GraphBEV++: Multi-Modal Feature Alignment for Autonomous Driving

• URL: http://arxiv.org/abs/2606.16354v1
• Date: Mon, 15 Jun 2026 07:50:33 GMT
• Title: GraphBEV++: Multi-Modal Feature Alignment for Autonomous Driving
• Abstract summary: Feature misalignment in BEV perception is a critical yet often overlooked challenge in autonomous driving.<n>We propose a robust multi-modal fusion framework, GraphBEV++, which systematically mitigates projection-induced misalignment.
• Score: 30.796057505726925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feature misalignment in BEV perception is a critical yet often overlooked challenge in autonomous driving, especially under calibration uncertainties between LiDAR and camera sensors. To address this issue, we propose a robust multi-modal fusion framework, GraphBEV++, which systematically mitigates projection-induced misalignment. The framework consists of two key modules: LocalAlign-v2 and GlobalAlign-v2. LocalAlign-v2 introduces neighborhood-aware depth features via graph matching to correct local misalignment. It supports both LSS-based and query-based BEV representations, making it compatible with BEVFusion and BEVFormer architectures for consistent cross-paradigm alignment. GlobalAlign-v2 encompasses two variants: Deformable and Diffusion. The Deformable variant addresses global misalignment in LSS-based multi-modal BEV by explicitly learning cross-modal feature offsets. In contrast, the Diffusion variant targets implicit misalignment in query-based BEV by injecting noise to simulate misalignment and employing a denoising process to recover aligned features. Experimental results show that GraphBEV++ achieves state-of-the-art performance under misalignment noise on nuScenes and Waymo subset, improves long-range detection on Argoverse2, and generalizes effectively to the 3D occupancy prediction task, consistently improving occupancy estimation accuracy and robustness under both clean and noisy settings. Furthermore, GraphBEV++ effectively alleviates misalignment issues in end-to-end autonomous driving. Compared with five baselines (UniAD, VAD, FusionAD, MomAD, and WoTE), it demonstrates superior performance in both open-loop (nuScenes) and closed-loop (Bench2Drive and NAVSIM) evaluations across perception, prediction, and planning tasks.

