🚗💨 ADAS-TO-Sample

10% Stratified Preview of ADAS-TO

~1,570 real-world takeover events · 163 vehicle models · 23 manufacturers

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📢 This is a 10% stratified sample for quick evaluation. For the full dataset (15,705 clips, ~33 GB), visit HenryYHW/ADAS-TO or contact henryyuhangwang@gmail.com.

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🎬 Takeover Examples

Each GIF shows ±3 seconds around the takeover moment — ADAS engaged → driver takes control

On-coming Traffic	Bridge	Night Driving	Sharp Curve
Surrounding Car	Traffic Light	Lane Change	Hard Brake

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📊 Dataset at a Glance

	Statistic	Value
🎥	Total takeover clips	~1,570 (10% sample)
🚘	Vehicle models	163 (all represented)
🏭	Manufacturers	23
⏱️	Clip duration	20 seconds (±10s around takeover)
📹	Video	Front-facing camera, 20 fps
📡	CAN / sensor signals	10–100 Hz
📁	Files per clip	10 (1 video + 1 meta + 8 CSV)
💾	Sample size	~3.3 GB
📦	Full dataset	15,705 clips, ~33 GB (ADAS-TO)

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🔍 Sampling Method

This sample was created using stratified sampling by vehicle model:

• Each of the 163 vehicle models contributes max(1, round(count × 0.1)) clips
• Every vehicle model is guaranteed at least 1 clip
• Clips are selected deterministically (sorted, evenly spaced)
• Same anonymization as the full dataset (driver_XXX, route_XXX)

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📁 Dataset Structure

ADAS-TO-Sample/
├── <CAR_MODEL>/                        # e.g., TOYOTA_PRIUS, TESLA_AP3_MODEL_3
│   └── <driver_XXX>/                   # 🔒 anonymized driver ID
│       └── <route_XXX>/                # 🔒 anonymized route ID
│           └── <clip_id>/              # integer (0-indexed per route)
│               ├── 🎥 takeover.mp4          20-second front-camera video
│               ├── 📋 meta.json             clip metadata & timing
│               ├── 🚗 carState.csv          vehicle dynamics & driver inputs
│               ├── 🤖 controlsState.csv     ADAS controller state & alerts
│               ├── 🎮 carControl.csv        lateral/longitudinal commands
│               ├── ⚙️ carOutput.csv          actuator outputs
│               ├── 🧠 drivingModelData.csv  model predictions & lane detection
│               ├── 📡 radarState.csv        lead vehicle radar data
│               ├── 📐 accelerometer.csv     IMU acceleration data
│               └── 📏 longitudinalPlan.csv  planner targets & FCW
└── ...

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📐 Takeover Event Definition

  ◄──────── 10 seconds ────────►◄──────── 10 seconds ────────►
  ┌──────────────────────────────┬──────────────────────────────┐
  │      🤖 ADAS ENGAGED         │      👤 MANUAL CONTROL        │
  │   (automation driving)       │   (driver takes over)        │
  └──────────────────────────────┴──────────────────────────────┘
                                 ▲
                            TAKEOVER EVENT
                         (ON → OFF transition)

A takeover event is detected as an ADAS ON → OFF transition satisfying:

Criterion	Value
ADAS engaged	controlsState.enabled OR cruiseState.enabled
Min ON duration	≥ 2 seconds before disengagement
Min OFF duration	≥ 2 seconds after disengagement
Gap merging	Transient gaps < 0.5s merged (filters sensor noise)
Clip window	±10 seconds centered on transition (20s total)

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📑 Data Fields Reference

📋 meta.json — Clip Metadata

Field	Type	Description
car_model	string	Vehicle model (e.g., TOYOTA_PRIUS)
dongle_id	string	Anonymized driver ID (driver_XXX)
route_id	string	Anonymized route ID (route_XXX)
log_kind	string	Log resolution: qlog (10 Hz) or rlog (100 Hz)
log_hz	int	CAN signal sampling rate
vid_kind	string	Camera source type
camera_fps	int	Video frame rate (20 fps)
clip_id	int	Clip index within route (0-indexed)
event_mono	int	Monotonic timestamp of takeover (ns)
video_time_s	float	Takeover time within full route video (s)
clip_start_s	float	Clip start time within route (s)
clip_dur_s	float	Clip duration (s)

🚗 carState.csv — Vehicle Dynamics & Driver Inputs

Column	Unit	Description
vEgo	m/s	Ego vehicle speed
aEgo	m/s²	Ego vehicle acceleration
steeringAngleDeg	deg	Steering wheel angle
steeringTorque	N·m	Driver steering torque
steeringPressed	bool	Driver actively steering
gasPressed	bool	Gas pedal pressed
brakePressed	bool	Brake pedal pressed
cruiseState.enabled	bool	Cruise / ADAS engaged

