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Datasets:
HenryYHW
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BATON-Sample

Tasks:
Video Classification
Time Series Forecasting
Modalities:
Video
Languages:
English
ArXiv:
Tags:
driving
autonomous-driving
multimodal
handover
benchmark
naturalistic-driving
License:
Dataset card Data Studio Files
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BATON-Sample / README.md
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---
license: cc-by-nc-4.0
task_categories:
- video-classification
- time-series-forecasting
language:
- en
tags:
- driving
- autonomous-driving
- multimodal
- handover
- benchmark
- naturalistic-driving
pretty_name: BATON-Sample
size_categories:
- 100G<n<1T
---
- time-series-forecasting
language:
- en
tags:
- driving
- autonomous-driving
- multimodal
- handover
- benchmark
- naturalistic-driving
pretty_name: BATON-Sample
size_categories:
- 100G<n<1T
<div align="center">
# 🚗 BATON-Sample
### **B**ehavioral **A**nalysis of **T**ransition and **O**peration in **N**aturalistic Driving
<p>
 <a href="https://arxiv.org/abs/2604.07263">
 <img src="https://img.shields.io/badge/arXiv-2604.07263-b31b1b.svg?style=for-the-badge&logo=arxiv"/>
 </a>
 &nbsp;
 <a href="https://huggingface.co/datasets/HenryYHW/BATON">
 <img src="https://img.shields.io/badge/🤗-Full_Dataset-ffd21e?style=for-the-badge"/>
 </a>
 &nbsp;
 <a href="https://huggingface.co/datasets/HenryYHW/BATON-Sample">
 <img src="https://img.shields.io/badge/🤗-Sample_Dataset-ffd21e?style=for-the-badge"/>
 </a>
 &nbsp;
 <a href="https://creativecommons.org/licenses/by-nc/4.0/">
 <img src="https://img.shields.io/badge/License-CC_BY--NC_4.0-lightgrey.svg?style=for-the-badge"/>
 </a>
</p>
*A large-scale multimodal benchmark for bidirectional human–DAS control transition in naturalistic driving*<br/>
*Submitted to ACM Multimedia 2026*
> **This is the sample release of BATON** — 43 routes covering all 9 modalities, ready for quick exploration and prototyping. For the full 380-route dataset, see [HenryYHW/BATON](https://huggingface.co/datasets/HenryYHW/BATON).
</div>
<div align="center">
 <img src="https://huggingface.co/datasets/HenryYHW/BATON/resolve/main/figs/teaser.png" width="100%"/>
</div>
## 🎬 Live Preview
<table width="100%">
<tr>
 <td width="38%" align="center" valign="top">
 <img src="https://huggingface.co/datasets/HenryYHW/BATON/resolve/main/figs/gif_fig1.gif" width="100%"/>
 <br/><sub><b>Continuous sequence</b> — cabin fisheye · front view · 5 fps</sub>
 </td>
 <td width="24%" align="center" valign="top">
 <img src="https://huggingface.co/datasets/HenryYHW/BATON/resolve/main/figs/gif_sensors.gif" width="100%"/>
 <br/><sub><b>8 CAN/IMU sensor streams</b><br/>cycling through all channels</sub>
 </td>
 <td width="38%" align="center" valign="top">
 <img src="https://huggingface.co/datasets/HenryYHW/BATON/resolve/main/figs/gif_fig3.gif" width="100%"/>
 <br/><sub><b>Daytime time-lapse</b> — front · cabin · 2-min intervals</sub>
 </td>
</tr>
</table>
<div align="center">
 <img src="https://huggingface.co/datasets/HenryYHW/BATON/resolve/main/figs/gif_fig2.gif" width="72%"/>
 <br/>
 <sub><b>Nighttime driving</b> — time-lapse with <b>⬆ DAS Handover</b> and <b>↩ Human Takeover</b> event highlights</sub>
</div>
## 📦 Sample Release at a Glance
<div align="center">
| 🌍 Routes | 👤 Drivers | 🚙 Car Models | ⏱️ Duration | 🔄 Handover Events |
|:---------:|:----------:|:-------------:|:-----------:|:-----------------:|
| **43** | **43** | **~20** | **~15 h** | **~330** |
| 🤖 DAS Driving | 🧑 Human Driving | ⬆️ DAS Handover | ↩️ Human Takeover | 🌍 Coverage |
|:--------------:|:----------------:|:---------------:|:-----------------:|:-----------:|
| ~52% | ~48% | ~165 | ~165 | 5 Continents |
</div>
> **Full dataset:** 380 routes · 127 drivers · 84 car models · 136.6 h · 2,892 handover events — available at [HenryYHW/BATON](https://huggingface.co/datasets/HenryYHW/BATON)
