This project uses a convolutional neural network to clone driving behavior (from a driving simulator). The input were frames from a recorded video and the output was the steering angle. Read more about the project here. Check out the model running through the final 2 corners of the test track; the video is not the best quality!
Below is an example of the output driving from the model that was built using Keras with a Tensorflow backend. The model architecture was adapted from NVIDIA's End to End Learning for Self-Driving Cars paper.
| Layer | Description |
|---|---|
| Input | 80x320x3 YUV Image |
| Normalization | Normalize batch |
| Convolution 5x5 | 2x2 stride, valid padding, outputs 38x158x24 |
| ELU activation | |
| Convolution 5x5 | 2x2 stride, valid padding, outputs 17x77x36 |
| ELU activation | |
| Convolution 5x5 | 2x2 stride, valid padding, outputs 7x37x48 |
| ELU activation | |
| Convolution 3x3 | 1x1 stride, valid padding, outputs 5x35x64 |
| ELU activation | |
| Convolution 3x3 | 1x1 stride, valid padding, outputs 3x33x64 |
| ELU activation | |
| Dropout | 0.5 keep probablility (training) |
| Flatten | |
| Fully connected | 3168 input, 100 output |
| Fully connected | 100 input, 50 output |
| Fully connected | 50 input, 10 output |
| Output | 10 input, 1 output |