2COOOL: 2nd Workshop on the Challenge Of Out-Of-Label Hazards in Autonomous Driving
On the map: End-to-end autonomous driving → VLMs for autonomous driving
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in VLMs for autonomous driving, one of 9 directions in End-to-end autonomous driving. Builds on 13 of its 17 references that are in the map. First posted on arXiv 18 Aug 2025.
Builds on
13 of 17 references.
Closest ancestors · 1
Same direction, newest first
- 2024Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark27 Dec
Other references · 12
- 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
- 2019The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentationcited by 122
- 2019DADA: Driver Attention Prediction in Driving Accident Scenarioscited by 70
- 2022Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wildcited by 70
- 2024A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlookcited by 66
- 2024Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Setscited by 25
- 2025Nexar Dashcam Collision Prediction Dataset and Challengecited by 21
- 2024Can OOD Object Detectors Learn from Foundation Models?cited by 10
- 2024COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous Drivingcited by 8
- 2025SMART-Vision: Survey of Modern Action Recognition Techniques in Visioncited by 8
- 2024Pose2Trajectory: Using Transformers on Body Pose to Predict Tennis Player's Trajectorycited by 5
- 2025SGNetPose+: Stepwise Goal-Driven Networks with Pose Information for Trajectory Prediction in Autonomous Drivingcited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2025Addressing Out-of-Label Hazard Detection in Dashcam Videos: Insights from the COOOL Challenge
- 2025Safety2Drive: Safety-Critical Scenario Benchmark for the Evaluation of Autonomous Driving
- 2023DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving
- 2026Localized Conformal Safety Monitoring with Vision-Language Models for Autonomous Driving
- 2025Towards a Multi-Agent Vision-Language System for Zero-Shot Novel Hazardous Object Detection for Autonomous Driving Safety
- 2025INSIGHT: Enhancing Autonomous Driving Safety through Vision-Language Models on Context-Aware Hazard Detection and Edge Case Evaluation
- 2026Safe2Drive: Evaluating Safe Driving Behaviors of E2E Autonomous Driving Models
- 2026A knowledge-augmented dataset of high-risk driving scenarios with LLM annotations for autonomous driving