VLAlert-Bench (v1)
A unified benchmark for driving-alert decision making.
VLAlert-Bench integrates six driving-event datasets — Nexar Collision, DoTA, DAD, DADA-2000, ADAS-TO-Critic, and the Kaggle ACCIDENT @ CVPR 2026 challenge — into a single per-tick prediction task with three actions: SILENT (0) / OBSERVE (1) / ALERT (2).
At each 1 Hz tick a model observes the last 8 frames of a video and must output one of three actions. Labels are derived from each source dataset's event-time annotations using a uniform 2 s ALERT / 4 s OBSERVE window around the event onset.
What's hosted here. Five 1 Hz tick parquets, per-frame action labels, per-video split manifests, the ADAS-TO-Critic mp4 corpus (1.6 GB, full source), and a HuggingFace loader. Nexar / DoTA / DAD / DADA-2000 / Kaggle ACCIDENT videos are not redistributed — see "How to load" below for download links.
At a glance
| Train | Val | Test | Extra: ADAS-TO | Extra: ACCIDENT | Total | |
|---|---|---|---|---|---|---|
| Videos | 6,406 | 1,219 | 2,647 | 1,051 | 2,211 | 13,534 |
| Ticks (1 Hz) | 97,649 | 11,220 | 23,661 | 21,020 | 39,342 | 192,892 |
A tick is a 1-second sliding-window record carrying 8 consecutive frame indices plus the action label at the window's last frame.
Per-source video counts
| Source | Train | Val | Test | Extra: ADAS-TO | Extra: ACCIDENT | Native source |
|---|---|---|---|---|---|---|
| Nexar Collision | 1,500 | 667 | 677 | — | — | Kaggle (Nexar Collision Prediction Challenge 2024) |
| DoTA | 2,949 | 326 | 1,402 | — | — | Detection of Traffic Anomaly (Yao et al. 2022) |
| DAD | 1,157 | 127 | 466 | — | — | Dashcam Accident Dataset (Chan et al. 2016) |
| DADA-2000 | 798 | 99 | 102 | — | — | Driver Attention in Accidents (Fang et al. 2022) |
| ADAS-TO-Critic | — | — | — | 1,051 | — | Critical takeover scenarios (this work; videos co-hosted) |
| Kaggle ACCIDENT | — | — | — | — | 2,211 | Kaggle ACCIDENT @ CVPR 2026 (Picek et al. 2026) |
Per-source tick counts (1 Hz sliding window)
| Source | Train | Val | Test | Extra: ADAS-TO | Extra: ACCIDENT |
|---|---|---|---|---|---|
| Nexar Collision | 56,948 | 6,721 | 6,831 | — | — |
| DoTA | 29,763 | 3,256 | 14,103 | — | — |
| DAD | 4,628 | 508 | 1,864 | — | — |
| DADA-2000 | 6,310 | 735 | 863 | — | — |
| ADAS-TO-Critic | — | — | — | 21,020 | — |
| Kaggle ACCIDENT | — | — | — | — | 39,342 |
| Total | 97,649 | 11,220 | 23,661 | 21,020 | 39,342 |
Action-label distribution (per split)
| Split | SILENT | OBSERVE | ALERT |
|---|---|---|---|
| train | 83.3% | 7.2% | 9.5% |
| val | 86.5% | 5.6% | 8.0% |
| test | 77.8% | 9.1% | 13.1% |
| extra_val_adasto | 80.0% | 10.0% | 10.0% |
| extra_val_accident | 77.9% | 10.8% | 11.2% |
Category distribution (public-facing schema)
We expose three clip-level categories: positive (an event
occurs), negative (no event), mixed (continuous human-takeover
clips with both alert and silent segments). Per-frame action labels
remain the primary supervision target.
| Split | positive | negative | mixed |
|---|---|---|---|
| train | 66,686 | 30,963 | — |
| val | 7,571 | 3,649 | — |
| test | 19,066 | 4,595 | — |
| extra_val_adasto | — | — | 21,020 |
| extra_val_accident | 39,342 | — | — |
Splits
| Split | Purpose |
|---|---|
train |
In-domain training (Nexar + DoTA + DAD + DADA-2000). Stratified, leakage-free. |
val |
In-domain validation for model selection. |
test |
In-domain held-out test (each source's native test split, untouched). |
extra_val_adasto |
Held-out OOD — full ADAS-TO-Critic corpus. Never used for training or selection. |
extra_val_accident |
Held-out OOD — Kaggle ACCIDENT @ CVPR 2026 challenge clips. |
All five splits are video-disjoint
(stats/leakage_report.json — max overlap = 0).
