2COOOL: 2nd Workshop on the Challenge of Out-of-Label Hazards in Autonomous Driving
Journal
Proceedings - 2025 IEEE/CVF International Conference on Computer Vision Workshops, ICCV-W 2025
Start Page
764
End Page
771
ISBN
[9798331589882]
Date Issued
2025-10-19
Author(s)
AlShami, Ali K.
Rabinowitz, Ryan
Shoman, Maged
Fang, Jianwu
Picek, Lukas
Cruz, Steve
Lam, Khang Nhut
Kamod, Nachiket
Li, Lei-Lei
Kalita, Jugal
Boult, Terrance E.
Abstract
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: It's 2025-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 dynamic mix of academic and industry participation.
Event(s)
2025 IEEE/CVF International Conference on Computer Vision Workshops, ICCV-W 2025
Subjects
Accident Detection
Autonomous Driving
Foundation Models
Hazard Detection
LLM
Multimodal
Novelty
Open-Set
Open-vocabulary
Open-world
Out of Distribution
VLLM
Publisher
IEEE
Type
conference paper