Bird's-eye-view (BEV) representations are the dominant paradigm for 3D perception in autonomous driving, providing a unified spatial canvas where detection and segmentation features are geometrically registered to the same physical coordinate system. However, existing radar-camera fusion methods treat these tasks in isolation, missing the opportunity for cross-task feature sharing: object-level geometric cues from detection can sharpen segmentation, while dense road-layout context from segmentation can anchor detection. We propose \textbf{CTAB} (Cross-Task Attention Bridge), a bidirectional module that exchanges features between detection and segmentation branches via multi-scale deformable attention in shared BEV space. CTAB is integrated into a multi-task framework with an Instance Normalization-based segmentation decoder and learnable BEV upsampling to provide a more detailed BEV representation. On nuScenes, CTAB improves segmentation on 7 classes over the joint multi-task baseline at essentially neutral detection. On a 4-class subset (drivable area, pedestrian crossing, walkway, vehicle), our joint multi-task model achieves 51.0 mIoU-4 while simultaneously providing competitive 3D detection.

翻译：鸟瞰视角（BEV）表征是自动驾驶中三维感知的主流范式，它提供统一的空间坐标系，使检测与分割特征在几何上对齐到同一物理坐标系。然而，现有的雷达-相机融合方法将这两类任务孤立处理，错失了跨任务特征共享的机会：检测任务中的目标级几何线索可提升分割精度，而分割任务中的密集道路布局上下文可辅助检测锚定。本文提出**CTAB**（跨任务注意力桥接），这是一个双向模块，通过共享BEV空间中的多尺度可变形注意力机制，在检测与分割分支之间交换特征。CTAB被集成到多任务框架中，该框架包含基于实例归一化的分割解码器和可学习BEV上采样模块，以提供更精细的BEV表征。在nuScenes数据集上，CTAB在联合多任务基准基础上，对7类分割任务实现性能提升，同时检测性能基本保持中性。在4类子集（可行驶区域、人行横道、人行道、车辆）上，我们的联合多任务模型达到51.0 mIoU-4，同时提供具有竞争力的3D检测性能。