Official implementation of DriveStreamBench: Evaluating User-Conditioned Watch-and-Notify in Streaming Driving Video (NeurIPS 2026).

DriveStreamBench evaluates user-conditioned watch-and-notify in streaming driving video. Given a watch instruction, a model observes causal video frames and emits ALERT when the specified event occurs, or NO REPLY otherwise.

Data · Croissant

python -m pip install -e '.[test]'
bash scripts/run_smoke_test.sh

Python 3.9 or later is required. Scoring and the example run work on CPU. Install .[vlm], .[api] or .[train] for the corresponding model backend.

bash scripts/prepare_data.sh
# A downloaded ZIP can also be installed locally:
python scripts/download_data.py --archive /path/to/DriveStreamBench-data-v1.0.0.zip

The annotation release contains 26,853 records across L1-L4. Source media are obtained separately under the providers' terms. See data preparation.

After data installation, external/ood/file/ood_video_links.txt lists the OOD source videos, and external/ood/OOD_data_process.md provides the preprocessing workflow.

python -m drivestreambench.inference.run_l4 \
  --annotations annotations/l4_core_test.jsonl \
  --dataroot /path/to/frames \
  --model qwen3vl:Qwen/Qwen3-VL-8B-Instruct \
  --output results/predictions/model.jsonl \
  --manifest results/model.manifest.json

python scripts/evaluate.py \
  --annotations annotations/l4_core_test.jsonl \
  --predictions results/predictions/model.jsonl \
  --output results/scores/model.json --fps 2

The default L4 protocol uses 2 FPS input and one decision per second. Scoring maps each 10-FPS reference frame onto the corresponding sampled-frame grid; pass --fps 1 when scoring a native 1-FPS run. Core Macro averages AlertScore@2s across S1-S5; S6 is evaluated separately. See evaluation for output formats, parser selection and L1-L3 scoring, and models for adapter usage.

For the short setting, run bash scripts/build_l4_short.sh; it writes annotations/l4_short_test.jsonl with a maximum duration of 15 seconds per sample.

The reference SFT recipe provides data conversion and training entry points. Model weights are outside this release.

@inproceedings{drivestreambench2026,
  title = {DriveStreamBench: Evaluating User-Conditioned Watch-and-Notify in Streaming Driving Video},
  author = {Yi Wang and Xin Zhao and Tian Meng and Rui Dai and Chi Zhang and Cui Tang and Bingzheng Liu and Fucheng Wang and Haopeng Zhang and Ruidong Ding and Yang Li and Kaikui Liu and Xiangxiang Chu},
  booktitle = {NeurIPS},
  year = {2026}
}

Code: Apache 2.0. Benchmark annotations: CC BY 4.0. Source media and third-party models retain their respective licenses. Dataset metadata and source-media instructions accompany the versioned data release.