Title: Learning Global Representaion from Queries for Vectorized HD Map Construction
Primary area: applications to robotics, autonomy, planning
Keywords: Perception, Autonomous+Driving, HDMap, Mapless+Driving

Abstract: The online construction of vectorized high-definition (HD) maps is a cornerstone of modern autonomous driving systems. State-of-the-art approaches, particularly those based on the DETR framework, formulate this as an instance detection problem. However, their reliance on independent, learnable object queries results in a predominantly local query perspective, neglecting the inherent global representation within HD maps. In this work, we propose \textbf{MapGR} (\textbf{G}lobal \textbf{R}epresentation learning for HD \textbf{Map} construction), an architecture designed to learn and utilize a global representations from queries. Our method introduces two synergistic modules: a Global Representation Learning (GRL) module, which encourages the distribution of all queries to better align with the global map through a carefully designed holistic segmentation task, and a Global Representation Guidance (GRG) module, which endows each individual query with explicit, global-level contextual information to facilitate its optimization. Evaluations on the nuScenes and Argoverse2 datasets validate the efficacy of our approach, demonstrating substantial improvements in mean Average Precision (mAP) compared to leading baselines. The code is provided in the supplementary materials and will be released upon acceptance of this paper.

TL;DR: MapGR builds better HD maps for self-driving cars, it learns the overall "big picture" of the map. This global context helps it place each query more accurately, leading to significant performance improvements over existing methods.