STrajNet: Occupancy Flow Prediction via Multi-modal Swin Transformer. (arXiv:2208.00394v1 [cs.CV])

Making an accurate prediction of occupancy and flow is essential to enable
better safety and interaction for autonomous vehicles under complex traffic
scenarios. This work proposes STrajNet: a multi-modal Swin Transformerbased
framework for effective scene occupancy and flow predictions. We employ Swin
Transformer to encode the image and interaction-aware motion representations
and propose a cross-attention module to inject motion awareness into grid cells
across different time steps. Flow and occupancy predictions are then decoded
through temporalsharing Pyramid decoders. The proposed …

arxiv cv flow prediction swin transformer