Lanelet2 for nuScenes: Enabling Spatial Semantic Relationships and Diverse Map-based Anchor Paths

Alexander Naumann, Felix Hertlein, Daniel Grimm, Maximilian Zipf, Steffen Thoma, Achim Rettinger, Lavdim Halilaj, Juergen Luettin, Stefan Schmid, Holger Caesar

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
18 Downloads (Pure)

Abstract

Motion prediction and planning are key components to enable autonomous driving. Although high definition (HD) maps provide important contextual information that constrains the action space of traffic participants, most approaches are not able to fully exploit this heterogeneous information. In this work, we enrich the existing road geometry of the popular nuScenes dataset and convert it into the open-source map framework Lanelet2. This allows easy access to the road topology and thus, enables the usage of (1) spatial semantic information, such as agents driving on intersecting roads and (2) map-generated anchor paths for target vehicles that can help to improve trajectory prediction performance. Further, we present DMAP, a simple, yet effective approach for diverse map-based anchor path generation and filtering. We show that combining DMAP with ground truth velocity profile information yields high-quality motion prediction results on nuScenes (MinADE5=1.09, MissRate5,2=0.18, Offroad rate=0.00). While it is obviously unfair to compare us against the state-of-the-art, it shows that our HD map accurately depicts the road geometry and topology. Future approaches can leverage this by focusing on data-driven sampling of map-based anchor paths and estimating velocity profiles. Moreover, our HD map can be used for map construction tasks and supplement perception. Code and data are made publicly available at https://felixhertlein.github.io/lanelet4nuscenes.
Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023
EditorsLisa O'Conner
Place of PublicationPiscataway
PublisherIEEE
Pages3248-3257
ISBN (Electronic)979-8-3503-0249-3
ISBN (Print)979-8-3503-0250-9
DOIs
Publication statusPublished - 2023
Event2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) - Vancouver, Canada
Duration: 17 Jun 202324 Jun 2023

Conference

Conference2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Country/TerritoryCanada
City Vancouver
Period17/06/2324/06/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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