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CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers

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arxiv 2207.02202 v2 pith:DFZN7UWA submitted 2022-07-05 cs.CV

Runsheng Xu , Zhengzhong Tu , Hao Xiang , Wei Shao , Bolei Zhou , Jiaqi Ma This is my paper
classification cs.CV
keywords cobevtsegmentationmulti-agentperceptionperformancesemanticsingle-agentsystems
verification ladder T0 review T1 audit T2 compute T3 formal
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Bird's eye view (BEV) semantic segmentation plays a crucial role in spatial sensing for autonomous driving. Although recent literature has made significant progress on BEV map understanding, they are all based on single-agent camera-based systems. These solutions sometimes have difficulty handling occlusions or detecting distant objects in complex traffic scenes. Vehicle-to-Vehicle (V2V) communication technologies have enabled autonomous vehicles to share sensing information, dramatically improving the perception performance and range compared to single-agent systems. In this paper, we propose CoBEVT, the first generic multi-agent multi-camera perception framework that can cooperatively generate BEV map predictions. To efficiently fuse camera features from multi-view and multi-agent data in an underlying Transformer architecture, we design a fused axial attention module (FAX), which captures sparsely local and global spatial interactions across views and agents. The extensive experiments on the V2V perception dataset, OPV2V, demonstrate that CoBEVT achieves state-of-the-art performance for cooperative BEV semantic segmentation. Moreover, CoBEVT is shown to be generalizable to other tasks, including 1) BEV segmentation with single-agent multi-camera and 2) 3D object detection with multi-agent LiDAR systems, achieving state-of-the-art performance with real-time inference speed. The code is available at https://github.com/DerrickXuNu/CoBEVT.

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Cited by 13 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CoGoal3D: Collaborative 3D Object Detection with 3D-Aware Fusion and Refinement

    cs.CV 2026-07 conditional novelty 6.0 of 10

    CoGoal3D, a broadcast two-stage collaborative 3D detector with 3D-aware fusion and point-reconstruction refinement, reports state-of-the-art 3D AP on three real-world V2X datasets.

  2. Variational Inference for Bird's Eye View Segmentation in Autonomous Driving

    cs.CV 2026-07 conditional novelty 6.0 of 10

    TVB combines a conditional variational autoencoder, normalizing flows, and attention-based fusion to produce bird's-eye-view segmentation from multiple car cameras, reporting small but consistent IoU gains on nuScenes...

  3. TruckV2X: A Truck-Centered Perception Dataset

    cs.RO 2025-07 conditional novelty 6.0 of 10

    TruckV2X is introduced as the first truck-centered cooperative perception dataset, providing multi-agent LiDAR and camera data from trailers, CAVs, and RSUs in CARLA simulation.

  4. Disentangling Instance and Scene Contexts for 3D Semantic Scene Completion

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A dual-stream BEV architecture that separates instance and scene class queries achieves state-of-the-art mIoU of 17.35 on SemanticKITTI and 20.55 on SSCBench-KITTI-360.

  5. End-to-End Generation of City-Scale Vectorized Maps by Crowdsourced Vehicles

    cs.RO 2025-07 conditional novelty 6.0 of 10

    Fusing vectorized map elements from multiple crowdsourced vehicles with a trip-aware transformer improves online HD map accuracy over single-vehicle methods on the Navinfo Dataset.

  6. AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration

    cs.CV 2025-06 conditional novelty 6.0 of 10

    AirV2X-Perception is a 6.73-hour simulated dataset and benchmark for collaborative perception with up to 5 vehicles, 5 roadside units, and 5 drones.

  7. HeteroPROPMT: A Real-time and Privacy-Preserving Heterogeneous Collaborative Perception Framework

    cs.CV 2026-07 conditional novelty 5.0 of 10

    A prompt-based, privacy-preserving heterogeneity-alignment framework that uses low-rank FiLM to adapt BEV features and an autoencoder-based classifier for metadata-free modality routing.

  8. QuantV2X: A Fully Quantized Multi-Agent System for Cooperative Perception

    cs.CV 2025-09 conditional novelty 5.0 of 10

    QuantV2X shows that a fully quantized multi-agent fusion system reduces end-to-end latency by 3.2x and improves system-level mAP30 by 9.5 over a full-precision system on the V2X-Real dataset.

  9. Progressive Bird's Eye View Perception for Safety-Critical Autonomous Driving: A Comprehensive Survey

    cs.RO 2025-08 conditional novelty 5.0 of 10

    A safety-critical survey that organizes BEV perception into single-modality, multimodal, and collaborative stages and consolidates robustness evidence that multimodal fusion degrades far less than single-modality perc...

  10. CRUISE: Cooperative Reconstruction and Editing in V2X Scenarios using Gaussian Splatting

    cs.CV 2025-07 conditional novelty 5.0 of 10

    CRUISE reconstructs real V2X driving scenes as editable Gaussians, then shows that training on its generated data improves 3D detection and tracking on the V2X-Seq benchmark.

  11. LLM-Assisted Coalition Formation for Cooperative Perception in Autonomous Driving

    cs.NI 2026-08 reject novelty 4.0 of 10

    A DPP-based, communication-aware coalition selection step feeds an LLM with diverse vehicle summaries, evaluated on OPV2V and V2V4Real.

  12. Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance

    cs.AI 2025-08 reject novelty 4.0 of 10

    A lightweight vision-language model on an edge device fuses roadside hazard alerts with onboard camera views to adjust trajectories, and the authors report a 77% simulated collision reduction over a vision-only baseline.

  13. Optimizing Cooperative Multi-Object Tracking using Graph Signal Processing

    cs.CV 2025-06 reject novelty 3.0 of 10

    Cooperative MOT via graph Laplacian smoothing is proposed, but the smoothing step returns the input detections exactly, invalidating the central claim.

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