  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. We identify 3 reasonable types of useful existing maps (minimalist,
noisy, and outdated). We also 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.
