Zero-shot hazard identification in Autonomous Driving: A Case Study on the COOOL Benchmark
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F08997187%3A_____%2F25%3AN0000002" target="_blank" >RIV/08997187:_____/25:N0000002 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/25:00382096 RIV/49777513:23520/25:43976567
Result on the web
<a href="https://openaccess.thecvf.com/content/WACV2025W/COOOL/papers/Picek_Zero-shot_hazard_identification_in_Autonomous_Driving_A_Case_Study_on_WACVW_2025_paper.pdf" target="_blank" >https://openaccess.thecvf.com/content/WACV2025W/COOOL/papers/Picek_Zero-shot_hazard_identification_in_Autonomous_Driving_A_Case_Study_on_WACVW_2025_paper.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/WACVW65960.2025.00074" target="_blank" >10.1109/WACVW65960.2025.00074</a>
Alternative languages
Result language
angličtina
Original language name
Zero-shot hazard identification in Autonomous Driving: A Case Study on the COOOL Benchmark
Original language description
This paper presents our submission to the COOOL competition, a novel benchmark for detecting and classifying out-of-label hazards in autonomous driving. Our approach integrates diverse methods across three core tasks: (i) driver reaction detection, (ii) hazard object identification, and (iii) hazard captioning. We propose kernel-based change point detection on bounding boxes and optical flow dynamics for driver reaction detection to analyze motion patterns. For hazard identification, we combined a naive proximity-based strategy with object classification using a pre-trained ViT model. At last, for hazard captioning, we used the Molmo vision-language model with tailored prompts to generate precise and context-aware descriptions of rare and low-resolution hazards. The proposed pipeline outperformed the baseline methods by a large margin, reducing the relative error by 33%, and scored 2nd on the final leaderboard consisting of 32 teams.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/TM05000009" target="_blank" >TM05000009: Secured information for CCAM operation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
ISBN
979-8-3315-3662-6
ISSN
2572-4398
e-ISSN
2690-621X
Number of pages
10
Pages from-to
611-620
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
Tucson
Event location
Tucson
Event date
Feb 28, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001510213100064