🤖 controlsState.csv — ADAS Controller

Column	Unit	Description
enabled	bool	ADAS system enabled
active	bool	ADAS actively controlling vehicle
curvature	1/m	Current path curvature
desiredCurvature	1/m	Target curvature from planner
vCruise	m/s	Set cruise speed
longControlState	enum	Longitudinal control state
alertText1	string	Primary driver alert
alertText2	string	Secondary driver alert

🎮 carControl.csv — Control Commands

Column	Unit	Description
latActive	bool	Lateral control active
longActive	bool	Longitudinal control active
actuators.accel	m/s²	Commanded acceleration
actuators.torque	N·m	Commanded steering torque
actuators.curvature	1/m	Commanded path curvature

⚙️ carOutput.csv — Actuator Outputs

Column	Description
actuatorsOutput.accel	Acceleration actuator output
actuatorsOutput.brake	Brake actuator output
actuatorsOutput.gas	Gas actuator output
actuatorsOutput.steer	Steering actuator output
actuatorsOutput.steerOutputCan	Raw CAN steering output
actuatorsOutput.steeringAngleDeg	Steering angle output (deg)

🧠 drivingModelData.csv — Driving Model Predictions

Column	Description
action.desiredCurvature	Model-predicted desired curvature
action.desiredAcceleration	Model-predicted desired acceleration
laneLineMeta.leftProb	Left lane line detection probability
laneLineMeta.rightProb	Right lane line detection probability

📡 radarState.csv — Lead Vehicle Detection

Column	Unit	Description
leadOne.dRel	m	Distance to primary lead vehicle
leadOne.vRel	m/s	Relative velocity of lead
leadOne.vLead	m/s	Absolute velocity of lead
leadOne.aLeadK	m/s²	Lead vehicle acceleration
leadTwo.*	—	Secondary lead vehicle (same fields)

📐 accelerometer.csv — IMU Data

Column	Unit	Description
acceleration.v	m/s²	3-axis acceleration vector
timestamp	—	Sensor timestamp

📏 longitudinalPlan.csv — Planner Outputs

Column	Unit	Description
aTarget	m/s²	Target acceleration
hasLead	bool	Lead vehicle detected
fcw	bool	Forward collision warning active
speeds[]	m/s	Planned speed profile
accels[]	m/s²	Planned acceleration profile

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🚀 Quick Start

Loading a Single Clip

import json
import pandas as pd
from huggingface_hub import hf_hub_download

repo_id = "HenryYHW/ADAS-TO-Sample"
clip_path = "TOYOTA_PRIUS/driver_001/route_001/0"

# 📋 Download metadata
meta_path = hf_hub_download(repo_id, f"{clip_path}/meta.json", repo_type="dataset")
with open(meta_path) as f:
    meta = json.load(f)

# 🚗 Load vehicle state signals
car_state = pd.read_csv(
    hf_hub_download(repo_id, f"{clip_path}/carState.csv", repo_type="dataset")
)
print(car_state[["vEgo", "aEgo", "steeringAngleDeg", "brakePressed"]].describe())

💾 Download the Sample

# Using huggingface-cli (recommended)
huggingface-cli download HenryYHW/ADAS-TO-Sample --repo-type dataset --local-dir ./ADAS-TO-Sample

# Using git-lfs
git lfs install
git clone https://huggingface.co/datasets/HenryYHW/ADAS-TO-Sample

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📦 Full Dataset

This is a 10% preview. The full ADAS-TO dataset contains:

• 15,705 takeover clips
• 327 unique drivers
• 2,312 unique routes
• ~33 GB total size

👉 Access the full dataset: HenryYHW/ADAS-TO

For questions or full dataset access, contact: henryyuhangwang@gmail.com

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🔒 Privacy & Ethics

• Anonymized identifiers: All driver and route IDs are replaced with anonymous tokens (driver_XXX, route_XXX)
• Forward-view only: Video captures road-facing view only — no cabin or driver footage
• No PII: No personally identifiable information is included in any data file
• Community-sourced: Data collected through autonomous driving enthusiast communities with informed participation

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📝 Citation

If you use ADAS-TO in your research, please cite:

@dataset{adas_to_2026,
  title     = {ADAS-TO: A Large-Scale Multimodal Naturalistic Dataset and
               Empirical Characterization of Human Takeovers during ADAS Engagement},
  author    = {Anonymous Authors},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/HenryYHW/ADAS-TO}
}

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📄 License

This dataset is released under CC BY-NC 4.0.

For academic and non-commercial research purposes.

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