The automobile industry has changed through the application of Machine Learning (ML) techniques in the design of autonomous vehicles. Yet, ensuring enhanced performance along with safety is still a prime challenge. The focus of this paper is on solving the concrete problem of improving autonomous vehicle choices by applying machine learning algorithms to process the real-time data generated, enabling predictive maintenance strategies and adaptive safety mechanisms. The study reviews various ML-driven use cases ranging from driving automation to collision avoidance to adaptive cruise control, highlighting how they accomplish increased vehicle efficiency and accident reduction. The final research analysis demonstrate that ML based predictive modeling effectively enhances the fuel efficiency and dynamic vehicle control performance, therefore further improving autonomous vehicle operational reliability and safety.
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Adarsh et al. (2025) studied this question.