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Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles

  • Collaborative perception in automated vehicles leverages the exchange of information between agents, aiming to elevate perception results. Previous camera-based collaborative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the environment. However, these approaches fall short in offering a comprehensive 3D environmental prediction. To bridge this gap, we introduce the first method for collaborative 3D semantic occupancy prediction. Particularly, it improves local 3D semantic occupancy predictions by hybrid fusion of (i) semantic and occupancy task features, and (ii) compressed orthogonal attention features shared between vehicles. Additionally, due to the lack of a collaborative perception dataset designed for semantic occupancy prediction, we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation. The experimental findings highlight that: (i) our collaborative semantic occupancy predictions excel above theCollaborative perception in automated vehicles leverages the exchange of information between agents, aiming to elevate perception results. Previous camera-based collaborative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the environment. However, these approaches fall short in offering a comprehensive 3D environmental prediction. To bridge this gap, we introduce the first method for collaborative 3D semantic occupancy prediction. Particularly, it improves local 3D semantic occupancy predictions by hybrid fusion of (i) semantic and occupancy task features, and (ii) compressed orthogonal attention features shared between vehicles. Additionally, due to the lack of a collaborative perception dataset designed for semantic occupancy prediction, we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation. The experimental findings highlight that: (i) our collaborative semantic occupancy predictions excel above the results from single vehicles by over 30%, and (ii) models anchored on semantic occupancy outpace state-of-the-art collaborative 3D detection techniques in subsequent perception applications, showcasing enhanced accuracy and enriched semantic-awareness in road environments.show moreshow less

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Metadaten
Author:Rui SongORCiD, Chenwei Liang, Hu Cao, Zhiran Yan, Walter Zimmer, Markus Gross, Andreas FestagORCiD, Alois KnollORCiD
Language:English
Document Type:Preprint
Year of first Publication:2024
Publisher:arXiv
Place of publication:Ithaca
Pages:16
Review:nein
Open Access:ja
URN:urn:nbn:de:bvb:573-46035
Related Identifier:https://doi.org/10.48550/arXiv.2402.07635
Faculties / Institutes / Organizations:Fakultät Elektro- und Informationstechnik
CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)
Institut für Innovative Mobilität (IIMo)
Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"
Licence (German):License Logo Creative Commons BY 4.0
Release Date:2024/03/06