/Self-Driving-Car-Engines

Gathers signal processing, computer vision, machine learning and deep learning for self-driving car engines.

Primary LanguageJupyter NotebookMIT LicenseMIT

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MIT License

Self-Driving-Cars-Engine, Gathers signal processing, computer vision, machine learning and deep learning for self-driving car engines.

What-Done

  1. Signal processing (1D smoothing, 2D smoothing, convolution 2 signals, pass-filters)
  2. Simple straight lane detection
  3. Steering suggestion
  4. Multi-lane detection
  5. Multi-lane angle
  6. Curve-lane detection
  7. Car detection using sliding + HOG + eXtreme Boosting
  8. Object detection using Tensorflow
  9. Distance + Angle for object detection
  10. Distance + Speed for object detection
  11. Traffic light detection
  12. Gradient Smoothing
  13. Lane Smoothing
  14. Dynamic count lane detection
  15. Road Segmentation
  16. Plate detection
  17. Image Augmentation
  18. Lane Augmentation offroad

Results

1. Signal processing

2. simple straight lane detection

3. Steering suggestion

4. Multi-lane detection

5. Multi-lane angle

6. Curve-lane detection

7. Car detection using sliding + HOG + eXtreme Boosting

8. Object detection using Tensorflow

9. Distance + Angle for object detection

10. Distance + Speed for object detection

11. Traffic light detection

12. Gradient Smoothing

13. Lane Smoothing

14. Dynamic count lane detection

15. Road Segmentation

VGG16 Road Segmentation

Mobilenet City Segmentation

16. Plate detection

17. Image augmentation

Originally from https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library

18. Lane Augmentation offroad