Unveiling the Hidden: Online Vectorized HD Map Construction with Clip-Level Token Interaction and Propagation

TL;DR

MapUnveiler introduces a clip-level approach for vectorized HD map construction that leverages temporal information and occlusion handling, significantly improving accuracy in autonomous driving scenarios.

Contribution

The paper presents a novel clip-level framework that explicitly models occluded map elements and propagates inter-clip information, advancing the state-of-the-art in HD map construction.

Findings

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

Outperforms existing methods with +10.7% mAP in occluded scenes.

Efficiently utilizes temporal information without redundant computation.

Abstract

Predicting and constructing road geometric information (e.g., lane lines, road markers) is a crucial task for safe autonomous driving, while such static map elements can be repeatedly occluded by various dynamic objects on the road. Recent studies have shown significantly improved vectorized high-definition (HD) map construction performance, but there has been insufficient investigation of temporal information across adjacent input frames (i.e., clips), which may lead to inconsistent and suboptimal prediction results. To tackle this, we introduce a novel paradigm of clip-level vectorized HD map construction, MapUnveiler, which explicitly unveils the occluded map elements within a clip input by relating dense image representations with efficient clip tokens. Additionally, MapUnveiler associates inter-clip information through clip token propagation, effectively utilizing long-term temporal map information. MapUnveiler runs efficiently with the proposed clip-level pipeline by avoiding redundant computation with temporal stride while building a global map relationship. Our extensive experiments demonstrate that MapUnveiler achieves state-of-the-art performance on both the nuScenes and Argoverse2 benchmark datasets. We also showcase that MapUnveiler significantly outperforms state-of-the-art approaches in a challenging setting, achieving +10.7% mAP improvement in heavily occluded driving road scenes. The project page can be found at https://mapunveiler.github.io.