  This paper proposes a specialized autonomous driving system that takes into
account the unique constraints and characteristics of automotive systems,
aiming for innovative advancements in autonomous driving technology. The
proposed system systematically analyzes the intricate data flow in autonomous
driving and provides functionality to dynamically adjust various factors that
influence deep learning models. Additionally, for algorithms that do not rely
on deep learning models, the system analyzes the flow to determine resource
allocation priorities. In essence, the system optimizes data flow and schedules
efficiently to ensure real-time performance and safety. The proposed system was
implemented in actual autonomous vehicles and experimentally validated across
various driving scenarios. The experimental results provide evidence of the
system's stable inference and effective control of autonomous vehicles, marking
a significant turning point in the development of autonomous driving systems.
