Gan Shen

HCAD-Net: end-to-end parking network with historical context and attention-based dual-decoder

A vision-based end-to-end autonomous parking framework trained through imitation learning that introduces a historical context fusion encoder to capture temporal dependencies from past vehicle motions, a dual-stream attention decoder to enhance interaction between scene features and trajectory representations, and kinematic-aware auxiliary losses to enforce smooth and feasible trajectory generation.