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Paper Citation Record · LEDGER

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark

As of 18 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2412.19944.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.19944 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:50:34.605775Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1f3c3637-8622-4f95-a977-263b14e87bad · outbound

This paper cites GPT-4 Technical Report.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark GPT-4 Technical Report

Reference 1

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Observation 4de9ff84-6bf4-422c-b21b-7a1776d08f86 · outbound

This paper cites Flamingo: a visual language model for few-shot learning.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Flamingo: a visual language model for few-shot learning

Reference 2

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Observation d8a828e3-b7fb-420f-aaca-def3b95f3b68 · outbound

This paper cites COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous Driving.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous Driving

Reference 3

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Observation f949dff7-46ae-44f0-8377-660d282bbb9f · outbound

This paper cites A kernel multiple change-point algorithm via model selection.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark A kernel multiple change-point algorithm via model selection

Reference 4

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Observation 80ebaf23-821c-49ba-83c1-258a975c5bde · outbound

This paper cites Towards open set deep networks.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Towards open set deep networks

Reference 5

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Observation 515a99ef-b60a-4d21-b99e-426f925297e5 · outbound

This paper cites API design for machine learning soft- ware: experiences from the scikit-learn project.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark API design for machine learning soft- ware: experiences from the scikit-learn project

Reference 6

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Observation 348b539b-eb6d-400c-99e8-882f2a8a9ca9 · outbound

This paper cites nuscenes: A multi- modal dataset for autonomous driving.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark nuscenes: A multi- modal dataset for autonomous driving

Reference 7

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Observation de73db71-6c34-4c9d-b5a8-14a29d3b4a42 · outbound

This paper cites New efficient algorithms for multiple change- point detection with reproducing kernels.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark New efficient algorithms for multiple change- point detection with reproducing kernels

Reference 8

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Observation 15b84629-12d0-4789-af53-a5133e7f0c54 · outbound

This paper cites Recent advance- ments in end-to-end autonomous driving using deep learn- ing: A survey.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Recent advance- ments in end-to-end autonomous driving using deep learn- ing: A survey

Reference 9

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Observation 30e42b1c-a5c6-4424-9c34-21dcf531781b · outbound

This paper cites Operational open-set recognition and post- max refinement.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Operational open-set recognition and post- max refinement

Reference 10

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Observation af018409-6805-4991-9b7b-44faa6d72e6d · outbound

This paper cites Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Reference 11

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Observation cc4bf96f-820c-4b86-9b0f-e0b34a087053 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Imagenet: A large-scale hierarchical image database

Reference 12

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Observation 00c26e63-c9c3-4cb4-903b-d86e997e1618 · outbound

This paper cites Di Lillo, T.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Di Lillo, T

Reference 13

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Observation 4ff45cb3-80e7-4063-bd04-05ddb5b268ae · outbound

This paper cites Trust your generator (trygen): Enhancing out-of-model scope detection.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Trust your generator (trygen): Enhancing out-of-model scope detection

Reference 14

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Source-reported events for the cited work

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Observation 29604c03-0b76-46be-9d83-76fa9b53de63 · outbound

This paper cites Two-frame motion estimation based on polynomial expansion.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Two-frame motion estimation based on polynomial expansion

Reference 15

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Observation e412ab66-acc9-4477-a043-96b5309ebe5c · outbound

This paper cites A Review on Deep Learning Techniques Applied to Semantic Segmentation.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark A Review on Deep Learning Techniques Applied to Semantic Segmentation

Reference 16

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Observation 40f5dc6c-030d-4a00-a622-1166bc50996f · outbound

This paper cites A baseline for detect- ing misclassified and out-of-distribution examples in neural networks.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark A baseline for detect- ing misclassified and out-of-distribution examples in neural networks

Reference 17

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Source-reported events for the cited work

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Observation e6e60e66-87aa-4cc9-b0c6-76c558381161 · outbound

This paper cites Evaluation of out-of-distribution detection perfor- mance on autonomous driving datasets.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Evaluation of out-of-distribution detection perfor- mance on autonomous driving datasets

