QUEST: Query Stream for Practical Cooperative Perception

TL;DR

This paper introduces QUEST, a novel query cooperation framework for cooperative perception that enhances interpretability and flexibility, improving perception accuracy and robustness in vehicle-infrastructure scenarios.

Contribution

The paper proposes the concept of query cooperation and develops QUEST, enabling interpretable, flexible feature interaction through query streams among agents.

Findings

QUEST improves perception accuracy on DAIR-V2X-Seq dataset.

Query cooperation enhances robustness to packet dropout.

The framework demonstrates effective cross-agent feature interaction.

Abstract

Cooperative perception can effectively enhance individual perception performance by providing additional viewpoint and expanding the sensing field. Existing cooperation paradigms are either interpretable (result cooperation) or flexible (feature cooperation). In this paper, we propose the concept of query cooperation to enable interpretable instance-level flexible feature interaction. To specifically explain the concept, we propose a cooperative perception framework, termed QUEST, which let query stream flow among agents. The cross-agent queries are interacted via fusion for co-aware instances and complementation for individual unaware instances. Taking camera-based vehicle-infrastructure perception as a typical practical application scene, the experimental results on the real-world dataset, DAIR-V2X-Seq, demonstrate the effectiveness of QUEST and further reveal the advantage of the query cooperation paradigm on transmission flexibility and robustness to packet dropout. We hope our work can further facilitate the cross-agent representation interaction for better cooperative perception in practice.