Datasets:
Update dataset card: rename to ADAS-TO, update GIF labels, anonymous authors
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README.md
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---
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license: cc-by-nc-4.0
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task_categories:
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- time-series-forecasting
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- video-classification
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tags:
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- autonomous-driving
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- ADAS
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- takeover
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- driver-behavior
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- time-series
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- multimodal
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- CAN-bus
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- vehicle-dynamics
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- driving-safety
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- human-factors
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size_categories:
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- 10K<n<100K
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language:
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- en
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pretty_name:
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#
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[](https://creativecommons.org/licenses/by-nc/4.0/)
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[](https://huggingface.co/datasets/HenryYHW/ADAS-TO)
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[]()
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[]()
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[]()
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[]()
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---
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**When does a human driver take over from an ADAS?** **Why?** **How?**
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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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</div>
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---
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## 🎬 Takeover Examples
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<div align="center">
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*Each GIF shows ±3 seconds around the takeover moment — ADAS engaged → driver takes control*
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<table>
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<tr>
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<td align="center"><img src="https://huggingface.co/datasets/HenryYHW/ADAS-TO/resolve/main/assets/takeover_1.gif" width="240"/><br/><sub>On-coming Traffic</sub></td>
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<td align="center"><img src="https://huggingface.co/datasets/HenryYHW/ADAS-TO/resolve/main/assets/takeover_2.gif" width="240"/><br/><sub>Bridge</sub></td>
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<td align="center"><img src="https://huggingface.co/datasets/HenryYHW/ADAS-TO/resolve/main/assets/takeover_3.gif" width="240"/><br/><sub>Night Driving</sub></td>
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<td align="center"><img src="https://huggingface.co/datasets/HenryYHW/ADAS-TO/resolve/main/assets/takeover_4.gif" width="240"/><br/><sub>Sharp Curve</sub></td>
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</tr>
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<tr>
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<td align="center"><img src="https://huggingface.co/datasets/HenryYHW/ADAS-TO/resolve/main/assets/takeover_5.gif" width="240"/><br/><sub>Surrounding Car</sub></td>
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<td align="center"><img src="https://huggingface.co/datasets/HenryYHW/ADAS-TO/resolve/main/assets/takeover_6.gif" width="240"/><br/><sub>Traffic Light</sub></td>
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<td align="center"><img src="https://huggingface.co/datasets/HenryYHW/ADAS-TO/resolve/main/assets/takeover_7.gif" width="240"/><br/><sub>Lane Change</sub></td>
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<td align="center"><img src="https://huggingface.co/datasets/HenryYHW/ADAS-TO/resolve/main/assets/takeover_8.gif" width="240"/><br/><sub>Hard Brake</sub></td>
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</tr>
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</table>
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</div>
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---
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## 📊 Dataset at a Glance
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<div align="center">
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| | Statistic | Value |
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|:---:|:---|:---|
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| 🎥 | **Total takeover clips** | **15,705** |
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| 👤 | **Unique drivers** | **327** |
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| 🛣️ | **Unique driving routes** | **2,312** |
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| 🚘 | **Vehicle models** | **163** |
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| 🏭 | **Manufacturers** | **23** |
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| ⏱️ | **Clip duration** | **20 seconds** (±10s around takeover) |
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| 📹 | **Video** | Front-facing camera, **20 fps** |
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| 📡 | **CAN / sensor signals** | **10–100 Hz** |
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| 📁 | **Files per clip** | **10** (1 video + 1 meta + 8 CSV) |
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| 💾 | **Total size** | **~33 GB** |
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</div>
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---
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## 🔥 Why ADAS-TO?
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> *"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."*
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### 🏆 Unprecedented Scale
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Over **15,000 real-world takeover events** — orders of magnitude larger than existing datasets that typically contain hundreds of events captured in driving simulators.
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### 🌍 Unmatched Diversity
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**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.
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### 🎯 Rich Multimodal Signals
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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.
