Enhancing BEV Perception Through Vehicle-Road Cooperative Systems: An Attention-Based Cross-View Fusion Approach

Engineering notes

Evaluated on DAIR-V2X dataset, the framework achieves an improvement of 14.03% mAP over baselines from BEV while reducing unobserved areas coverage by 30.46% with real-time efficiency.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

This paper presents a novel vehicle-to-infrastructure (V2I) cooperative perception framework to address inherent limitations of bird's eye view (BEV) systems in autonomous driving. Using sensor constraints that include occlusion and restricted field of view, our framework integrates sensor systems mounted on the infrastructure with perception of the ego vehicle through three innovations. First, a geometry-aware feature alignment module that resolving cross-view discrepancies via projective geometry transformations. Second, attention-optimized fusion architecture (AFFENet) with dual-attention mechanisms for channel recalibration and spatial-contextual enhancement. Third, a multiscale dynamic aggregation protocol enabling context-aware fusion of heterogeneous sensor data. Evaluated on DAIR-V2X dataset, the framework achieves an improvement of 14.03% mAP over baselines from BEV while reducing unobserved areas coverage by 30.46% with real-time efficiency. This work establishes a new paradigm for cooperative perception systems, providing theoretical foundations and practical implementations for multiperspective sensor fusion in intelligent transportation ecosystems. The proposed methodologies address critical challenges in geometric alignment in cross-view and adaptive feature fusion, ultimately advancing robust autonomous driving systems through infrastructure-vehicle perception synergy.

Links and sources

• Official / arXiv page

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