  Autonomous driving requires the model to perceive the environment and (re)act
within a low latency for safety. While past works ignore the inevitable changes
in the environment after processing, streaming perception is proposed to
jointly evaluate the latency and accuracy into a single metric for video online
perception. In this paper, instead of searching trade-offs between accuracy and
speed like previous works, we point out that endowing real-time models with the
ability to predict the future is the key to dealing with this problem. We build
a simple and effective framework for streaming perception. It equips a novel
DualFlow Perception module (DFP), which includes dynamic and static flows to
capture the moving trend and basic detection feature for streaming prediction.
Further, we introduce a Trend-Aware Loss (TAL) combined with a trend factor to
generate adaptive weights for objects with different moving speeds. Our simple
method achieves competitive performance on Argoverse-HD dataset and improves
the AP by 4.9% compared to the strong baseline, validating its effectiveness.
Our code will be made available at https://github.com/yancie-yjr/StreamYOLO.
