LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraints

Mengmeng Liu, Hao Cheng*, Lin Chen, Hellward Broszio, Jiangtao Li, Runjiang Zhao, Monika Sester, Michael Ying Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

14 Citations (Scopus)
1 Downloads (Pure)

Abstract

Existing trajectory prediction methods for autonomous driving typically rely on one-stage trajectory prediction models, which condition future trajectories on observed trajectories combined with fused scene information. However, they often struggle with complex scene constraints, such as those encountered at intersections. To this end, we present a novel method, called LAformer. It uses an attention-based temporally dense lane-aware estimation module to continuously estimate the likelihood of the alignment between motion dynamics and scene information extracted from an HD map. Additionally, unlike one-stage prediction models, LAformer utilizes predictions from the first stage as anchor trajectories. It leverages a second-stage motion refinement module to further explore temporal consistency across the complete time horizon. Extensive experiments on nuScenes and Argoverse 1 demonstrate that LAformer achieves excellent generalized performance for multimodal trajectory prediction. The source code of LAformer is available at https://github.com/mengmengliu1998/LAformer.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages2039-2049
Number of pages11
ISBN (Electronic)979-8-3503-6547-4
ISBN (Print)979-8-3503-6548-1
DOIs
Publication statusE-pub ahead of print/First online - 27 Sept 2024
Event2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024 - Seattle, United States
Duration: 16 Jun 202422 Jun 2024

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
PublisherIEEE
Volume2024
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2024
Country/TerritoryUnited States
CitySeattle
Period16/06/2422/06/24

Keywords

  • 2025 OA procedure
  • Motion refinement
  • Multimodal
  • Trajectory prediction
  • Lane-aware selection

Fingerprint

Dive into the research topics of 'LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraints'. Together they form a unique fingerprint.

Cite this