ADMap: Anti-disturbance framework for reconstructing online vectorized HD map

TL;DR

ADMap is a novel framework that enhances online HD map reconstruction for autonomous driving by reducing point jitter and improving stability through multi-scale perception, attention mechanisms, and specialized loss functions.

Contribution

The paper introduces ADMap, a new anti-disturbance framework that effectively mitigates point jitter in vectorized HD maps during online reconstruction.

Findings

Achieves state-of-the-art performance on nuScenes and Argoverse2 datasets.

Produces stable and reliable map elements in complex driving scenarios.

Effectively reduces point-order jitter in vectorized HD maps.

Abstract

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.