This project comprises of a simulated environment "Road Network" built with Unity3D and a Convolutiuonal Neural Network that is trained to drive a car autonomously on this environment.
Introduction In this project we train a car in a simulated environment to safely navigate itself. The approach is to make use of a Convolutional Neural Network.
Technical details - Here I make use of Unity3D to develop the environment and Python to write our machine learning scripts.
The dataset for our CNN is built by taking instances of actions[steer left,accelerate,reverse,steeer right,take no action] and their respective image frames.
The camera feed and the respective action of this frame is sent to a CNN. By making enough number of image and action pairs we build our dataset and train on an AlexNet.
The dataset was created by playing the game numerous hours and accounting the keystrokes which each image frame. These were stored in a 2-dimensional array format in numpy, where the array[0] contained the image frame pixel values in grayscale and the array[1] contained the corresponding keystroke. There are about 40-45 thousand samples each of them are of the dimension – [160 pixels X 120 pixels X 1 grayscale channel] X [ particular keystroke(a,w,s,d,'nothing')]
The CNN manages to do a fairly remarkable job by classifying correctly as to what action needs to be taken –
- Steer left
- Accelerate
- Steer right
- Apply brakes
- Take no action Even when the car is set out of track, if there is a road present in the frame the CNN drives the vehicle towards the track, this is something surprising.
