MapExpert: Online HD Map Construction with Simple and Efficient Sparse Map Element Expert
Dapeng Zhang, Dayu Chen, Peng Zhi, Yinda Chen, Zhenlong Yuan, Chenyang, Li, Sunjing, Rui Zhou, Qingguo Zhou

TL;DR
MapExpert introduces an expert-based online HD map construction method that accurately distinguishes various non-cubic map elements using sparse experts and an efficient temporal fusion module, achieving state-of-the-art results in autonomous driving perception.
Contribution
The paper proposes a novel expert-based approach with an auxiliary loss and an efficient temporal fusion module for improved online HD map construction.
Findings
Achieves state-of-the-art performance on nuScenes and Argoverse2 datasets.
Effectively distinguishes different non-cubic map elements.
Maintains high efficiency in online HD map construction.
Abstract
Constructing online High-Definition (HD) maps is crucial for the static environment perception of autonomous driving systems (ADS). Existing solutions typically attempt to detect vectorized HD map elements with unified models; however, these methods often overlook the distinct characteristics of different non-cubic map elements, making accurate distinction challenging. To address these issues, we introduce an expert-based online HD map method, termed MapExpert. MapExpert utilizes sparse experts, distributed by our routers, to describe various non-cubic map elements accurately. Additionally, we propose an auxiliary balance loss function to distribute the load evenly across experts. Furthermore, we theoretically analyze the limitations of prevalent bird's-eye view (BEV) feature temporal fusion methods and introduce an efficient temporal fusion module called Learnable Weighted Moving…
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Taxonomy
Topics3D Modeling in Geospatial Applications · Augmented Reality Applications
