🚗💨 ADAS-TO

A Large-Scale Multimodal Naturalistic Dataset and Empirical Characterization of Human Takeovers during ADAS Engagement

15,705 real-world takeover events · 327 drivers · 163 vehicle models · 23 manufacturers

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When does a human driver take over from an ADAS? Why? How?

ADAS-TO captures the critical moment of control transition — the exact instant a driver decides the automation is no longer sufficient — across thousands of real-world scenarios with synchronized front-view video, vehicle dynamics, radar, and IMU data.

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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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🔍 Sample Data Preview

Example clip: On-coming Traffic scenario (HONDA CIVIC)

📋 meta.json — Clip Metadata

{
  "car_model": "HONDA_CIVIC",
  "dongle_id": "driver_085",
  "route_id": "route_001",
  "log_kind": "qlog",
  "log_hz": 10,
  "vid_kind": "qcamera",
  "camera_fps": 20,
  "clip_id": 0,
  "event_mono": 289529060357,
  "video_time_s": 256.0,
  "clip_start_s": 246.0,
  "clip_dur_s": 20.0
}

🚗 carState.csv — Vehicle dynamics & driver inputs (200 rows × 16 cols @ 10 Hz)

time_s	vEgo (m/s)	aEgo (m/s²)	steeringAngleDeg	steeringTorque	gasPressed	brakePressed	cruiseState.enabled
242.80	13.74	0.30	0.4	-132.0	False	False	True
242.90	13.80	0.52	0.5	-119.0	False	False	True
243.00	13.82	0.32	0.7	-115.0	False	False	True
...	...	...	...	...	...	...	...
~256.0	—	—	—	—	—	—	True → False
...	...	...	...	...	...	...	...
261.80	—	—	—	—	—	—	False

The cruiseState.enabled column transitions from True to False at the takeover moment (~10s into the clip).

🤖 controlsState.csv — ADAS controller state (200 rows × 12 cols @ 10 Hz)

Columns: logMonoTime, time_s, enabled, active, curvature, desiredCurvature, vCruise, vCruiseCluster, forceDecel, longControlState, alertText1, alertText2

📡 radarState.csv — Lead vehicle detection (80 rows × 19 cols @ 4 Hz)

Columns: logMonoTime, time_s, leadOne.status, leadOne.dRel, leadOne.vRel, leadOne.vLead, leadOne.aLeadK, leadTwo.dRel, leadTwo.vRel, leadTwo.vLead, leadTwo.aLeadK, ...

📂 All 10 files in each clip

File	Rows × Cols	Freq	Description
takeover.mp4	400 frames	20 fps	Front-camera video
meta.json	—	—	Clip metadata
carState.csv	200 × 16	10 Hz	Vehicle dynamics & driver inputs
controlsState.csv	200 × 12	10 Hz	ADAS controller state
carControl.csv	200 × 8	10 Hz	Control commands
carOutput.csv	200 × 9	10 Hz	Actuator outputs
drivingModelData.csv	200 × 7	10 Hz	Model predictions
radarState.csv	80 × 19	4 Hz	Radar / lead vehicle
accelerometer.csv	~200 × 4	~10 Hz	IMU data
longitudinalPlan.csv	200 × 8	10 Hz	Planner outputs

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📊 Dataset at a Glance

	Statistic	Value
🎥	Total takeover clips	15,705
👤	Unique drivers	327
🛣️	Unique driving routes	2,312
🚘	Vehicle models	163
🏭	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)
💾	Total size	~33 GB

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🔥 Why ADAS-TO?

"The takeover moment is the most safety-critical instant in human-automation interaction — yet it remains one of the least studied due to lack of data."

🏆 Unprecedented Scale

Over 15,000 real-world takeover events — orders of magnitude larger than existing datasets that typically contain hundreds of events captured in driving simulators.

🌍 Unmatched Diversity

163 vehicle models from 23 manufacturers including Tesla, Toyota, Honda, Hyundai, Ford, Volkswagen, Rivian, and more. From compact EVs to full-size trucks — spanning the full spectrum of modern ADAS implementations.