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### 🌐 Real-World Naturalistic Data
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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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---
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## 🎯 Use Cases
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| Application | Description |
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|:---|:---|
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| 🔮 **Takeover Prediction** | Build early warning systems that predict when a driver will need to take over |
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| 🧠 **Driver Behavior Modeling** | Understand human responses during control transitions |
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| 📈 **ADAS Performance Analysis** | Compare disengagement patterns across vehicle types and ADAS systems |
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| 🤖 **Autonomous Driving Safety** | Train and evaluate safety-critical decision-making models |
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| 🧪 **Human Factors Research** | Study cognitive load, reaction times, and situational awareness |
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| 📊 **Multimodal Time-Series** | Develop forecasting and classification models on rich temporal data |
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| 🏗️ **HMI Design** | Design better human-machine interfaces for automated vehicles |
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---
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## 📁 Dataset Structure
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```
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ADAS-TO/
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├── <CAR_MODEL>/ # e.g., TOYOTA_PRIUS, TESLA_AP3_MODEL_3
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│ └── <driver_XXX>/ # 🔒 anonymized driver ID
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│ └── <route_XXX>/ # 🔒 anonymized route ID
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│ └── <clip_id>/ # integer (0-indexed per route)
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│ ├── 🎥 takeover.mp4 20-second front-camera video
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│ ├── 📋 meta.json clip metadata & timing
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│ ├── 🚗 carState.csv vehicle dynamics & driver inputs
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│ ├── 🤖 controlsState.csv ADAS controller state & alerts
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│ ├── 🎮 carControl.csv lateral/longitudinal commands
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│ ├── ⚙️ carOutput.csv actuator outputs
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│ ├── 🧠 drivingModelData.csv model predictions & lane detection
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│ ├── 📡 radarState.csv lead vehicle radar data
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│ ├── 📐 accelerometer.csv IMU acceleration data
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│ └── 📏 longitudinalPlan.csv planner targets & FCW
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└── ...
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```
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---
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## 📐 Takeover Event Definition
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<div align="center">
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```
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◄──────── 10 seconds ────────►◄──────── 10 seconds ────────►
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┌──────────────────────────────┬──────────────────────────────┐
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│ 🤖 ADAS ENGAGED │ 👤 MANUAL CONTROL │
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│ (automation driving) │ (driver takes over) │
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└──────────────────────────────┴──────────────────────────────┘
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▲
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TAKEOVER EVENT
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(ON → OFF transition)
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```
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</div>
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A **takeover event** is detected as an ADAS ON → OFF transition satisfying:
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| Criterion | Value |
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|:---|:---|
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| **ADAS engaged** | `controlsState.enabled` OR `cruiseState.enabled` |
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| **Min ON duration** | ≥ 2 seconds before disengagement |
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| **Min OFF duration** | ≥ 2 seconds after disengagement |
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| **Gap merging** | Transient gaps < 0.5s merged (filters sensor noise) |
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| **Clip window** | ±10 seconds centered on transition (20s total) |
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---
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## 📑 Data Fields Reference
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### 📋 meta.json — Clip Metadata
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| Field | Type | Description |
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| `car_model` | string | Vehicle model (e.g., `TOYOTA_PRIUS`) |
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| `dongle_id` | string | Anonymized driver ID (`driver_XXX`) |
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| `route_id` | string | Anonymized route ID (`route_XXX`) |
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| `log_kind` | string | Log resolution: `qlog` (10 Hz) or `rlog` (100 Hz) |
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| `log_hz` | int | CAN signal sampling rate |
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| `vid_kind` | string | Camera source type |
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| `camera_fps` | int | Video frame rate (20 fps) |
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| `clip_id` | int | Clip index within route (0-indexed) |
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| `event_mono` | int | Monotonic timestamp of takeover (ns) |
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| `video_time_s` | float | Takeover time within full route video (s) |
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| `clip_start_s` | float | Clip start time within route (s) |
