  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.
