Accurate BEV map prediction is a critical component of autonomous driving systems, enabling downstream planning and control. Recent approaches such as Mask2Map achieve strong performance by predicting vectorized, instance-level map elements directly from multi-view images. However, existing BEV map prediction models operate largely independently of object-level scene understanding, despite the fact that traffic participants frequently occlude or implicitly encode information about road geometry. In this work, we propose a multitask architecture that jointly predicts BEV maps and 3D objects, explicitly enabling information exchange between the two tasks. Building upon Mask2Map, we introduce an object detection branch and incorporate cross-task feature fusion at both the BEV feature level and the instance-query level via deformable attention and cross-attention mechanisms. This design allows map instances to leverage object context while remaining robust to occlusions caused by traffic participants. Experimental results on the nuScenes and Argoverse 2 datasets demonstrate that our approach consistently improves BEV map prediction accuracy, with particularly significant gains of 21% mAP in scenes containing heavy object occlusion.

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