  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
