
'translation_m': [1.422, 0, 0.25]
'size': [4.877, 2, 1.473]
'yaw': [0]

### Contact

Any questions or discussion are welcome! Please raise an issue (preferred), or send me an email.

Cainan Davidson [crdavids@andrew.cmu.edu]

## Citation
If you find our paper and code repository useful, please cite us:
```bib
@inproceedings{davidson2026refav,
  title={RefAV: Towards Planning-Centric Scenario Mining},
  author={Davidson, Cainan and Ramanan, Deva and Peri, Neehar},
  journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2026}
}
```

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