Imitation Learning for Autonomous Driving Cars

The world is the middle of another industrial revolution. But this time, instead of the steam engines, the real revolution will be led by computer scientists across the globe who will forever change the way we interact with our environment. A small subset of groundbreaking research has been going on in the field of autonomous driving to ensure the safety of passengers and human comfort. Autonomous driving, which primarily relies on the subset of machine learning i.e., imitation learning has been a subject of research for several decades now. The critical problem in autonomous driving is predicting the steering angles of the vehicle. Behavior cloning is a form of imitation learning and it learns from the actions of human experts. However, imitation learning has its own set of challenges and performs poorly in certain conditions. In this research a new algorithm is proposed, CNNO, to predict the steering angles of the vehicle. It has five convolution layers, two max pool layers, four fully connected layers, flatten layer, and a drop-out layer. It is subsequently compared against CNN-Neural Circuit Policy, CNN, ResNet50, VGG16, and VGG19 architectures. The proposed algorithm has shown to give the best evaluation error results from epochs 10, 30 & 50 and the best training error in epoch 70.