Source datasets, licenses, and how to obtain the videos
| Source | Videos hosted here? | Where to obtain | License |
|---|---|---|---|
| Nexar Collision | ✗ annotations only | https://www.kaggle.com/competitions/nexar-collision-prediction | Kaggle competition terms (non-commercial use) |
| DoTA | ✗ annotations only | https://github.com/MoonBlvd/Detection-of-Traffic-Anomaly | Research-only |
| DAD | ✗ annotations only | http://aliensunmin.github.io/project/dashcam/ | Research-only |
| DADA-2000 | ✗ annotations only | https://github.com/JWFangit/LOTVS-DADA | Research-only |
| ADAS-TO-Critic | ✓ full mp4s (1.6 GB) | This repository, adasto_critic_videos/ |
CC-BY-NC-4.0 (this work) |
| Kaggle ACCIDENT | ✗ annotations only | https://www.kaggle.com/competitions/accident | Kaggle competition terms |
ADAS-TO-Critic videos are mirrored in this repository under
adasto_critic_videos/so the OOD evaluation can be reproduced end-to-end without further downloads.
How to load
Read the parquet directly (no install of datasets needed)
import pandas as pd
val = pd.read_parquet("hf://datasets/HenryYHW/VLAlert/data/val.parquet")
print(val.head())
print(val.tick_label.value_counts()) # 0=SILENT 1=OBSERVE 2=ALERT
Use the HuggingFace datasets loader
from datasets import load_dataset
ds = load_dataset("HenryYHW/VLAlert", split="validation")
print(ds[0])
# {'video_id': 'nexar_00002',
# 'source': 0, # ClassLabel: nexar
# 'category': 0, # ClassLabel: positive
# 'frame_indices': [...8 ints], # window of consecutive frame indices
# 'tta_raw': 5.13, # seconds-to-event at last frame
# 'tick_label': 1, # ClassLabel: OBSERVE
# 'video_path': 'NEXAR_COLLISION/test-public/positive/00002.mp4',
# ...}
ds_adasto = load_dataset("HenryYHW/VLAlert", split="extra_val_adasto")
ds_kaggle = load_dataset("HenryYHW/VLAlert", split="extra_val_accident")
Materialize frames from a local copy of the source videos
import cv2
def load_window(record, root="/path/to/your/source-dataset-root"):
cap = cv2.VideoCapture(f"{root}/{record['video_path']}")
frames = []
for fi in record["frame_indices"]:
cap.set(cv2.CAP_PROP_POS_FRAMES, fi)
ok, frame = cap.read()
if ok:
frames.append(frame)
cap.release()
return frames
For ADAS-TO-Critic, the corresponding mp4s live in the repo at
adasto_critic_videos/<video_id>.mp4 — pull them with the HF Hub or
git lfs.
Label generation rules
For each clip with an event time t_event (seconds since clip start),
per-frame labels are assigned as:
| Window relative to t_event | Label |
|---|---|
t < t_event − 4 |
SILENT |
t_event − 4 ≤ t < t_event − 2 |
OBSERVE |
t_event − 2 ≤ t < t_event |
ALERT |
t ≥ t_event (post-event) |
SILENT |
(any frame of a negative clip) |
SILENT |
Source-specific event time:
| Source | t_event (seconds) |
|---|---|
| Nexar | time_of_event from per-folder metadata.csv |
| DoTA | anomaly_start (frames) ÷ 10 fps |
| DAD | fixed t_event = 4.0 (videos are 4 s leading directly into the accident) |
| DADA-2000 | accident_time (frames) ÷ 30 fps from per-clip annotation.json |
| ADAS-TO-Critic | fixed t_event = 10.0 (uniform 20 s clips centred on the takeover request) |
| Kaggle ACCIDENT | t_takeover from takeover_manifest_b50.csv |
Each tick is a 1 Hz slide of an 8-frame window. The tick label is the per-frame label at the last frame of the window.
