Advanced Self Driving Car Using Machine Learning

There are surprisingly innovative approaches to develop AI systems for self-driving cars, many of which require advanced and high-end hardware. However, this paper proposes a simple AI-based system with low hardware requirements. The proposed system comprises a simple three-layer fully connected neural network that can interpret images captured by a forward-facing QVGA camera and generate corresponding steering commands. When presented with an input image, the neural network selects one of four available commands: forward, left, right, or stop. Surprisingly, the system successfully learns to navigate the road and stay within its lane using only a limited amount of training data (just 250 images). Notably, the system acquires knowledge of crucial road features solely based on the steering angle provided by the human driver, without explicit training for road line detection. In comparison to more intricate approaches like Nvidia's convolutional neural network-based lane detection and management, this method demonstrates remarkable robustness and cost-effectiveness. The aim is to showcase that this approach can lead to enhanced performance and reduced hardware requirements, thereby facilitating the development of more accessible and affordable self-driving vehicles. The described paper highlights that a simple artificial neural network, like the one discussed, is sufficient for accomplishing relatively complex tasks such as lane keeping.