<div align="center">
 <img src="https://huggingface.co/datasets/HenryYHW/BATON/resolve/main/figs/DatasetOverview.jpg" width="88%"/>
 <br/><sub><i>Global distribution of participants, per-driver duration, and handover event breakdown (full dataset).</i></sub>
</div>
## 📁 Sample Contents
Each of the 43 routes contains all 9 synchronized modalities:
```
BATON-Sample/
└── {vehicle_model}/
 └── {driver_id}/
 └── {route_hash}/
 ├── vehicle_dynamics.csv # Speed, accel, steering, pedals, DAS status
 ├── planning.csv # DAS curvature, lane change intent
 ├── radar.csv # Lead vehicle distance & relative speed
 ├── driver_state.csv # Face pose, eye openness, awareness
 ├── imu.csv # 3-axis accel & gyro at 100 Hz
 ├── gps.csv # Coordinates, heading
 ├── localization.csv # Road curvature, lane position
 ├── qcamera.mp4 # Front-view video (526×330, H.264, 20 fps)
 └── dcamera.mp4 # In-cabin fisheye video (1928×1208, HEVC, 20 fps)
```
## 🔬 Data Collection & Modalities
<table>
<tr>
<td width="42%" valign="top">
**Setup:** Non-intrusive plug-and-play OBD-II dongle + dual cameras. Drivers use their own vehicles during real daily commutes — no lab, no script.
| Component | Spec |
|-----------|------|
| 📡 OBD-II Dongle | CAN-bus at 100 Hz |
| 📷 Front camera | 526×330 · H.264 · 20 fps |
| 🎥 Cabin fisheye | 1928×1208 · HEVC · 20 fps |
| 🛰️ GPS | 10 Hz |
**9 synchronized modalities:**
- `vehicle_dynamics.csv` — speed, accel, steering, pedals, DAS status
- `planning.csv` — DAS curvature, lane change intent
- `radar.csv` — lead vehicle distance & relative speed
- `driver_state.csv` — face pose, eye openness, awareness
- `imu.csv` — 3-axis accel & gyro at 100 Hz
- `gps.csv` — coordinates, heading
- `localization.csv` — road curvature, lane position
- `qcamera.mp4` — front-view video
- `dcamera.mp4` — in-cabin fisheye video
</td>
<td width="58%" valign="top">
<img src="https://huggingface.co/datasets/HenryYHW/BATON/resolve/main/figs/experiment_method.jpg" width="100%"/>
<table>
<tr>
 <td align="center"><img src="https://huggingface.co/datasets/HenryYHW/BATON/resolve/main/figs/qcamera_day.jpg" width="100%"/><br/><sub>📷 Front · Day</sub></td>
 <td align="center"><img src="https://huggingface.co/datasets/HenryYHW/BATON/resolve/main/figs/qcamera_night.jpg" width="100%"/><br/><sub>📷 Front · Night</sub></td>
 <td align="center"><img src="https://huggingface.co/datasets/HenryYHW/BATON/resolve/main/figs/qcamera_activation.jpg" width="100%"/><br/><sub>⬆️ DAS Handover</sub></td>
</tr>
<tr>
 <td align="center"><img src="https://huggingface.co/datasets/HenryYHW/BATON/resolve/main/figs/dcamera_day.jpg" width="100%"/><br/><sub>🎥 Cabin · Day</sub></td>
 <td align="center"><img src="https://huggingface.co/datasets/HenryYHW/BATON/resolve/main/figs/dcamera_night.jpg" width="100%"/><br/><sub>🎥 Cabin · Night</sub></td>
 <td align="center"><img src="https://huggingface.co/datasets/HenryYHW/BATON/resolve/main/figs/dcamera_takeover.jpg" width="100%"/><br/><sub>↩️ Human Takeover</sub></td>
</tr>
</table>
</td>
</tr>
</table>
<div align="center">
 <img src="https://huggingface.co/datasets/HenryYHW/BATON/resolve/main/figs/BenchmarkOverview.jpg" width="100%"/>
 <br/><sub><i>Aligned multimodal streams around a HANDOVER event: cabin video · front video · GPS trajectory · sensor signals.</i></sub>
</div>
## 🏆 Benchmark Tasks
<div align="center">
 <img src="https://huggingface.co/datasets/HenryYHW/BATON/resolve/main/figs/TaskDistribution.jpg" width="80%"/>
</div>
<br/>
| Task | Description | Samples (full) | Labels | Primary Metric |
|------|-------------|:--------------:|--------|:--------------:|
| 🎯 **Task 1** | Driving action recognition (7-class) | 979,809 | Cruising · Car Following · Accelerating · Braking · Lane Change · Turning · Stopped | Macro-F1 |
| ⬆️ **Task 2** | Handover prediction (Human→DAS) | 56,564 | Handover (14.9%) · No Handover | AUPRC |
