  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.