Related papers

• VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation [50.18051979020699]
  VIPS is a benchmark for cooperative autonomous driving in V2I settings based on pseudo-simulation.<n>CoS-V2X is a cooperative planning framework based on sparse representations.
  arXiv  Detail & Related papers  (2026-09-02T11:32:52Z)
• Open-Vocabulary BEV Segmentation with 3D-Aware Geometric Constraints [20.26519299938903]
  We introduce open-vocabulary BEV segmentation (OVBS) to recognize categories beyond the training set.<n>OVBEVSeg is a geometry-aware OVBS framework that enhances efficient Gaussian splatting (GS)-based unprojection.<n>On the nuScenes dataset, OVBEVSeg achieves state-of-the-art performance, outperforming closed-set methods by 15.3 mIoU on unseen categories.
  arXiv  Detail & Related papers  (2026-06-23T09:43:12Z)
• Bench2Drive-VL: Benchmarks for Closed-Loop Autonomous Driving with Vision-Language Models [50.22099309218635]
  In autonomous driving, closed-loop evaluation has become widely recognized as a more reliable validation method than open-loop evaluation.<n>We present Bench2Drive-VL, an extension of Bench2Drive that brings closed-loop evaluation to VLM-based driving.
  arXiv  Detail & Related papers  (2026-04-01T11:38:46Z)
• DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Driving [65.7087560656003]
  Generative diffusion models for end-to-end autonomous driving often suffer from mode collapse.<n>We propose DiffusionDriveV2, which leverages reinforcement learning to constrain low-quality modes and explore for superior trajectories.<n>This significantly enhances the overall output quality while preserving the inherent multimodality of its core Gaussian Mixture Model.
  arXiv  Detail & Related papers  (2025-12-08T17:29:52Z)
• Vehicle-to-Infrastructure Collaborative Spatial Perception via Multimodal Large Language Models [41.00138090010061]
  A lightweight, plug-and-play bird's-eye view (BEV) injection connector is proposed to overcome the limitations of large language models (MLLMs)<n>Ray tracing is developed to generate RGB, LiDAR, GPS, and wireless signal data across varied sensing scenarios.<n> Simulation results show that the proposed BEV injection framework consistently improved performance across all tasks.
  arXiv  Detail & Related papers  (2025-09-04T02:57:47Z)
• Dual-Domain Homogeneous Fusion with Cross-Modal Mamba and Progressive Decoder for 3D Object Detection [12.77616717954945]
  Fusing LiDAR and image features in a homogeneous BEV domain has become popular for 3D object detection in autonomous driving.<n>However, this paradigm is constrained by the excessive feature compression.<n>We propose a Dual-Domain Homogeneous Fusion network (DDHFusion) to overcome these limitations.
  arXiv  Detail & Related papers  (2025-03-12T01:55:02Z)
• GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detection [18.21607858133675]
  We propose a robust fusion framework called Graph BEV to integrate LiDAR and camera BEV features.<n>Our framework outperforms BEV Fusion by 8.3% under conditions with misalignment noise.
  arXiv  Detail & Related papers  (2024-03-18T15:00:38Z)
• U-BEV: Height-aware Bird's-Eye-View Segmentation and Neural Map-based Relocalization [81.76044207714637]
  Relocalization is essential for intelligent vehicles when GPS reception is insufficient or sensor-based localization fails.<n>Recent advances in Bird's-Eye-View (BEV) segmentation allow for accurate estimation of local scene appearance.<n>This paper presents U-BEV, a U-Net inspired architecture that extends the current state-of-the-art by allowing the BEV to reason about the scene on multiple height layers before flattening the BEV features.
  arXiv  Detail & Related papers  (2023-10-20T18:57:38Z)
• X-Align++: cross-modal cross-view alignment for Bird's-eye-view segmentation [44.58686493878629]
  X-Align is a novel end-to-end cross-modal and cross-view learning framework for BEV segmentation. X-Align significantly outperforms the state-of-the-art by 3 absolute mIoU points on nuScenes and KITTI-360 datasets.
  arXiv  Detail & Related papers  (2023-06-06T15:52:55Z)
• X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation [44.95630790801856]
  X-Align is a novel end-to-end cross-modal and cross-view learning framework for BEV segmentation. X-Align significantly outperforms the state-of-the-art by 3 absolute mIoU points on nuScenes.
  arXiv  Detail & Related papers  (2022-10-13T06:42:46Z)
• Higher Performance Visual Tracking with Dual-Modal Localization [106.91097443275035]
  Visual Object Tracking (VOT) has synchronous needs for both robustness and accuracy. We propose a dual-modal framework for target localization, consisting of robust localization suppressingors via ONR and the accurate localization attending to the target center precisely via OFC.
  arXiv  Detail & Related papers  (2021-03-18T08:47:56Z)
• Inter-class Discrepancy Alignment for Face Recognition [55.578063356210144]
  We propose a unified framework calledInter-class DiscrepancyAlignment(IDA) IDA-DAO is used to align the similarity scores considering the discrepancy between the images and its neighbors. IDA-SSE can provide convincing inter-class neighbors by introducing virtual candidate images generated with GAN.
  arXiv  Detail & Related papers  (2021-03-02T08:20:08Z)
• LiDAR-based Panoptic Segmentation via Dynamic Shifting Network [56.71765153629892]
  LiDAR-based panoptic segmentation aims to parse both objects and scenes in a unified manner. We propose the Dynamic Shifting Network (DS-Net), which serves as an effective panoptic segmentation framework in the point cloud realm. Our proposed DS-Net achieves superior accuracies over current state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-11-24T08:44:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.