🚗 BATON

Behavioral Analysis of Transition and Operation in Naturalistic Driving

     

A large-scale multimodal benchmark for bidirectional human–DAS control transition in naturalistic driving
Submitted to ACM Multimedia 2026

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🎬 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}

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📊 Dataset at a Glance

🌍 Routes	👤 Drivers	🚙 Car Models	⏱️ Duration	🔄 Handover Events
380	127	84	136.6 h	2,892

🤖 DAS Driving	🧑 Human Driving	⬆️ DAS Handover	↩️ Human Takeover	🌍 Coverage
52.5%	47.5%	1,460	1,432	6 Continents

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

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🔬 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

📷 Front · Day 📷 Front · Night ⬆️ DAS Handover 🎥 Cabin · Day 🎥 Cabin · Night ↩️ Takeover

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

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🏆 Benchmark Tasks

Task	Description	Samples	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)

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📁 Repository Structure

BATON/
├── benchmark/                   # Benchmark data and generation code
│   ├── generate_benchmark.py        # Full benchmark construction pipeline
│   ├── routes.csv                   # Route metadata (380 routes)
│   ├── action_labels.csv            # 1 Hz action labels
│   ├── task1_action_samples.csv     # Task 1 samples
│   ├── task2_activation_samples_h{1,3,5}.csv   # Task 2 at 3 horizons
│   ├── task3_takeover_samples_h{1,3,5}.csv     # Task 3 at 3 horizons
│   ├── split_cross_driver.json      # Primary evaluation split
│   ├── split_cross_vehicle.json     # Cross-vehicle split
│   ├── split_random.json            # Random split
│   └── benchmark_protocol.md        # Detailed protocol specification
│
├── baseline/                    # Training and evaluation code
│   ├── config.py                    # Paths, modality definitions, hyperparameters
│   ├── dataset.py                   # PyTorch dataset for all tasks
│   ├── models.py                    # GRU and TCN with gated fusion
│   ├── metrics.py                   # Evaluation metrics
│   ├── train_nn.py                  # Neural network training (GRU / TCN)
│   ├── train_classical.py           # XGBoost and LR baselines
│   ├── run_vlm.py                   # Zero-shot VLM baselines (Gemini / GPT-4o)
│   ├── vlm_prompts.py               # VLM prompt construction
│   └── collect_results.py           # Aggregate and print result tables
│
└── data_processing/             # Feature extraction scripts
    ├── extract_front_video_features.py    # EfficientNet-B0 front video
    ├── extract_cabin_video_features.py    # EfficientNet-B0 cabin video
    ├── extract_clip_features.py           # CLIP ViT-B/32 features
    ├── video_utils.py                     # Shared video decoding utilities
    └── gps_semantic_enrichment.py         # GPS → road context features

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🚀 Quick Start

1. Get the data

# Sample dataset (~few GB, all modalities, 43 routes)
git lfs install
git clone https://huggingface.co/datasets/HenryYHW/BATON-Sample

# Full dataset — download via HuggingFace Hub
python -c "
from huggingface_hub import snapshot_download
snapshot_download('HenryYHW/BATON', repo_type='dataset', local_dir='./data')
"

2. Preprocess signals

cd baseline
python preprocess.py

3. Extract video features

cd data_processing

# EfficientNet-B0 features (used in main baselines)
python extract_front_video_features.py
python extract_cabin_video_features.py

# CLIP ViT-B/32 features (optional)
python extract_clip_features.py

4. Train baselines

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

# TCN ablation — Task 3, sensors only
python train_nn.py --task task3 --modality Text --model tcn --seed 42

# Zero-shot VLM baseline (GPT-4o or Gemini 2.5 Flash)
python run_vlm.py --model gpt4o --task task1
python run_vlm.py --model gemini --task task2

5. Collect results

python collect_results.py   # prints all result tables

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📐 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

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📡 Data Access

Resource	Link
📦 Full Dataset	HuggingFace — HenryYHW/BATON
🔍 Sample Dataset (43 routes)	HuggingFace — HenryYHW/BATON-Sample
📄 arXiv Paper	arxiv.org/abs/2604.07263

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📜 Citation

@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}
}

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📄 License

This dataset is released for academic research use only under CC 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.

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