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| `clip_dur_s` | float | Clip duration (s) |
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### 🚗 carState.csv — Vehicle Dynamics & Driver Inputs
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| Column | Unit | Description |
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|:---|:---|:---|
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| `vEgo` | m/s | Ego vehicle speed |
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| `aEgo` | m/s² | Ego vehicle acceleration |
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| `steeringAngleDeg` | deg | Steering wheel angle |
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| `steeringTorque` | N·m | Driver steering torque |
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| `steeringPressed` | bool | Driver actively steering |
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| `gasPressed` | bool | Gas pedal pressed |
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| `brakePressed` | bool | Brake pedal pressed |
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| `cruiseState.enabled` | bool | Cruise / ADAS engaged |
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### 🤖 controlsState.csv — ADAS Controller
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| Column | Unit | Description |
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| `enabled` | bool | ADAS system enabled |
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| `active` | bool | ADAS actively controlling vehicle |
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| `curvature` | 1/m | Current path curvature |
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| `desiredCurvature` | 1/m | Target curvature from planner |
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| `vCruise` | m/s | Set cruise speed |
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| `longControlState` | enum | Longitudinal control state |
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| `alertText1` | string | Primary driver alert |
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| `alertText2` | string | Secondary driver alert |
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### 🎮 carControl.csv — Control Commands
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| Column | Unit | Description |
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| `latActive` | bool | Lateral control active |
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| `longActive` | bool | Longitudinal control active |
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| `actuators.accel` | m/s² | Commanded acceleration |
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| `actuators.torque` | N·m | Commanded steering torque |
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| `actuators.curvature` | 1/m | Commanded path curvature |
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### ⚙️ carOutput.csv — Actuator Outputs
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| Column | Description |
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|:---|:---|
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| `actuatorsOutput.accel` | Acceleration actuator output |
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| `actuatorsOutput.brake` | Brake actuator output |
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| `actuatorsOutput.gas` | Gas actuator output |
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| `actuatorsOutput.steer` | Steering actuator output |
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| `actuatorsOutput.steerOutputCan` | Raw CAN steering output |
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| `actuatorsOutput.steeringAngleDeg` | Steering angle output (deg) |
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### 🧠 drivingModelData.csv — Driving Model Predictions
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| Column | Description |
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|:---|:---|
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| `action.desiredCurvature` | Model-predicted desired curvature |
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| `action.desiredAcceleration` | Model-predicted desired acceleration |
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| `laneLineMeta.leftProb` | Left lane line detection probability |
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| `laneLineMeta.rightProb` | Right lane line detection probability |
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### 📡 radarState.csv — Lead Vehicle Detection
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| Column | Unit | Description |
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| `leadOne.dRel` | m | Distance to primary lead vehicle |
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| `leadOne.vRel` | m/s | Relative velocity of lead |
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| `leadOne.vLead` | m/s | Absolute velocity of lead |
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| `leadOne.aLeadK` | m/s² | Lead vehicle acceleration |
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| `leadTwo.*` | — | Secondary lead vehicle (same fields) |
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### 📐 accelerometer.csv — IMU Data
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| Column | Unit | Description |
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| `acceleration.v` | m/s² | 3-axis acceleration vector |
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| `timestamp` | — | Sensor timestamp |
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### 📏 longitudinalPlan.csv — Planner Outputs
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| Column | Unit | Description |
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|:---|:---|:---|
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| `aTarget` | m/s² | Target acceleration |
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| `hasLead` | bool | Lead vehicle detected |
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| `fcw` | bool | Forward collision warning active |
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| `speeds[]` | m/s | Planned speed profile |
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| `accels[]` | m/s² | Planned acceleration profile |
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---
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## 🚘 Vehicle Coverage
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<div align="center">
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**23 Manufacturers · 163 Models · From Compact EVs to Full-Size Trucks**
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</div>
