Real-Time Machine Learning Applications in Autonomous Vehicles

Abstract

The development of autonomous vehicles (AVs) has seen tremendous advancements, primarily driven by machine learning (ML) algorithms that enable real-time decision-making, perception, and control. This paper explores the real-time applications of ML in autonomous vehicles, emphasizing their roles in perception, path planning, obstacle avoidance, and decision-making systems. We discuss the integration of real-time data processing, sensor fusion, and deep learning techniques that enable AVs to navigate complex environments with minimal human intervention. Challenges such as data processing speed, safety concerns, and ethical considerations are also addressed, offering insights into future developments in the field.