DaDe: Delay-adaptive Detector for Streaming Perception
This work addresses a critical issue for autonomous driving systems by improving streaming perception under real-world hardware constraints, though it is incremental as it builds on existing methods.
The paper tackles the problem of real-time object detection in autonomous driving by addressing processing delays that cause outdated perceptions, achieving state-of-the-art performance on the Argoverse-HD dataset under delayed conditions.
Recognizing the surrounding environment at low latency is critical in autonomous driving. In real-time environment, surrounding environment changes when processing is over. Current detection models are incapable of dealing with changes in the environment that occur after processing. Streaming perception is proposed to assess the latency and accuracy of real-time video perception. However, additional problems arise in real-world applications due to limited hardware resources, high temperatures, and other factors. In this study, we develop a model that can reflect processing delays in real time and produce the most reasonable results. By incorporating the proposed feature queue and feature select module, the system gains the ability to forecast specific time steps without any additional computational costs. Our method is tested on the Argoverse-HD dataset. It achieves higher performance than the current state-of-the-art methods(2022.12) in various environments when delayed . The code is available at https://github.com/danjos95/DADE