This paper outlines an organized approach to creating a dataset and training an Artificial Neural Network (ANN) for autonomous vehicle driving systems. It utilizes the KITTI dataset and includes key preprocessing steps like converting binary files to CSV. Other steps comprise calibrating and plotting 3D point cloud data and creating 2D front-view projections with consistent sizes. A novel data preparation pipeline is presented, ensuring homogeneity while addressing challenges like variable point distributions. It also confronts cropping consistency to improve data quality. The results demonstrate a robust methodology for generating training-ready datasets. A three-layer ANN is trained effectively for autonomous driving tasks. This work contributes significantly to autonomous systems. It provides a scalable approach that can adapt to various datasets.