File layout
HenryYHW/VLAlert/
├── README.md ← this file
├── vlalert_bench.py ← HF GeneratorBasedBuilder loader
├── dataset_infos.json ← lightweight metadata
├── manifest/
│ ├── video_split.json ← all 13,534 videos, full schema
│ ├── nexar_split.json
│ ├── dota_split.json
│ ├── dad_split.json
│ ├── dada_split.json
│ ├── adasto_critic_split.json
│ └── accident_split.json
├── labels/
│ ├── train_perframe.json ← per-video per-frame labels
│ ├── val_perframe.json
│ ├── test_perframe.json
│ ├── extra_val_adasto_perframe.json
│ └── extra_val_accident_perframe.json
├── data/
│ ├── train.parquet ← per-tick records (primary training input)
│ ├── val.parquet
│ ├── test.parquet
│ ├── extra_val_adasto.parquet
│ └── extra_val_accident.parquet
├── adasto_critic_videos/ ← 1,051 mp4 clips (ADAS-TO-Critic full source)
└── stats/
├── per_source_video_count.csv
└── leakage_report.json
Reproducibility
All split assignments are deterministic given the source datasets
(seed = 42; 10 % of each native training set carved into val).
To regenerate from scratch:
python tools/build_unified_benchmark.py --step all
Citations
Primary
@misc{wang2026vlalertbench,
author = {Wang, Yuhang and Zhou, Hao},
title = {VLAlert-Bench: A Unified Benchmark for Driving-Alert Decisions},
year = {2026},
url = {https://huggingface.co/datasets/HenryYHW/VLAlert}
}
Source-dataset attribution (please cite the ones you use)
@misc{nexar2024collision,
author = {{Nexar}},
title = {Nexar Collision Prediction Challenge},
year = {2024},
howpublished = {\url{https://www.kaggle.com/competitions/nexar-collision-prediction}},
note = {Kaggle competition}
}
@inproceedings{yao2022dota,
title = {{DoTA}: Unsupervised Detection of Traffic Anomaly in Driving Videos},
author = {Yao, Yu and Wang, Xizi and Xu, Mingze and Pu, Zelin and Wang, Yuchen and Atkins, Ella and Crandall, David J.},
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
year = {2022}
}
@inproceedings{chan2016dad,
title = {Anticipating Accidents in Dashcam Videos},
author = {Chan, Fu-Hsiang and Chen, Yu-Ting and Xiang, Yu and Sun, Min},
booktitle = {Asian Conference on Computer Vision (ACCV)},
year = {2016}
}
@article{fang2022dada,
title = {{DADA}-2000: Can Driving Accident be Predicted by Driver Attention? Analyzed by a Benchmark},
author = {Fang, Jianwu and Yan, Dingxin and Qiao, Jiahuan and Xue, Jianru and Yu, Hongkai},
journal = {IEEE Transactions on Intelligent Transportation Systems},
year = {2022}
}
@misc{accident2026cvpr,
author = {Picek, Lukas and {\v{C}}erm{\'a}k, Vojt{\v{e}}ch and Hanzl, Marek and {\v{C}}erm{\'a}k, Michal},
title = {{ACCIDENT} @ {CVPR}},
year = {2026},
howpublished = {\url{https://kaggle.com/competitions/accident}},
note = {Kaggle}
}
@misc{adastocritic2026,
author = {Wang, Yuhang and Zhou, Hao},
title = {{ADAS-TO-Critic}: Critical Takeover Scenarios for Driver-Alert Evaluation},
year = {2026},
note = {Released as part of VLAlert-Bench, this repository},
url = {https://huggingface.co/datasets/HenryYHW/VLAlert}
}
Related methodology
@article{kaelbling1998planning,
title = {Planning and Acting in Partially Observable Stochastic Domains},
author = {Kaelbling, Leslie Pack and Littman, Michael L. and Cassandra, Anthony R.},
journal = {Artificial Intelligence},
volume = {101}, number = {1-2}, year = {1998}
}
@inproceedings{lee2019set,
title = {Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks},
author = {Lee, Juho and Lee, Yoonho and Kim, Jungtaek and Kosiorek, Adam R. and Choi, Seungjin and Teh, Yee Whye},
booktitle = {International Conference on Machine Learning (ICML)},
year = {2019}
}
@inproceedings{cho2014gru,
title = {Learning Phrase Representations using {RNN} Encoder--Decoder for Statistical Machine Translation},
author = {Cho, Kyunghyun and van Merri{\"e}nboer, Bart and Gulcehre, Caglar and Bahdanau, Dzmitry and Bougares, Fethi and Schwenk, Holger and Bengio, Yoshua},
booktitle = {EMNLP},
year = {2014}
}
@inproceedings{hu2022lora,
title = {{LoRA}: Low-Rank Adaptation of Large Language Models},
author = {Hu, Edward J. and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu},
booktitle = {ICLR},
year = {2022}
}
Acknowledgments
We thank the maintainers of Nexar, DoTA, DAD, DADA-2000, and the organizers of the Kaggle ACCIDENT @ CVPR 2026 challenge for releasing their data. This work was supported in part by the University of South Florida.
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