Application of Machine Learning Algorithms in Autonomous Vehicle Navigation Systems

Application of Machine Learning Algorithms in Autonomous Vehicle Navigation Systems

Surabhi Bhavarlal Dangi1, Deepali Hiraman Gavhane2

1 Assistant Professor, Pune Vidyarthi Griha’s College of Science and Commerce, Parvati, Pune-411 009, Maharashtra, India

2 Assistant Professor, Sadhu Vaswani Institute of Management Studies for Girls, Koregaon Park, Pune-411001, Maharashtra, India

Abstract

The development of autonomous vehicles (AVs) has greatly benefited from the use of machine learning (ML) algorithms, which help improve the vehicle's ability to navigate and make decisions. This paper looks at how different ML algorithms are used in AVs to process sensor data, make smart driving choices, and safely navigate complex road environments. The focus is on using techniques like supervised learning, unsupervised learning, reinforcement learning, and deep learning for tasks such as planning the vehicle's path, detecting obstacles, combining sensor data, and making real-time decisions. The paper also addresses the challenges of training these models in the real world, where conditions can be unpredictable and data quality may vary. Furthermore, it discusses how these ML algorithms are improving vehicle safety, autonomy, and performance. The research also explores future trends like edge computing and multi-agent systems, which could make ML in autonomous vehicles even more effective. Overall, the paper shows how ML has the potential to make transportation safer, more efficient, and adaptable to different driving situations.

Keywords : Autonomous, Automobiles, Machine learning, Algorithm, Navigation