DaDe: Delay-adaptive Detector for Streaming Perception

TL;DR

DaDe introduces a delay-adaptive detection model for streaming perception in autonomous driving, effectively handling environment changes during processing with real-time delay reflection, outperforming current methods on the Argoverse-HD dataset.

Contribution

The paper proposes a novel delay-adaptive detection framework with feature queue and select modules, enabling real-time delay compensation without extra computational costs.

Findings

Outperforms state-of-the-art methods on Argoverse-HD dataset

Effectively handles environment changes during streaming perception

Achieves higher accuracy in various delayed scenarios

Abstract

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