Towards Autonomous Driving System Using Behavioral Cloning Approach

The automotive industry has been focused on developing autonomous driving technology, which has been a subject of research for many years. Recent advancements in Convolutional Neural Networks (CNN) have shown remarkable performance in computer vision tasks, including autonomous driving systems. Behavioral cloning is a widely adopted technique in autonomous vehicle systems, that involves training a model to replicate the driving behavior of human drivers. This paper presents research on implementing behavioral cloning using a CNN architecture for an autonomous vehicle system in the Udacity self-driving car simulator environment. Our approach employs a custom-designed CNN Model to learn driving behavior from a dataset of images captured from the simulator, and we have applied various data augmentation techniques to the dataset to enhance the model’s performance. This model has a total of 27 layers, including convolutional layers, normalization layers, activation layers, dropout layers, flatten layers, and dense layers. We evaluate the model’s performance on a metric, called mean square loss error and demonstrate that our approach outperforms previous works in this domain. The results of this research underscore the effectiveness of using behavioral cloning and data augmentation techniques for autonomous vehicle systems.