RAAlign: Multi-Agent Feature Alignment and Reliability-Aware Fusion for Vehicle–Infrastructure Cooperative Object Localization and Perception

Existing V2X cooperative perception methods mainly rely on LiDAR-only or LiDAR-camera inputs，and remain vulnerable to adverse weather and cross-agent alignment errors. We introduce RAAlign, a reliability-aware LiDAR–4D Radar fusion framework for cooperative 3D object detection. RAAlign exchanges quality-gated complementary messages, promotes object-level consistency across agents and modalities using an InfoNCE objective , and compensates for residual offsets before weighting neighbor features using spatial and temporal reliability for attention aggregation. Experiments on V2X-R show that RAAlign improves average [email protected] from 69.05 to 72.99 on AttFuse and from 70.24 to 73.64 with MDD, with the largest weather gain reaching 2.24 percentage points in snow. These results provide evidence that reliability-aware cross-agent LiDAR–4D Radar fusion improves cooperative perception under adverse weather and imperfect alignment.