--- 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 # 🚗 BATON-Sample ### **B**ehavioral **A**nalysis of **T**ransition and **O**peration in **N**aturalistic Driving

*A large-scale multimodal benchmark for bidirectional human–DAS control transition in naturalistic driving*
*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). ---

--- ## 🎬 Live Preview

_{Continuous sequence — cabin fisheye · front view · 5 fps}

_{8 CAN/IMU sensor streams
cycling through all channels}

_{Daytime time-lapse — front · cabin · 2-min intervals}

_{Nighttime driving — time-lapse with ⬆ DAS Handover and ↩ Human Takeover event highlights}

--- ## 📦 Sample Release at a Glance

| 🌍 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 |

> **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)

_{Global distribution of participants, per-driver duration, and handover event breakdown (full dataset).}

--- ## 📁 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 **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

_{Aligned multimodal streams around a HANDOVER event: cabin video · front video · GPS trajectory · sensor signals.}

--- ## 🏆 Benchmark Tasks

| 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. ---

_{🔗 Paper ·
Full Dataset ·
Sample Dataset ·
GitHub}