Reference 18

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Observation c9c57da2-da68-499e-a819-85377826007f · outbound

This paper cites Planning-oriented autonomous driving.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Planning-oriented autonomous driving

Reference 19

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Observation 73b61e35-4a08-4737-b9ec-98a69c1709f5 · outbound

This paper cites Open source computer vision library.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Open source computer vision library

Reference 20

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Observation bae2e6bc-3924-45f8-8a19-6c18ef99f672 · outbound

This paper cites Op- timal detection of changepoints with a linear computa- tional cost.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Op- timal detection of changepoints with a linear computa- tional cost

Reference 21

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Observation 1f5521e7-ecc3-41a1-a138-f5fa561b7d9b · outbound

This paper cites Cifar- 100 (canadian institute for advanced research).

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Cifar- 100 (canadian institute for advanced research)

Reference 22

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Observation 777f2a86-3ae8-48af-a836-33eba8fba63a · outbound

This paper cites Large car-following data based on lyft level-5 open dataset: Following autonomous vehicles vs.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Large car-following data based on lyft level-5 open dataset: Following autonomous vehicles vs

Reference 23

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Observation 6076cda2-1688-4b0e-be26-5a12692ce286 · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 2cb3ec1f-c993-4dc1-989b-8a03f5dab627 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Learning transferable visual models from natural language supervi- sion

Reference 25

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Observation 6125408e-fa46-4a67-987b-d0dbca05b9b7 · outbound

This paper cites Deep learning serves traffic safety analysis: A forward-looking review.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Deep learning serves traffic safety analysis: A forward-looking review

Reference 26

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Observation 541d3f7c-0b96-40ed-b6c8-3596a2d256ba · outbound

This paper cites The extreme value machine.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark The extreme value machine

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation c9ed0361-9abf-43f8-a3a3-4c3d7e8cf112 · outbound

This paper cites Scalability in perception for autonomous driving: Waymo open dataset.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Scalability in perception for autonomous driving: Waymo open dataset

Reference 28

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Observation 397f0935-ae39-4052-911e-e05c5c66eee4 · outbound

This paper cites Selec- tive review of offline change point detection methods.Signal Processing, 167:107299, 2020.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Selec- tive review of offline change point detection methods.Signal Processing, 167:107299, 2020

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation feb4af5e-22bd-4b92-94bb-05b6a7c780d8 · outbound

This paper cites Image Captioners Sometimes Tell More Than Images They See.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Image Captioners Sometimes Tell More Than Images They See

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation b0113546-8a1f-48b5-a951-ddd8f3dc0512 · outbound

This paper cites Open-set recognition: A good closed-set classifier is all you need.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Open-set recognition: A good closed-set classifier is all you need

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation d0d232ef-26fa-487b-b077-228ae63d68c8 · outbound

This paper cites Pixood: Pixel- level out-of-distribution detection.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Pixood: Pixel- level out-of-distribution detection

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation bf2e23ee-447f-4aee-bd63-388df882976d · outbound

This paper cites Visual transform- ers: Token-based image representation and processing for computer vision, 2020.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Visual transform- ers: Token-based image representation and processing for computer vision, 2020

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 489a3de1-16dd-4d90-9fe5-4e209def2193 · outbound

This paper cites Autonomous driving system: A comprehensive survey.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Autonomous driving system: A comprehensive survey

Reference 34

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raw_fallback, observed 2026-08-10T23:50:34.844514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-10T23:50:34.605775Z digest=sha256:0fb9092879219b9429700a91fbf46a035a74284746018b4af7013ed531d33880

Observation 4580a948-5eb3-48c3-b7f0-62c397778c26 · outbound

This paper cites an unresolved cited work.

Zero-shot Hazard Identification in Autonomous Driving: A Case Study on the COOOL Benchmark Unresolved cited work

Reference 2024

Resolution
parse uncertain
raw_fallback, observed 2026-08-10T23:50:35.329037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-10T23:50:34.457439Z digest=sha256:681a7c15f852ee7ac925360d14da6b3fa6e81053624c3317b422781260acf7c3

Pith citing papers

No inbound Pith citation observations are available.