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CoIRL-AD-models

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upload info files, ckpts and result files

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  1. .gitattributes +1 -0
  2. README.md +31 -0
  3. assets/main_figure.jpg +3 -0
  4. ckpts/CoIRL-AD-sinapore/20251008_020028.log +0 -0
  5. ckpts/CoIRL-AD-sinapore/epoch_13.pth +3 -0
  6. ckpts/CoIRL-AD-sinapore/eval_metrics.xlsx +0 -0
  7. ckpts/CoIRL-AD/20250924_002311.log +0 -0
  8. ckpts/CoIRL-AD/epoch_21.pth +3 -0
  9. ckpts/CoIRL-AD/eval_metrics.xlsx +0 -0
  10. ckpts/CoIRL-AD/results.pkl +3 -0
  11. ckpts/LAW-sinapore/20251007_000457.log +0 -0
  12. ckpts/LAW-sinapore/epoch_13.pth +3 -0
  13. ckpts/LAW-sinapore/eval_metrics.xlsx +0 -0
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  16. ckpts/LAW/eval_metrics.xlsx +0 -0
  17. ckpts/LAW/results.pkl +3 -0
  18. info-files/vad_nuscenes_infos_temporal_train.pkl +3 -0
  19. info-files/vad_nuscenes_infos_temporal_train_singapore.pkl +3 -0
  20. info-files/vad_nuscenes_infos_temporal_val.pkl +3 -0
  21. info-files/vad_nuscenes_infos_temporal_val_longtail_L2.pkl +3 -0
  22. info-files/vad_nuscenes_infos_temporal_val_longtail_collision.pkl +3 -0
.gitattributes CHANGED
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  ---
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  license: apache-2.0
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  license: apache-2.0
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+ # CoIRL-AD: Collaborative–Competitive Imitation–Reinforcement Learning in Latent World Models for Autonomous Driving
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+
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+ <div align="center">
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+ <a href="https://seu-zxj.github.io/">Xiaoji Zheng</a>*,
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+ <a href="https://ziyuan-yang.github.io">Yangzi Yuan</a>*,
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+ <a href="https://github.com/Ian-cyh">Yanhao Chen</a>,
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+ <a href="https://github.com/doraemonaaaa">Yuhang Peng</a>,
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+ <a href="https://github.com/TANGXTONG1">Yuanrong Tang</a>,
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+ <a href="https://openreview.net/profile?id=~Gengyuan_Liu1">Gengyuan Liu</a>,
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+ <a href="https://scholar.google.com/citations?user=_Wrx_yEAAAAJ">Bokui Chen</a>‡ and
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+ <a href="https://scholar.google.com/citations?user=AktmI14AAAAJ">Jiangtao Gong</a>‡.
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+ <div>
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+ *: Equal contribution.
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+ ‡: Corresponding authors.
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+ </div>
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+
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+ <div>
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+ <a href="https://github.com/SEU-zxj/CoIRL-AD"><img alt="Static Badge" src="https://img.shields.io/badge/_-page-blue?style=flat&logo=githubpages&logoColor=white&logoSize=auto&labelColor=gray"></a>
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+ <a href="https://arxiv.org/"><img alt="Static Badge" src="https://img.shields.io/badge/arxiv-paper-red?logo=arxiv"></a>
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+ <a href="https://github.com/SEU-zxj/CoIRL-AD"><img alt="Static Badge" src="https://img.shields.io/badge/github-code-white?logo=github"></a>
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+ <a href="https://huggingface.co/Student-Xiaoji/CoIRL-AD-models"><img alt="Static Badge" src="https://img.shields.io/badge/hf-models-yellow?logo=huggingface"></a>
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+ </div>
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+
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+ </div>
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+
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+ **CoIRL-AD** introduces a dual-policy framework that unifies imitation learning (IL) and reinforcement learning (RL) through a collaborative–competitive mechanism within a latent world model.
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+ The framework enhances generalization and robustness in end-to-end autonomous driving without relying on external simulators.
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+
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+ ![main figure](./assets/main_figure.jpg)
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+
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+ ## For more details, please refer to our [github repo](https://github.com/SEU-zxj/CoIRL-AD).
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