2COOOL: 2nd Workshop on the Challenge Of Out-Of-Label Hazards in Autonomous Driving
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912558002208768 |
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| author | AlShami, Ali K. Rabinowitz, Ryan Shoman, Maged Fang, Jianwu Picek, Lukas Lo, Shao-Yuan Cruz, Steve Lam, Khang Nhut Kamod, Nachiket Li, Lei-Lei Kalita, Jugal Boult, Terrance E. |
| author_facet | AlShami, Ali K. Rabinowitz, Ryan Shoman, Maged Fang, Jianwu Picek, Lukas Lo, Shao-Yuan Cruz, Steve Lam, Khang Nhut Kamod, Nachiket Li, Lei-Lei Kalita, Jugal Boult, Terrance E. |
| contents | As the computer vision community advances autonomous driving algorithms, integrating vision-based insights with sensor data remains essential for improving perception, decision making, planning, prediction, simulation, and control. Yet we must ask: Why don't we have entirely safe self-driving cars yet? A key part of the answer lies in addressing novel scenarios, one of the most critical barriers to real-world deployment. Our 2COOOL workshop provides a dedicated forum for researchers and industry experts to push the state of the art in novelty handling, including out-of-distribution hazard detection, vision-language models for hazard understanding, new benchmarking and methodologies, and safe autonomous driving practices. The 2nd Workshop on the Challenge of Out-of-Label Hazards in Autonomous Driving (2COOOL) will be held at the International Conference on Computer Vision (ICCV) 2025 in Honolulu, Hawaii, on October 19, 2025. We aim to inspire the development of new algorithms and systems for hazard avoidance, drawing on ideas from anomaly detection, open-set recognition, open-vocabulary modeling, domain adaptation, and related fields. Building on the success of its inaugural edition at the Winter Conference on Applications of Computer Vision (WACV) 2025, the workshop will feature a mix of academic and industry participation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_21080 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 2COOOL: 2nd Workshop on the Challenge Of Out-Of-Label Hazards in Autonomous Driving AlShami, Ali K. Rabinowitz, Ryan Shoman, Maged Fang, Jianwu Picek, Lukas Lo, Shao-Yuan Cruz, Steve Lam, Khang Nhut Kamod, Nachiket Li, Lei-Lei Kalita, Jugal Boult, Terrance E. Computer Vision and Pattern Recognition Robotics 68T45 (Machine vision and scene understanding) I.2.10; I.4.8 As the computer vision community advances autonomous driving algorithms, integrating vision-based insights with sensor data remains essential for improving perception, decision making, planning, prediction, simulation, and control. Yet we must ask: Why don't we have entirely safe self-driving cars yet? A key part of the answer lies in addressing novel scenarios, one of the most critical barriers to real-world deployment. Our 2COOOL workshop provides a dedicated forum for researchers and industry experts to push the state of the art in novelty handling, including out-of-distribution hazard detection, vision-language models for hazard understanding, new benchmarking and methodologies, and safe autonomous driving practices. The 2nd Workshop on the Challenge of Out-of-Label Hazards in Autonomous Driving (2COOOL) will be held at the International Conference on Computer Vision (ICCV) 2025 in Honolulu, Hawaii, on October 19, 2025. We aim to inspire the development of new algorithms and systems for hazard avoidance, drawing on ideas from anomaly detection, open-set recognition, open-vocabulary modeling, domain adaptation, and related fields. Building on the success of its inaugural edition at the Winter Conference on Applications of Computer Vision (WACV) 2025, the workshop will feature a mix of academic and industry participation. |
| title | 2COOOL: 2nd Workshop on the Challenge Of Out-Of-Label Hazards in Autonomous Driving |
| topic | Computer Vision and Pattern Recognition Robotics 68T45 (Machine vision and scene understanding) I.2.10; I.4.8 |
| url | https://arxiv.org/abs/2508.21080 |