TY - INPR A1 - Song, Rui A1 - Liang, Chenwei A1 - Cao, Hu A1 - Yan, Zhiran A1 - Zimmer, Walter A1 - Gross, Markus A1 - Festag, Andreas A1 - Knoll, Alois T1 - Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles N2 - 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 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. UR - https://doi.org/10.48550/arXiv.2402.07635 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2402.07635 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46035 PB - arXiv CY - Ithaca ER -