Self-Driving Cars Revolutionize Transportation with Deep Learning for Safety and Mobility

Abstract

This study aims to develop and optimize self-driving technologies through a comprehensive methodology encompassing data collection, preprocessing, exploratory data analysis, modeling with attention mechanisms, and performance evaluation of neural network architectures. The methodology involves structured data collection from sources including camera images and vehicle parameters, followed by preprocessing steps such as filename extraction, cropping, color space conversion, and normalization. EDA is conducted to understand variables like steering angles and throttle values. Modeling incorporates attention mechanisms and pre-trained models like DenseNet169. Performance evaluation is performed using metrics including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Loss.DenseNet169 exhibits superior performance compared to DenseNet201, ResNet101, and VGG16, as evidenced by lower MSE, MAE, and Loss scores. This indicates its effectiveness in accurately predicting outcomes, highlighting its potential for advancing self-driving technologies. The methodology provides valuable insights for model development and deployment, contributing to the creation of safer and more efficient autonomous driving systems.