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### Top Vehicle Models by Clip Count
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| # | Vehicle Model | Clips | | # | Vehicle Model | Clips |
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|:---:|:---|---:|:---:|:---:|:---|---:|
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| 1 | 🏆 RIVIAN R1 GEN1 | 2,127 | | 10 | CHEVROLET BOLT EUV | 244 |
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| 2 | 🥈 ACURA MDX 3G | 1,863 | | 11 | TOYOTA RAV4 TSS2 | 228 |
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| 3 | 🥉 FORD F-150 MK14 | 1,226 | | 12 | RAM HD 5TH GEN | 221 |
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| 4 | CHEVROLET SILVERADO | 639 | | 13 | VOLKSWAGEN JETTA MK7 | 215 |
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| 5 | TOYOTA PRIUS | 482 | | 14 | KIA EV6 | 209 |
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| 6 | HONDA CIVIC | 470 | | 15 | VOLKSWAGEN GOLF MK7 | 192 |
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| 7 | TESLA MODEL 3 | 432 | | 16 | KIA NIRO EV | 185 |
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| 8 | FORD MAVERICK MK1 | 300 | | 17 | HYUNDAI IONIQ 6 | 177 |
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| 9 | HYUNDAI IONIQ 5 | 266 | | 18 | VOLKSWAGEN ATLAS MK1 | 153 |
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<details>
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<summary>📋 <b>All 23 Manufacturers</b> (click to expand)</summary>
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> 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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</details>
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---
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## 🚀 Quick Start
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### Loading a Single Clip
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```python
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import json
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import pandas as pd
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from huggingface_hub import hf_hub_download
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repo_id = "HenryYHW/ADAS-TO"
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clip_path = "TOYOTA_PRIUS/driver_001/route_001/0"
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# 📋 Download metadata
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meta_path = hf_hub_download(repo_id, f"{clip_path}/meta.json", repo_type="dataset")
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with open(meta_path) as f:
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meta = json.load(f)
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# 🚗 Load vehicle state signals
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car_state = pd.read_csv(
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hf_hub_download(repo_id, f"{clip_path}/carState.csv", repo_type="dataset")
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)
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print(car_state[["vEgo", "aEgo", "steeringAngleDeg", "brakePressed"]].describe())
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# 🤖 Load ADAS controller state
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controls = pd.read_csv(
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hf_hub_download(repo_id, f"{clip_path}/controlsState.csv", repo_type="dataset")
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)
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# 📡 Load radar data
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radar = pd.read_csv(
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hf_hub_download(repo_id, f"{clip_path}/radarState.csv", repo_type="dataset")
|
| 345 |
+
)
|
| 346 |
+
```
|
| 347 |
+
|
| 348 |
+
### Iterating Over All Clips
|
| 349 |
+
|
| 350 |
+
```python
|
| 351 |
+
from huggingface_hub import HfApi
|
| 352 |
+
|
| 353 |
+
api = HfApi()
|
| 354 |
+
files = api.list_repo_files("HenryYHW/ADAS-TO", repo_type="dataset")
|
| 355 |
+
meta_files = [f for f in files if f.endswith("meta.json")]
|
| 356 |
+
print(f"Total clips: {len(meta_files)}") # → 15,705
|
| 357 |
+
```
|
| 358 |
+
|
| 359 |
+
### 💾 Download the Full Dataset
|
| 360 |
+
|
| 361 |
+
```bash
|
| 362 |
+
# Using huggingface-cli (recommended)
|
| 363 |
+
huggingface-cli download HenryYHW/ADAS-TO --repo-type dataset --local-dir ./ADAS-TO
|
| 364 |
+
|
| 365 |
+
# Using git-lfs
|
| 366 |
+
git lfs install
|
| 367 |
+
git clone https://huggingface.co/datasets/HenryYHW/ADAS-TO
|
| 368 |
+
```
|
| 369 |
+
|
| 370 |
+
---
|
| 371 |
+
|
| 372 |
+
## 🔒 Privacy & Ethics
|
| 373 |
+
|
| 374 |
+
- **Anonymized identifiers**: All driver and route IDs are replaced with anonymous tokens (`driver_XXX`, `route_XXX`)
|
| 375 |
+
- **Forward-view only**: Video captures road-facing view only — no cabin or driver footage
|
| 376 |
+
- **No PII**: No personally identifiable information is included in any data file
|
| 377 |
+
- **Community-sourced**: Data collected through autonomous driving enthusiast communities with informed participation
|
| 378 |
+
|
| 379 |
+
---
|
| 380 |
+
|
| 381 |
+
## 📖 Data Collection
|
| 382 |
+
|
| 383 |
+
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:
|
| 384 |
+
|
| 385 |
+
1. **Detect** ADAS disengagement events (ON→OFF transitions)
|
| 386 |
+
2. **Extract** synchronized video and CAN-bus signals within a 20-second window
|
| 387 |
+
3. **Validate** each clip for signal completeness and temporal alignment
|
| 388 |
+
4. **Anonymize** all driver and route identifiers
|
| 389 |
+
|
| 390 |
+
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.
|
| 391 |
+
|
| 392 |
+
---
|
| 393 |
+
|
| 394 |
+
## 📝 Citation
|
| 395 |
+
|
| 396 |
+
If you use ADAS-TO in your research, please cite:
|
| 397 |
+
|
| 398 |
+
```bibtex
|
| 399 |
+
@dataset{adas_to_2026,
|
| 400 |
+
title = {ADAS-TO: A Large-Scale Multimodal Naturalistic Dataset and
|
| 401 |
+
Empirical Characterization of Human Takeovers during ADAS Engagement},
|
| 402 |
+
author = {Anonymous Authors},
|
| 403 |
+
year = {2026},
|
| 404 |
+
publisher = {Hugging Face},
|
| 405 |
+
url = {https://huggingface.co/datasets/HenryYHW/ADAS-TO}
|
| 406 |
+
}
|
| 407 |
+
```
|
| 408 |
+
|
| 409 |
+
---
|
| 410 |
+
|
| 411 |
+
## 📄 License
|
| 412 |
+
|
| 413 |
+
<div align="center">
|
| 414 |
+
|
| 415 |
+
This dataset is released under [**CC BY-NC 4.0**](https://creativecommons.org/licenses/by-nc/4.0/).
|
| 416 |
+
|
| 417 |
+
For academic and non-commercial research purposes.
|
| 418 |
+
|
| 419 |
+
---
|
| 420 |
+
|
| 421 |
+
*Built with ❤️ for the autonomous driving research community*
|
| 422 |
+
|
| 423 |
+
</div>
|
|
|