Open access

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

Daisy X. M. Zheng Bingli Zhang Xinyu Wang Yixin Wang Yun-sheng Ye Gan Shen Guozhong Zhang

Aug 2026 · Engineering Research Express · Vol 8 · 0 citations · 31 references

Physics

TL;DR

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.

Abstract

Autonomous parking requires accurate perception, reliable trajectory generation, and physically feasible vehicle motion in highly constrained environments. Existing end-to-end parking methods mainly rely on single-frame scene understanding and often neglect temporal motion priors and vehicle kinematic characteristics, limiting their planning accuracy and deployment robustness. To address these limitations, this paper proposes a vision-based end-to-end autonomous parking framework trained through imitation learning. The proposed framework 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. These components jointly improve trajectory prediction accuracy while maintaining vehicle manoeuvrability in parking scenarios. Extensive experiments demonstrate that the proposed method achieves over 25% lower trajectory prediction error than the baseline model on the ParkingE2E dataset and attains an 89.84% parking success rate in closed-loop CARLA simulations. The results verify the effectiveness of combining temporal context modeling, attention-based feature interaction, and kinematic constraint learning for end-to-end autonomous parking.

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