Mind the Map! Accounting for Existing Maps When Estimating Online HDMaps from Sensors

Rémy Sun, Li Yang, Diane Lingrand, Frederic Precioso; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1671-1681

While HDMaps are a crucial component of autonomous driving they are expensive to acquire and maintain. Estimating these maps from sensors therefore promises to significantly lighten costs. These estimations however overlook existing HDMaps with current methods at most geolocalizing low quality maps or considering a general database of known maps. In this paper we propose to account for existing maps of the precise situation studied when estimating HDMaps. To prove this we identify 3 reasonable types of useful existing maps (minimalist noisy and outdated). We then introduce MapEX a novel online HDMap estimation framework that accounts for existing maps. MapEX achieves this by encoding map elements into query tokens and by refining the matching algorithm used to train classic query based map estimation models. We demonstrate that MapEX brings significant improvements on the nuScenes dataset. For instance MapEX - given noisy maps - improves by 38% over the MapTRv2 detector it is based on and by 8% over the current SOTA.