The cameras in the simulator capture images in size of 160 x 320 x 3. I drive the car clocwise and counter-clockwise to combat the bias towards the left turns. The following plots show the distributions of steering angles of training and testing dataset.
There are three cameras on the vehicle, I use images taken by all of them, but correcting the steering agnle by 0.2 for image taken by left cameras and -0.2 for right cameras. I also randomly flip the images as shown in the following
First, I crop the top part of images that have information not relevant to driving. Then I use three convoluted layers with 64, 96, and 128 filters respectively, and each of which is followed by a maximum pooling with keep probability of 0.8. The last conv2D layer is flattened and followed by three fully connected layers of size 128, 64 and 1 with keep prbability of 0.7, 0.7 and 1 respectively. Dropout is used to avoid overfitting.
| input size | kernel size | filters | keep probability | |
|---|---|---|---|---|
| conv2D | 160 x 320 x 3 | 5 x 5 | 64 | 0.8 |
| conv2D | 5 x 5 x 128 | 3 x 3 | 96 | 0.8 |
| conv2D | 3 x 3 x 128 | 3 x 3 | 128 | 0.8 |
| full connect | flatten | 1 | ||
| full connect | 128 | 0.7 | ||
| full connect | 64 | 0.7 | ||
| full connect | 1 | 1 |
The model is trained to predict the steering angle, loss function is MSE (Mean Squared Error) = sum (f_i - y_i)^2 / N, where N is number of samples and f_i is the estimation of y_i. I minimize MSE using AdamOptimizer with learning rate of 5e-5 in 150 epochs. The loss of training and validation in 150 epochs. The car isdriven autonomously around the track by executing
python drive.py model.hdf5
| Loss function | Auto Driving |
|---|---|
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