TopoCoT @ WACV 2026

The 1st WACV 2026 Workshop on Robust and Generalized Lane Topology Understanding and HD Map Generation through CoT Design (TopoCoT) seeks to provide a platform for industry experts and academics to brainstorm and exchange ideas about road understanding CoT and its derived outstanding works to advance autonomous driving. Through a half-day in-person event, the workshop will showcase regular and demo paper presentations and invited talks from famous researchers in academia and industry. Additionally, TopoCoT will launch two open-source real-world CoT lane topology reasoning datasets. The workshop will host a challenge based on this dataset to assess the capabilities of language and computer vision models in addressing HD map generation challenges.

Important Dates/Links

Submission Deadline: TBD
Acceptance Notification Deadline: TBD
Camera Ready Papers Submission Deadline: TBD
Workshop Day: Mar 6 or Mar 7, 2026
Accepted papers will be published in the WACV 2026 proceedings.

Submission Guidelines and Review Process:

Authors are encouraged to submit high-quality, original (i.e., not been previously published or accepted for publication in substantially similar form in any peer-reviewed venue including journal, conference or workshop) research.
All submissions should follow the same template as for the main WACV2026 conference. Please refer to the WACV Author Kit.
The main paper has an 8-page limit, references do not count toward this. There is no limit on the number of pages in the supplementary material. Only pdf files are accepted.
Please submit your paper via this link: TBD
Authors of accepted papers will be notified via email by: TBD

Topics:

The workshop will cover a wide range of topics, including but not limited to:

LLM for lane topology understanding and HD map generation.
VLM for lane topology understanding and HD map generation.
CoT for lane topology understanding and HD map generation.
Reinforcement learning for lane topology understanding and HD map generation.
Transfer learning for lane topology understanding and HD map generation.
World modeling and simulation for lane topology understanding and HD map generation.
Evaluation metrics and benchmarks for lane topology understanding and HD map generation.

Organizers


Dr. Zhen Li Assistant Professor, CUHKSZ	Dr. Chao Zheng Tencent	Dr. Hongyang Li Assistant Professor, HKU

Program Committee


Yiming Yang CUHKSZ	Chao Zhan CUHKSZ	Kun Tang Tencent	Zhipeng Cao Tencent	Erlong Li Tencent

Xu Cao UIUC	Yifan Shen UIUC	Yunsheng Ma Purdue

Schedule

Workshop Date: Mar 6 or Mar 7, 2026

Workshop Location: TBD

Please find the tentative workshop schedule below:

Time	Event	Speaker
13:00 -- 13:10	Opening Remarks
13:10 -- 13:40	Topic: TBD	Hongyang Li (HKU)
13:40 -- 14:10	Topic: TBD	Hengshuang Zhao (HKU)
14:10 -- 14:40	Oral Accepted Papers	Authors
14:40 -- 15:00	Coffee Break & Poster Discussion
15:00 -- 15:30	Topic: TBD	Manmohan Chandraker (UC San Diego)
15:30 -- 16:00	Topic: TBD	Jim Rehg (UIUC)
16:00 -- 16:30	Topic: TBD	Ziran Wang (Purdue)
16:30 -- 17:00	Summary of the Workshop	Zhen Li (CUHKSZ)
17:00 -- 17:10	Closing Remarks

Contact Us

If you have any questions or inquiries, please contact us at email.

Citation

If the workshop inspire you, please consider citing our work:

@article{yang2025topo2seq,
  title={Topo2Seq: Enhanced Topology Reasoning via Topology Sequence Learning},
  author={Yang, Yiming and Luo, Yueru and He, Bingkun and Li, Erlong and Cao, Zhipeng and Zheng, Chao and Mei, Shuqi and Li, Zhen},
  journal={AAAI 2025},
  year={2025}
}

@article{yang2025topostreamer,
  title={TopoStreamer: Temporal Lane Segment Topology Reasoning in Autonomous Driving},
  author={Yang, Yiming and Luo, Yueru and He, Bingkun and Lin, Hongbin and Fu, Suzhong and Zheng, Chao and Cao, Zhipeng and Li, Erlong and Yan, Chao and Cui, Shuguang and others},
  journal={arXiv preprint arXiv:2507.00709},
  year={2025}
}

@article{yang2025fastopowm,
  title={FASTopoWM: Fast-Slow Lane Segment Topology Reasoning with Latent World Models},
  author={Yang, Yiming and Lin, Hongbin and Luo, Yueru and Fu, Suzhong and Zheng, Chao and Yan, Xinrui and Mei, Shuqi and Tang, Kun and Cui, Shuguang and Li, Zhen},
  journal={arXiv preprint arXiv:2507.23325},
  year={2025}
}

@article{luo2025reltopo,
  title={RelTopo: Enhancing Relational Modeling for Driving Scene Topology Reasoning},
  author={Luo, Yueru and Zhou, Changqing and Yang, Yiming and Li, Erlong and Zheng, Chao and Mei, Shuqi and Cui, Shuguang and Li, Zhen},
  journal={arXiv preprint arXiv:2506.13553},
  year={2025}
}