离线向量化地图是高精度自动驾驶与地图服务的关键基础设施。现有方法主要依赖单一自车轨迹，存在固有的视角不足问题：基于记忆的方法虽通过聚合自车轨迹帧延长观测时间，却缺乏揭示被遮挡区域所需的几何空间多样性。引入周围车辆视角可提供互补视点，但简单融合会带来三方面挑战：大规模候选车辆池导致的计算开销、近共线视角引发的冗余性，以及位姿误差与遮挡伪影引入的噪声。本文提出 OptiMVMap，将多车地图构建重新建模为“先选择、后融合”的问题，以系统性应对上述挑战。其中，最优车辆选择（OVS）模块策略性地选取一组精简的辅助车辆子集，以最大程度降低自车视角下被遮挡区域的不确定性，从而缓解计算开销与冗余性问题；跨车注意力机制（CVA）与语义感知噪声滤波器（SNF）则在鸟瞰图（BEV）级融合前，分别实现位姿鲁棒的对齐与伪影抑制，以应对噪声挑战。该目标导向的流程相比无差别聚合，仅需更少视角即可生成更完整、拓扑更保真的地图。在 nuScenes 和 Argoverse2 数据集上，OptiMVMap 分别将 MapTRv2 的 mAP 提升了 +10.5 和 +9.3；在 nuScenes 上，其 mAP 分别较记忆增强基线 MVMap 和 HRMapNet 提升 +6.2 和 +3.8。结果表明，以不确定性为指导的辅助车辆选择，对高效且准确的多车向量化地图构建至关重要。代码已开源：https://github.com/DanZeDong/OptiMVMap。

Offline vectorized maps constitute critical infrastructure for high-precision autonomous driving and mapping services. Existing approaches rely predominantly on single ego-vehicle trajectories, which fundamentally suffer from viewpoint insufficiency: while memory-based methods extend observation time by aggregating ego-trajectory frames, they lack the spatial diversity needed to reveal occluded regions. Incorporating views from surrounding vehicles offers complementary perspectives, yet naive fusion introduces three key challenges: computational cost from large candidate pools, redundancy from near-collinear viewpoints, and noise from pose errors and occlusion artifacts. We present OptiMVMap, which reformulates multi-vehicle mapping as a select-then-fuse problem to address these challenges systematically. An Optimal Vehicle Selection (OVS) module strategically identifies a compact subset of helpers that maximally reduce ego-centric uncertainty in occluded regions, addressing computation and redundancy challenges. Cross-Vehicle Attention (CVA) and Semantic-aware Noise Filter (SNF) then perform pose-tolerant alignment and artifact suppression before BEV-level fusion, addressing the noise challenge. This targeted pipeline yields more complete and topologically faithful maps with substantially fewer views than indiscriminate aggregation. On nuScenes and Argoverse2, OptiMVMap improves MapTRv2 by +10.5 mAP and +9.3 mAP, respectively, and surpasses memory-augmented baselines MVMap and HRMapNet by +6.2 mAP and +3.8 mAP on nuScenes. These results demonstrate that uncertainty-guided selection of helper vehicles is essential for efficient and accurate multi-vehicle vectorized mapping. The code is released at https://github.com/DanZeDong/OptiMVMap.