| ↩️ **Task 3** | Takeover prediction (DAS→Human) | 71,079 | Takeover (11.9%) · No Takeover | AUPRC |
> **Evaluation protocol:** Cross-driver split · 5-second input window · 3-second prediction horizon · 3 seeds (42, 123, 7)
## 🚀 Quick Start
### 1. Get the sample data
```bash
# Clone this sample dataset (~few GB, all modalities, 43 routes)
git lfs install
git clone https://huggingface.co/datasets/HenryYHW/BATON-Sample
# Or via Python
from huggingface_hub import snapshot_download
snapshot_download('HenryYHW/BATON-Sample', repo_type='dataset', local_dir='./data')
```
### 2. Get the full dataset
```bash
# Full dataset (380 routes) — requires HuggingFace account
python -c "
from huggingface_hub import snapshot_download
snapshot_download('HenryYHW/BATON', repo_type='dataset', local_dir='./data')
"
```
### 3. Extract video features
```bash
cd data_processing
# EfficientNet-B0 features (used in main baselines)
python extract_front_video_features.py
python extract_cabin_video_features.py
```
### 4. Train baselines (requires full dataset + benchmark files)
```bash
cd baseline
# GRU on all modalities — Task 1
python train_nn.py --task task1 --modality Full-All --model gru --seed 42
# XGBoost on structured signals — Task 2
python train_classical.py --task task2 --model xgb --seed 42
# Zero-shot VLM baseline (GPT-4o or Gemini 2.5 Flash)
python run_vlm.py --model gpt4o --task task1
```
See [GitHub — OpenLKA/BATON](https://github.com/OpenLKA/BATON) for the complete codebase.
## 📐 Evaluation Protocol
| Setting | Value |
|---------|-------|
| **Primary split** | Cross-driver (disjoint drivers in train / val / test) |
| **Additional splits** | Cross-vehicle, Random |
| **Input window** | 5 seconds |
| **Prediction horizon** | 1 s, 3 s, 5 s (main: **3 s**) |
| **Random seeds** | 42, 123, 7 — report 3-seed average |
| **Task 1 metric** | Macro-F1 |
| **Task 2 / 3 metrics** | AUPRC (primary), AUC-ROC, F1 |
## 📡 Data Access
| Resource | Link |
|----------|------|
| 🔍 **This Sample** (43 routes) | [HuggingFace — HenryYHW/BATON-Sample](https://huggingface.co/datasets/HenryYHW/BATON-Sample) |
| 📦 Full Dataset (380 routes) | [HuggingFace — HenryYHW/BATON](https://huggingface.co/datasets/HenryYHW/BATON) |
| 💻 Code & Baselines | [GitHub — OpenLKA/BATON](https://github.com/OpenLKA/BATON) |
| 📄 arXiv Paper | [arxiv.org/abs/2604.07263](https://arxiv.org/abs/2604.07263) |
## 📜 Citation
```bibtex
@article{wang2026baton,
 title = {BATON: A Multimodal Benchmark for Bidirectional Automation Transition
 Observation in Naturalistic Driving},
 author = {Wang, Yuhang and Xu, Yiyao and Yang, Chaoyun and Li, Lingyao
 and Sun, Jingran and Zhou, Hao},
 journal = {arXiv preprint arXiv:2604.07263},
 year = {2026}
}
```
## 📄 License
This dataset is released for **academic research use only** under [**CC BY-NC 4.0**](https://creativecommons.org/licenses/by-nc/4.0/) (Creative Commons Attribution–NonCommercial 4.0 International).
**You are free to** use and redistribute the data for non-commercial research, and to adapt or build upon it for non-commercial purposes — **provided that:**
- **Attribution** — You must cite the BATON paper (see Citation above) in any publication or work that uses this dataset.
- **Non-Commercial** — Commercial use of this dataset or any derivative is **strictly prohibited**.
- **Academic Use Only** — This dataset is intended solely for academic research. Use in any commercial product, service, or application is not permitted.
For commercial licensing inquiries, please contact the authors.
<div align="center">
 <sub>
 🔗 <a href="https://arxiv.org/abs/2604.07263">Paper</a> &nbsp;·&nbsp;
 <a href="https://huggingface.co/datasets/HenryYHW/BATON">Full Dataset</a> &nbsp;·&nbsp;
 <a href="https://huggingface.co/datasets/HenryYHW/BATON-Sample">Sample Dataset</a> &nbsp;·&nbsp;
 <a href="https://github.com/OpenLKA/BATON">GitHub</a>
 </sub>
</div>