Critical Application Feasibility of Predictive Learning in Autonomous Vehicles

In this article, we will explores the impact of deep learning on autonomous vehicles (AVs), defining both concepts and highlighting their relationship. Behavioral prediction and control used to make decisions based on vast datasets and real-time sensory input, reducing accidents and increasing vehicle safety. Computer vision techniques, such as object detection and semantic segmentation, enable AVs to perceive and interpret with their environment. Sensor fusion is another critical application for safer navigation and decision-making. Path planning optimizes routes that balance efficiency and safety. In the context of fault diagnosis, deep learning excels in detecting and addressing issues within the vehicle's systems. We will also examine the use of deep learning in a few key AV research areas, including behavioral prediction, control, sensor fusion, defect detection, path planning, and computer vision. There will also be a discussion of recent developments in deep learning for autonomous vehicles.