Mind the map! Accounting for existing map information when estimating online HDMaps from sensor

TL;DR

This paper introduces MapEX, an online HDMap estimation framework that leverages existing map information to improve accuracy and reduce costs in autonomous driving applications.

Contribution

The paper proposes a novel method to incorporate various types of existing maps into HDMap estimation, enhancing accuracy over previous approaches.

Findings

MapEX improves HDMap estimation accuracy by 38% over MapTRv2 with noisy maps.

MapEX outperforms current state-of-the-art methods by 8%.

Incorporating existing maps significantly benefits online HDMap estimation.

Abstract

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 We identify 3 reasonable types of useful existing maps (minimalist, noisy, and outdated).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