Control Map Distribution using Map Query Bank for Online Map Generation

• URL: http://arxiv.org/abs/2504.03868v1
• Date: Fri, 04 Apr 2025 18:47:42 GMT
• Title: Control Map Distribution using Map Query Bank for Online Map Generation
• Authors: Ziming Liu, Leichen Wang, Ge Yang, Xinrun Li, Xingtao Hu, Hao Sun, Guangyu Gao,
• Abstract summary: Reliable autonomous driving systems require high-definition (HD) map for planning and navigation.<n>OMG has become an alternative low-cost solution to build a local HD map.<n>OMG learns HD map predictions from an initial map queries distribution.<n>It is important to keep point-level information in map queries when interacting with BEV feature map.
• Score: 18.325267388089696
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reliable autonomous driving systems require high-definition (HD) map that contains detailed map information for planning and navigation. However, pre-build HD map requires a large cost. Visual-based Online Map Generation (OMG) has become an alternative low-cost solution to build a local HD map. Query-based BEV Transformer has been a base model for this task. This model learns HD map predictions from an initial map queries distribution which is obtained by offline optimization on training set. Besides the quality of BEV feature, the performance of this model also highly relies on the capacity of initial map query distribution. However, this distribution is limited because the limited query number. To make map predictions optimal on each test sample, it is essential to generate a suitable initial distribution for each specific scenario. This paper proposes to decompose the whole HD map distribution into a set of point representations, namely map query bank (MQBank). To build specific map query initial distributions of different scenarios, low-cost standard definition map (SD map) data is introduced as a kind of prior knowledge. Moreover, each layer of map decoder network learns instance-level map query features, which will lose detailed information of each point. However, BEV feature map is a point-level dense feature. It is important to keep point-level information in map queries when interacting with BEV feature map. This can also be solved with map query bank method. Final experiments show a new insight on SD map prior and a new record on OpenLaneV2 benchmark with 40.5%, 45.7% mAP on vehicle lane and pedestrian area.

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