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This paper proposes a specialized autonomous driving system that takes intoaccount the unique constraints and characteristics of automotive systems,aiming for innovative advancements in autonomous driving technology. Theproposed system systematically analyzes the intricate data flow in autonomousdriving and provides functionality to dynamically adjust various factors thatinfluence deep learning models. Additionally, for algorithms that do not relyon deep learning models, the system analyzes the flow to determine resourceallocation priorities. In essence, the system optimizes data flow and schedulesefficiently to ensure real-time performance and safety. The proposed system wasimplemented in actual autonomous vehicles and experimentally validated acrossvarious driving scenarios. The experimental results provide evidence of thesystem's stable inference and effective control of autonomous vehicles, markinga significant turning point in the development of autonomous driving systems.

NeuroFlow: Development of lightweight and efficient model integration scheduling strategy for autonomous driving system