# QUEST: Query Stream for Practical Cooperative Perception

arXiv: https://arxiv.org/abs/2308.01804 · HTML: https://mlsanity.com/paper/2308.01804/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 31 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 29 of 29)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://mlsanity.com/paper/2110.06922/) (2021)
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2203.05625/) (2022)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021)
- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision](https://mlsanity.com/paper/2211.10439/) (2022)
- [V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction](https://mlsanity.com/paper/2008.07519/) (2020)
- [DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection](https://mlsanity.com/paper/2204.05575/) (2022)
- [Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps](https://mlsanity.com/paper/2209.12836/) (2022)
- [F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds](https://mlsanity.com/paper/1909.06459/) (2019)
- [Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds](https://mlsanity.com/paper/1905.05265/) (2019)
- [An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection](https://mlsanity.com/paper/1904.09730/) (2019)
- [When2com: Multi-Agent Perception via Communication Graph Grouping](https://mlsanity.com/paper/2006.00176/) (2020)
- [V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting](https://mlsanity.com/paper/2305.05938/) (2023)
- [Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors](https://mlsanity.com/paper/1912.12147/) (2019)
- [COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles](https://mlsanity.com/paper/2205.02222/) (2022)
- [MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries](https://mlsanity.com/paper/2205.00613/) (2022)
- [Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges](https://mlsanity.com/paper/2301.06262/) (2023)
- [Collaboration Helps Camera Overtake LiDAR in 3D Detection](https://mlsanity.com/paper/2303.13560/) (2023)
- [Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking](https://mlsanity.com/paper/2302.03802/) (2023)
- [Vehicle-Infrastructure Cooperative 3D Object Detection via Feature Flow Prediction](https://mlsanity.com/paper/2303.10552/) (2023)
- [A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperation](https://mlsanity.com/paper/2208.10590/) (2022)
- [Calibration-free BEV Representation for Infrastructure Perception](https://mlsanity.com/paper/2303.03583/) (2023)
- [VIMI: Vehicle-Infrastructure Multi-view Intermediate Fusion for Camera-based 3D Object Detection](https://mlsanity.com/paper/2303.10975/) (2023)

## Built on by (most cited first, 23 of 23)

- [MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings](https://mlsanity.com/paper/2310.00400/) (2023)
- [V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception](https://mlsanity.com/paper/2411.10962/) (2024)
- [Vehicle-to-Everything Cooperative Perception for Autonomous Driving](https://mlsanity.com/paper/2310.03525/) (2023)
- [Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark](https://mlsanity.com/paper/2503.06983/) (2025)
- [SparseAlign: A Fully Sparse Framework for Cooperative Object Detection](https://mlsanity.com/paper/2503.12982/) (2025)
- [CoCMT: Communication-Efficient Cross-Modal Transformer for Collaborative Perception](https://mlsanity.com/paper/2503.13504/) (2025)
- [CoopTrack: Exploring End-to-End Learning for Efficient Cooperative Sequential Perception](https://mlsanity.com/paper/2507.19239/) (2025)
- [Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition](https://mlsanity.com/paper/2507.21610/) (2025)
- [CoDynTrust: Robust Asynchronous Collaborative Perception via Dynamic Feature Trust Modulus](https://mlsanity.com/paper/2502.08169/) (2025)
- [Systematic Literature Review on Vehicular Collaborative Perception -- A Computer Vision Perspective](https://mlsanity.com/paper/2504.04631/) (2025)
- [INSTINCT: Instance-Level Interaction Architecture for Query-Based Collaborative Perception](https://mlsanity.com/paper/2509.23700/) (2025)
- [SparseCoop: Cooperative Perception with Kinematic-Grounded Queries](https://mlsanity.com/paper/2512.06838/) (2025)
- [EIMC: Efficient Instance-aware Multi-modal Collaborative Perception](https://mlsanity.com/paper/2603.02532/) (2026)
- [StreamLTS: Query-based Temporal-Spatial LiDAR Fusion for Cooperative Object Detection](https://mlsanity.com/paper/2407.03825/) (2024)
- [Beyond Blanket Masking: Examining Granularity for Privacy Protection in Images Captured by Blind and Low Vision Users](https://mlsanity.com/paper/2508.09245/) (2025)
- [WhisperNet: A Scalable Solution for Bandwidth-Efficient Collaboration](https://mlsanity.com/paper/2603.01708/) (2026)
- [Is Intermediate Fusion All You Need for UAV-based Collaborative Perception?](https://mlsanity.com/paper/2504.21774/) (2025)
- [Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception](https://mlsanity.com/paper/2604.09206/) (2026)
- [BOLT: Online Lightweight Adaptation for Preparation-Free Heterogeneous Cooperative Perception](https://mlsanity.com/paper/2605.00405/) (2026)
- [RayFusion: Ray Fusion Enhanced Collaborative Visual Perception](https://mlsanity.com/paper/2510.08017/) (2025)
- [Towards Efficient 3D Object Detection for Vehicle-Infrastructure Collaboration via Risk-Intent Selection](https://mlsanity.com/paper/2601.03001/) (2026)
- [UECP: Uncertainty-Enhanced Collaborative Perception](https://mlsanity.com/paper/2606.23046/) (2026)
- [VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation](https://mlsanity.com/paper/2609.02462/) (2026)

## Abstract (arXiv)

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.