MTD: Multi-Timestep Detector for Delayed Streaming Perception
Yihui Huang Ningjiang Chen
Autonomous driving systems require real-time environmental perception to
ensure user safety and experience. Streaming perception is a task of reporting
the current state of the world, which is used to evaluate the delay and
accuracy of autonomous driving systems. In real-world applications, factors
such as hardware limitations and high temperatures inevitably cause delays in
autonomous driving systems, resulting in the offset between the model output
and the world state. In order to solve this problem, this paper propose the
Multi- Timestep Detector (MTD), an end-to-end detector which uses dynamic
routing for multi-branch future prediction, giving model the ability to resist
delay fluctuations. A Delay Analysis Module (DAM) is proposed to optimize the
existing delay sensing method, continuously monitoring the model inference
stack and calculating the delay trend. Moreover, a novel Timestep Branch Module
(TBM) is constructed, which includes static flow and adaptive flow to
adaptively predict specific timesteps according to the delay trend. The
proposed method has been evaluated on the Argoverse-HD dataset, and the
experimental results show that it has achieved state-of-the-art performance
across various delay settings.