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 Routering 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 demonstrating the adaptability of DyRoNet to diverse branch selection strategies, resulting in significant performance enhancements across different scenarios. This work not only establishes a new benchmark for streaming perception but also provides valuable engineering insights for future work.

翻译：自动驾驶系统的发展依赖于实现低延迟、高精度感知的能力。为应对这一关键需求，本文提出动态路由网络（DyRoNet）——一种用于自动驾驶系统流式感知的低秩增强动态路由框架。DyRoNet集成了一套预训练分支网络，每个分支均针对特定环境条件进行精细调优。该框架的核心是一个速度路由器模块，用于评估输入数据并将其路由至最合适的分支进行处理。该方法不仅解决了传统模型在适应多样化驾驶条件时固有的局限性，还确保了性能与效率之间的平衡。大量实验评估表明，DyRoNet能够适应多种分支选择策略，在不同场景下均实现了显著的性能提升。本工作不仅为流式感知建立了新的基准，也为未来研究提供了宝贵的工程见解。