NeuroFlow：面向自动驾驶系统的轻量高效模型集成调度策略开发
NeuroFlow: Development of lightweight and efficient model integration scheduling strategy for autonomous driving system
AI总结：
本研究提出名为NeuroFlow的自动驾驶专用系统，通过优化数据流、动态调整深度学习模型影响因素及确定非深度学习算法资源优先级，在实车多场景实验中验证了稳定推理与有效控制能力，为自动驾驶系统发展提供新突破。
摘要：
本文提出了一种专门的自动驾驶系统，该系统充分考虑汽车系统的独特约束与特性，旨在实现自动驾驶技术的创新性突破。所提系统系统分析了自动驾驶中复杂的数据流，具备动态调整影响深度学习模型的各类因素的功能。此外，对于不依赖深度学习模型的算法，该系统通过分析流程来确定资源分配优先级。从本质上讲，该系统通过优化数据流与高效调度来保障实时性能与安全性。研究人员将所提系统部署在实际自动驾驶车辆中，并在多种驾驶场景下开展实验验证。实验结果表明，该系统能够实现稳定推理，可对自动驾驶车辆进行有效控制，标志着自动驾驶系统发展的重要转折点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
