NeuroFlow: Development of lightweight and efficient model integration scheduling strategy for autonomous driving system
This addresses real-time and safety constraints in autonomous driving systems, though it appears incremental in its approach.
The paper tackles the problem of optimizing data flow and scheduling for autonomous driving systems to ensure real-time performance and safety, resulting in stable inference and effective control validated in actual vehicles across various scenarios.
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.