README.md

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license: mit

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pretty_name: DATAD — Driver Attention in Takeover of Autonomous Driving tags: - computer-vision - autonomous-driving - driver-attention - gaze-estimation - semantic-segmentation - dataset license: cc-by-4.0 task_categories: - image-classification - object-detection - image-segmentation - other size_categories: - 10K<n<100K

DATAD: Driver Attention in Takeover of Autonomous Driving

This dataset provides multimodal recordings for analyzing driver attention during takeover scenarios in autonomous driving.
It includes gaze-object interactions, feature vectors, and image segmentation data.
The dataset supports research in driver monitoring, gaze estimation, takeover performance, and semantic scene understanding.

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📂 Dataset Structure

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/ │ └── ...

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📊 File Descriptions

1. Gaze_object_output/

• Each Stare_obj_X.csv contains gaze-object interaction results for subject X.
• Typical fields include:
  • timestamp
  • object_id
  • gaze_x, gaze_y
  • object_class

2. Tester2_feature_csv/

• Each feature_X.csv provides extracted feature vectors for subject X.
• Features may cover:
  • Driver monitoring metrics
  • Eye-movement statistics
  • Vehicle state parameters

3. Tester2_IS/

• frame_output/: Instance segmentation images for each frame (.png).
• obj_pixel_table.csv: Pixel-level statistics for segmented background vehicles.
  • Example fields:
    • object_id
    • pixel_count (area of mask)
    • bbox_x1, bbox_y1, bbox_x2, bbox_y2
    • class_label

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