In the field of autonomous driving, online high-definition (HD) map reconstruction is crucial for planning tasks. Recent research has developed several high-performance HD map reconstruction models to meet this necessity. However, the point sequences within the instance vectors may be jittery or jagged due to prediction bias, which can impact subsequent tasks. Therefore, this paper proposes the Anti-disturbance Map reconstruction framework (ADMap). To mitigate point-order jitter, the framework consists of three modules: Multi-Scale Perception Neck, Instance Interactive Attention (IIA), and Vector Direction Difference Loss (VDDL). By exploring the point-order relationships between and within instances in a cascading manner, the model can monitor the point-order prediction process more effectively. ADMap achieves state-of-the-art performance on the nuScenes and Argoverse2 datasets. Extensive results demonstrate its ability to produce stable and reliable map elements in complex and changing driving scenarios. Code and more demos are available at https://github.com/hht1996ok/ADMap.

翻译：在自动驾驶领域，在线高清（HD）地图重建对于规划任务至关重要。近期研究已开发出多种高性能高清地图重建模型以满足这一需求。然而，由于预测偏差，实例向量内的点序列可能出现抖动或锯齿状，从而影响后续任务。为此，本文提出抗干扰地图重建框架（ADMap）。为缓解节点抖动问题，该框架包含多尺度感知颈部（Multi-Scale Perception Neck）、实例交互注意力（IIA）和向量方向差损失（VDDL）三个模块。通过级联方式探索实例间及实例内的点序关系，模型可更有效地监控点序预测过程。ADMap在nuScenes和Argoverse2数据集上达到了最先进性能。大量结果表明，该模型能在复杂多变的驾驶场景中生成稳定可靠的地图元素。代码及更多演示见https://github.com/hht1996ok/ADMap。