With the development of automation and electrification, autonomous robotics and vehicles have a wide range of use in space science and self-driving cars. An important part of autonomous vehicles is the navigation system. In the past decades using vision-based systems guidance systems became more popular. By taking the tire and road interaction from the vehicles automatically the terrain has to be classified these type of technique was used in the rovers and pathfinder now it came to the automobiles. In this project, deep learning technology is used to detect curved paths in autonomous vehicles. In this paper, a customized lane detection algorithm was implemented to detect the curvature of the lane. A ground truth labeling toolbox for deep learning is used to detect the curved path in an autonomous vehicle. By mapping point to point in each frame 80-90% computing efficiency and accuracy are achieved in detecting path.
- Principal Component Analysis
- DCT and shape features
- SVM classifier
- GLCM Features
- NN Classifier
- The segmentation algorithm Proves to be simple and effective
- The greyscale Co-occurrence matrix performed well in NN
- Better texture and edge representation
- Segmentation provides better clustering efficiency
- 4 GB of RAM
- 500 GB of Hard disk
- MATLAB 2014a
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