{
  "fetched_at": "2026-10-20T05:08:28Z",
  "source_url": "https://www.kaggle.com/competitions/driving-behaviour/data",
  "pages": [
    {
      "name": "data-description",
      "content": "\n## Files\n\n*   **train_motion_data.csv** - the training set\n*   **Test.csv** - the test set\n*   **Sample.csv** - a sample submission file in the correct format\n\n## Columns\n\n*   `AccX`        \\\n*   `AccY`         | - X, Y, Z acceleration axis\n*   `AccZ`        /\n*   `GyroX`       \\\n*   `GyroY`         | - X, Y, Z orientation axis\n*   `GyroZ`        /\n*   `Class`            - classification label\n*   `Timestamp`  - time in seconds",
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    {
      "name": "Description",
      "content": "### **Context**\n\nAggressive driving behavior is the leading factor of road traffic accidents. As reported by the **AAA Foundation for Traffic Safety**, 106,727 fatal crashes – 55.7 percent of the total – during a recent four-year period involved drivers who committed one or more aggressive driving actions. Therefore, how to predict dangerous driving behavior quickly and accurately?\n\n### **Solution Approach**\n\nAggressive driving includes speeding, sudden breaks and sudden left or right turns. All these events are reflected on accelerometer and gyroscope data. Therefore, knowing that almost everyone owns a smartphone nowadays which has a wide variety of sensors, we've designed a data collector application in android based on the accelerometer and gyroscope sensors.\n\n### **Content**\n\n- Sampling Rate: 2 samples (rows) per second.\n- Gravitational acceleration: removed.\n- Sensors: Accelerometer and Gyroscope.\n- Data: \n   1.  Acceleration (X,Y,Z axis in meters per second squared (m/s2))\n   2.  Rotation (X,Y, Z axis in degrees per second (°/s))  \n   3.  Classification label (SLOW, NORMAL, AGGRESSIVE)\n   4.  Timestamp (time in seconds)\n- Driving Behaviors:\n   1.  Slow\n   2.  Normal\n   3.  Aggressive\n- Device: Samsung Galaxy S21\n\n### **Articles**\n- [Building a Driving Behaviour Dataset](http://rochi.utcluj.ro/articole/10/RoCHI2022-Cojocaru-I-1.pdf)\n- [Driver Behaviour Analysis based on Deep Learning Algorithms](http://rochi.utcluj.ro/articole/10/RoCHI2022-Cojocaru-I-2.pdf)\n\n### **Authors**\n\n- Paul-Stefan Popescu\n- Ion Cojocaru",
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    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition is Categorization Accuracy, see https://www.kaggle.com/getting-started/191889 for mode details.\nSee sample.csv from data tab for a more brief picture.\nThe file should contain a header and have the following format:\n\nID,Class\n1,SLOW",
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      "postTitle": "",
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    },
    {
      "name": "rules",
      "content": "You can upload:\n\n - 5 Maximum Daily Submissions!\n\n - 2 Scored Private Submissions!\n\n\n **Have fun!**",
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      "hasIsPublished": false,
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}