Machine Learning Applications for Autonomous Driving: From Perception to Decision-Making
Keywords:
Machine Learning, Autonomous Driving, Perception, Decision-Making, Object Detection, Path Planning, Traffic Prediction, Challenges, Future DirectionsAbstract
Autonomous driving technology has witnessed remarkable advancements in recent years, largely due to the integration of machine learning (ML) techniques. This paper provides a comprehensive overview of ML applications in autonomous driving systems, focusing on perception and decision-making aspects. It discusses how ML models improve perception tasks such as object detection and tracking, as well as decision-making processes like path planning and traffic prediction. The paper also examines challenges and future directions in the integration of ML algorithms for achieving safer and more efficient autonomous vehicles.
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