Advancements in Neural Network Architectures for Autonomous Vehicles

Abstract

This article provides a comprehensive review of the latest advancements in neural network architectures tailored for autonomous vehicle technology. As the demand for safer and more efficient autonomous systems grows, researchers are continually exploring innovative neural designs to enhance vehicle perception, decision-making, and control mechanisms. We evaluate various neural network models, including convolutional and recurrent neural networks, and their integration into autonomous systems. Our findings indicate significant improvements in vehicle navigation and obstacle detection, underscoring the importance of these advancements for future developments in the autonomous vehicle industry.