2024/03/08 by Xiang Huang, Huang, Xiang, Zhi-Qi Cheng +9 · 1 citation
Computer Science · Neuroscience · #Artificial Intelligence (cs.AI) #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #Energy Efficient Wireless Sensor Networks #FOS: Computer and information sciences #Multimedia (cs.MM) #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2403.05050
openalex publication_date 2024/03/08 · openalex created_date 2024/03/13 · openalex updated_date 2026/07/28
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.