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.

翻译：本文提出一种专门针对自动驾驶系统的设计方法，充分考虑了汽车系统的独特约束与特性，旨在推动自动驾驶技术的创新突破。所提方法系统性地分析了自动驾驶中复杂的数据流，并提供动态调整影响深度学习模型的多重因素的机制。此外，对于不依赖深度学习模型的算法，该方法通过分析数据流来确定资源分配优先级。本质上，该系统通过优化数据流与高效调度，确保实时性能与安全性。该方案已在实车环境下实现，并通过多种驾驶场景进行了实验验证。实验结果证明了该系统在自动驾驶车辆稳定推理与有效控制方面的能力，标志着自动驾驶系统发展的重要转折点。