Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion
  in Connected Automated Vehicles
Rui Song Chenwei Liang Hu Cao Zhiran Yan Walter Zimmer Markus Gross Andreas Festag Alois Knoll
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.