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Autonomous driving systems require real-time environmental perception toensure user safety and experience. Streaming perception is a task of reportingthe current state of the world, which is used to evaluate the delay andaccuracy of autonomous driving systems. In real-world applications, factorssuch as hardware limitations and high temperatures inevitably cause delays inautonomous driving systems, resulting in the offset between the model outputand the world state. In order to solve this problem, this paper propose theMulti- Timestep Detector (MTD), an end-to-end detector which uses dynamicrouting for multi-branch future prediction, giving model the ability to resistdelay fluctuations. A Delay Analysis Module (DAM) is proposed to optimize theexisting delay sensing method, continuously monitoring the model inferencestack and calculating the delay trend. Moreover, a novel Timestep Branch Module(TBM) is constructed, which includes static flow and adaptive flow toadaptively predict specific timesteps according to the delay trend. Theproposed method has been evaluated on the Argoverse-HD dataset, and theexperimental results show that it has achieved state-of-the-art performanceacross various delay settings.

MTD: Multi-Timestep Detector for Delayed Streaming Perception