The advancement of autonomous driving systems hinges on the ability to achieve low-latency and high-accuracy perception. To address this critical need, this paper introduces Dynamic Routing Network (DyRoNet), a low-rank enhanced dynamic routing framework designed for streaming perception in autonomous driving systems. DyRoNet integrates a suite of pre-trained branch networks, each meticulously fine-tuned to function under distinct environmental conditions. At its core, the framework offers a speed router module, developed to assess and route input data to the most suitable branch for processing. This approach not only addresses the inherent limitations of conventional models in adapting to diverse driving conditions but also ensures the balance between performance and efficiency. Extensive experimental evaluations demonstrate the adaptability of DyRoNet to diverse branch selection strategies, resulting in significant performance enhancements across different scenarios. This work establishes a new benchmark for streaming perception and provides valuable engineering insights for future work.

翻译：自动驾驶系统的进步依赖于实现低延迟与高精度感知能力。为满足这一关键需求，本文提出动态路由网络（DyRoNet），一种专为自动驾驶系统流式感知设计的低秩增强型动态路由框架。DyRoNet集成了一组预训练的支路网络，每个网络均经过精细微调以在特定环境条件下运行。该框架的核心是一个速度路由模块，其功能在于评估输入数据并将其路由至最合适的支路进行处理。该方法不仅解决了传统模型在适应多样化驾驶条件时的固有局限，同时确保了性能与效率的平衡。大量实验评估表明，DyRoNet能够灵活适应不同的支路选择策略，从而在各种场景下实现显著的性能提升。本研究为流式感知确立了新的基准，并为未来工作提供了宝贵的工程洞见。