Supervised and Unsupervised Learning in Tesla’s Full Self-Driving (FSD) System: A Comparative Study

Abstract

Mayank Vadaliya

Tesla’s Full Self-Driving (FSD) system represents a significant leap in autonomous vehicle technology, leveraging advanced machine learning algorithms for real-time decisionmaking. This paper offers an in-depth comparative analysis of the supervised and unsupervised learning methods employed within Tesla’s FSD system. By evaluating the performance of these approaches in real-world driving conditions, we explore their respective strengths and limitations. The findings highlight the interplay between supervised and unsupervised learning, demonstrating their combined role in enhancing the system’s safety, efficiency, and scalability.

PDF