SWA-SOP: Spatially-aware Window Attention for Semantic Occupancy Prediction in Autonomous Driving

Understand

Perception systems in autonomous driving rely on sensors such as LiDAR and cameras to perceive the 3D environment.

• However, due to occlusions and data sparsity, these sensors often fail to capture complete information.
• Semantic Occupancy Prediction (SOP) addresses this challenge by inferring both occupancy and semantics of unobserved regions.
• Existing transformer-based SOP methods lack explicit modeling of spatial structure in attention computation, resulting in limited geometric awareness and poor performance in sparse or occluded areas.