🎯 Rich Multimodal Signals

Every clip contains synchronized front-camera video, vehicle dynamics (speed, acceleration, steering), ADAS controller state, control commands, actuator outputs, driving model predictions, radar/lead vehicle data, and IMU measurements.

🌐 Real-World Naturalistic Data

Collected through online and offline autonomous driving communities with diverse real-world driving conditions — highways, urban streets, suburbs, varying weather and lighting. No simulators. No scripted scenarios. Pure naturalistic driving behavior.

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🎯 Use Cases

Application	Description
🔮 Takeover Prediction	Build early warning systems that predict when a driver will need to take over
🧠 Driver Behavior Modeling	Understand human responses during control transitions
📈 ADAS Performance Analysis	Compare disengagement patterns across vehicle types and ADAS systems
🤖 Autonomous Driving Safety	Train and evaluate safety-critical decision-making models
🧪 Human Factors Research	Study cognitive load, reaction times, and situational awareness
📊 Multimodal Time-Series	Develop forecasting and classification models on rich temporal data
🏗️ HMI Design	Design better human-machine interfaces for automated vehicles

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📁 Dataset Structure

ADAS-TO/
├── <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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🚘 Vehicle Coverage

23 Manufacturers · 163 Models · From Compact EVs to Full-Size Trucks

Top Vehicle Models by Clip Count

#	Vehicle Model	Clips		#	Vehicle Model	Clips
1	🏆 RIVIAN R1 GEN1	2,127		10	CHEVROLET BOLT EUV	244
2	🥈 ACURA MDX 3G	1,863		11	TOYOTA RAV4 TSS2	228
3	🥉 FORD F-150 MK14	1,226		12	RAM HD 5TH GEN	221
4	CHEVROLET SILVERADO	639		13	VOLKSWAGEN JETTA MK7	215
5	TOYOTA PRIUS	482		14	KIA EV6	209
6	HONDA CIVIC	470		15	VOLKSWAGEN GOLF MK7	192
7	TESLA MODEL 3	432		16	KIA NIRO EV	185
8	FORD MAVERICK MK1	300		17	HYUNDAI IONIQ 6	177
9	HYUNDAI IONIQ 5	266		18	VOLKSWAGEN ATLAS MK1	153

📋 All 23 Manufacturers (click to expand)

Acura · Audi · BYD · Chevrolet · Ford · Genesis · Honda · Hyundai · Jeep · Kia · Lexus · Mazda · Nissan · Porsche · RAM · Rivian · Skoda · Subaru · Tesla · Toyota · Volkswagen · Volvo

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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"
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())

# 🤖 Load ADAS controller state
controls = pd.read_csv(
    hf_hub_download(repo_id, f"{clip_path}/controlsState.csv", repo_type="dataset")
)

# 📡 Load radar data
radar = pd.read_csv(
    hf_hub_download(repo_id, f"{clip_path}/radarState.csv", repo_type="dataset")
)

Iterating Over All Clips

from huggingface_hub import HfApi

api = HfApi()
files = api.list_repo_files("HenryYHW/ADAS-TO", repo_type="dataset")
meta_files = [f for f in files if f.endswith("meta.json")]
print(f"Total clips: {len(meta_files)}")  # → 15,705

💾 Download the Full Dataset

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

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

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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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📖 Data Collection

ADAS-TO was built from naturalistic driving logs contributed by online and offline autonomous driving communities. Participating drivers voluntarily shared their driving data collected through various ADAS-equipped vehicles during everyday driving. The raw logs were processed through an automated pipeline to:

1. Detect ADAS disengagement events (ON→OFF transitions)
2. Extract synchronized video and CAN-bus signals within a 20-second window
3. Validate each clip for signal completeness and temporal alignment
4. Anonymize all driver and route identifiers

This community-driven collection approach enables unprecedented scale and diversity, capturing genuine driver behavior across a wide spectrum of vehicles, road types, and driving conditions.

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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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