README.md

---
license: cc-by-4.0
task_categories:
- image-classification
- object-detection
- image-segmentation
- other
language:
- en
tags:
- computer-vision
- autonomous-driving
- driver-attention
- gaze-estimation
- semantic-segmentation
- dataset
pretty_name: DATAD — Driver Attention in Takeover of Autonomous Driving
size_categories:
- 100M<n<1B
---



# DATAD: Driver Attention in Takeover of Autonomous Driving

## Dataset Overview

This dataset provides **multimodal recordings** for analyzing driver attention during **takeover scenarios in autonomous driving**.  
It includes **gaze–object annotations, per-frame feature vectors, and instance segmentation outputs**, supporting research in **driver monitoring, gaze estimation, takeover performance, and semantic scene understanding**.

## Data Organization and Participants

Data are organized **per participant**, with each participant’s data compressed and uploaded individually in **7Z format**.

- **Tester1–Tester10**: university students with driving experience  
- **Tester11–Tester30**: experienced drivers (ride-hailing drivers)

The two participant groups were exposed to **different scenario designs**.


## Scenario Design
### Tester1–Tester10 (Student Drivers)
Two major categories of **explicit high-risk scenarios**, each containing:
- **One primary risk**
- **One secondary risk**

Scenario categories:
1. **Road construction ahead**
2. **Sudden intrusion of non-motorized vehicles**

Each category includes multiple concrete scenarios generated by **varying background vehicle behaviors**.


### Tester11–Tester30 (Experienced Drivers)
**Progressive risk scenarios** with latent and gradually emerging hazards, divided into two major categories:

1. **Right-side vehicle squeezing lane change + left-side non-motorized sudden appearance**
2. **Left-side non-motorized vehicle intrusion + front traffic accident**

Similarly, each category is instantiated into multiple scenarios by **adjusting background traffic behaviors**.

Overall, the dataset enables comparative analysis of **driver attention and takeover behavior across driver experience levels and scenario complexities**.

---

## 📂 Dataset Structure

```text
Tester1/
├── Gaze_object_output/
│   ├── Stare_obj_0.csv      # Gaze target data for scene 1
│   ├── Stare_obj_1.csv
│   └── ...
│
├── Tester1_feature_csv/
│   ├── feature_0.csv       # Feature vectors for scene 1
│   ├── feature_1.csv
│   └── ...
│
├── Tester1_IS/
│   ├── Tester1_0_IS/
│   │   ├── frame_output/   # Instance segmentation images (PNG frames)
│   │   │   ├── frame_1.png
│   │   │   └── ...
│   │   └── obj_pixel_table.csv  # Pixel-level statistics for each segmented vehicle
│   ├── Tester1_1_IS/
│   └── ...
```
---
### `Gaze_object_output/`
This directory contains **gaze–object annotation files** for each scene.
**File format**
- `<scene_id>`: scene index (starting from 0)
- Each row corresponds to one time step / frame

##### Gaze Target Information
- `Stare_obj`: ID of the object being gazed at  
  - `0` indicates background or no valid gaze target
- `Stare_area`: coarse gaze region label on the screen
The screen resolution is **5740 × 1010**, and gaze regions are defined as:
| Label | Description | Region (pixels) |
|------|-------------|-----------------|
| `LF` | Left front view | `[0, 0] – [2870, 1010]` |
| `RF` | Right front view | `[2870, 0] – [5740, 1010]` |
| `LB` | Left side mirror | `[700, 570] – [1370, 1000]` |
| `RB` | Right side mirror | `[4719, 560] – [5389, 990]` |
| `MB` | Rear-view mirror | `[2890, 210] – [3540, 400]` |

##### Vehicle Screen Positions
- `Car{i}_screen_X`, `Car{i}_screen_Y`: 2D screen-space coordinates of risk-relevant vehicles
- Coordinates are aligned with the same frame as gaze annotations
- Missing vehicles are filled with `0`
**Number of risk objects per frame**
- For the **first 10 participants**, each frame contains up to **9 risk objects**
- For the **remaining 20 participants**, scenes **5–9** contain **8 risk objects**
- Columns are kept consistent across files; unused slots are zero-padded

These files jointly describe **where the driver is looking** and **where potential risk objects are located** on the screen at each time step, and are time-aligned with other modalities in the dataset.

---
---

### `Tester1_feature_csv/`
This directory contains **per-frame driving state and scene feature files** for each scene.
**File format**
- `<scene_id>`: scene index (starting from 0)
- Each row corresponds to one time step / frame
- Rows are time-aligned with gaze annotations and instance segmentation outputs

##### Ego Vehicle and Driver State
- `time`: timestamp (Unix time)
- `steering`: steering wheel angle
- `accelerator`: accelerator pedal value
- `brake`: brake pedal value
- `TOR_flag`: take-over request indicator
- `Handchange_flag`: handover / control change indicator
- `Collision_flag`: collision indicator (binary)

##### Ego Vehicle Position
- `main_car_id`: ID of the ego vehicle
- `main_car_x`, `main_car_y`: ego vehicle position in world coordinates

##### Surrounding Risk Object Features
For each risk-relevant object in the scene, features are stored using indexed columns:

- Object indices: `Car1` … `Car9`
- Typical attributes include:
  - World-space position
  - Screen-space position (`Car{i}_screen_X`, `Car{i}_screen_Y`)
  - Additional kinematic or geometric features
If a risk object is not present in a frame, its corresponding feature values are filled with `0`.

##### Gaze Point Projection
- `ScreenPoint2D_x`, `ScreenPoint2D_y`: projected 2D gaze point on the screen, aligned with gaze annotations

These files provide **low-level driving signals, ego vehicle states, and scene-level object features**, and are intended to be used jointly with:
- `Gaze_object_output/` (gaze–object annotations)
- `Tester*_IS/` (instance segmentation outputs)

---
---

### `Tester*_IS/Tester*_<scene_id>_IS/`

This directory contains **instance segmentation (IS) outputs** for each scene, generated using **CARLA 0.9.15**.

Each subfolder corresponds to one scene and includes the following files:
##### `frame_output/`
- A sequence of **PNG images** representing **instance segmentation foregrounds**
- Each image corresponds to **one frame**, and is **row-aligned** with the CSV files in other modalities
- This design enables precise **frame-level alignment and multimodal analysis**

##### `obj_pixel_table.csv`
- A lookup table mapping **vehicle IDs to instance segmentation pixel values**
- Required because in **CARLA 0.9.15**, instance segmentation assigns **random pixel values** to objects in each run
- This file provides the **ground-truth correspondence** between vehicles and their pixel labels **for this specific scene**

##### `processed_screenshot.png`
- A **top-down overview image** captured at the **start of the takeover recording**
- Visualizes the **vehicle–pixel correspondence**, where connecting lines indicate matched vehicles and pixel labels
- This file is intended **for validation and debugging only**

**Important note:**  
If abnormal vertical lines or incorrect non-motorized object segmentation are observed in `processed_screenshot.png`, the data in this folder **should not be used**, as it indicates unreliable instance segmentation results for the scene.

Together, these files support **pixel-level, object-aware analysis** of driver attention and scene context, and are designed to be used jointly with gaze annotations and feature CSV files.