Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+---
+---
+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**.
+
+---
+
+## 📂 Dataset Structure
+
+Tester2/
+├── Gaze_object_output/
+│ ├── Stare_obj_0.csv # Gaze target data for subject 0
+│ ├── Stare_obj_1.csv
+│ └── ...
+│
+├── Tester2_feature_csv/
+│ ├── feature_0.csv # Feature vectors for subject 0
+│ ├── feature_1.csv
+│ └── ...
+│
+├── Tester2_IS/
+│ ├── Tester2_0_IS/
+│ │ ├── frame_output/ # Instance segmentation images (PNG frames)
+│ │ │ ├── frame_0001.png
+│ │ │ └── ...
+│ │ └── obj_pixel_table.csv # Pixel-level statistics for each segmented vehicle
+│ ├── Tester2_1_IS/
+│ └── ...
+
+markdown
+复制
+编辑
+
+---
+
+## 📊 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`
+
+---
+
+## 🧪 Usage Example
+
+```python
+import pandas as pd
+from PIL import Image
+
+# Load gaze data
+gaze_df = pd.read_csv("Tester2/Gaze_object_output/Stare_obj_0.csv")
+print("Gaze sample:", gaze_df.head())
+
+# Load feature CSV
+feat_df = pd.read_csv("Tester2/Tester2_feature_csv/feature_0.csv")
+print("Feature sample:", feat_df.describe())
+
+# Load segmentation image
+img = Image.open("Tester2/Tester2_IS/Tester2_0_IS/frame_output/frame_0001.png")
+img.show()
+
+# Load pixel statistics
+obj_stats = pd.read_csv("Tester2/Tester2_IS/Tester2_0_IS/obj_pixel_table.csv")
+print("Segmentation stats:", obj_stats.head())
+🎯 Potential Research Tasks
+Driver gaze estimation
+
+Driver attention prediction
+
+Takeover performance analysis
+
+Semantic segmentation of driving scenes
+
+Multimodal learning (gaze + features + segmentation)