  The advent of autonomous vehicles has heralded a transformative era in
transportation, reshaping the landscape of mobility through cutting-edge
technologies. Central to this evolution is the integration of Artificial
Intelligence (AI) and learning algorithms, propelling vehicles into realms of
unprecedented autonomy. This paper provides a comprehensive exploration of the
evolutionary trajectory of AI within autonomous vehicles, tracing the journey
from foundational principles to the most recent advancements. Commencing with a
current landscape overview, the paper delves into the fundamental role of AI in
shaping the autonomous decision-making capabilities of vehicles. It elucidates
the steps involved in the AI-powered development life cycle in vehicles,
addressing ethical considerations and bias in AI-driven software development
for autonomous vehicles. The study presents statistical insights into the usage
and types of AI/learning algorithms over the years, showcasing the evolving
research landscape within the automotive industry. Furthermore, the paper
highlights the pivotal role of parameters in refining algorithms for both
trucks and cars, facilitating vehicles to adapt, learn, and improve performance
over time. It concludes by outlining different levels of autonomy, elucidating
the nuanced usage of AI and learning algorithms, and automating key tasks at
each level. Additionally, the document discusses the variation in software
package sizes across different autonomy levels
