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Paper Citation Record · LEDGER

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving

As of 13 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2507.12762.

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

pith.paper-citation-record.v1
2507.12762 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:45:06.262282Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

60 of 60 outbound references displayed

  • verified exact0
  • verified fuzzy42
  • unresolved17
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0dc62768-4ef5-4800-b94e-af6e5260c3ce · outbound

This paper cites Autonomous vehicles: challenges, opportunities, and future implications for transportation policies.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:01.494439Z digest=sha256:f8390392c0a86fdf16996073bb565695fc725fd0926653a71c7b37ebb990ae03

Observation 3d1eeb15-90f6-407c-85fb-debee6240846 · outbound

This paper cites Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability? Transportation Research Part A: Policy and Practice, 94:182–193, 2016.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability? Transportation Research Part A: Policy and Practice, 94:182–193, 2016

Reference 2

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raw_fallback, observed 2026-08-06T16:45:07.973699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:01.609302Z digest=sha256:78b5a3cfbac4415ca7627bb66c3b4123a32b08cb02115859f710a1e9f79c95e7

Observation 896af7dd-ae95-4cb5-bf35-a2e9012b4166 · outbound

This paper cites Vision- based traffic accident detection and anticipation: A survey.IEEE Transactions on Circuits and Systems for Video Technology, 2023.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Vision- based traffic accident detection and anticipation: A survey.IEEE Transactions on Circuits and Systems for Video Technology, 2023

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:01.737266Z digest=sha256:3ebdf0406784007b540a1665c0314aefb727169f3fd33785d7de5f34e9181403

Observation 126a94e9-064d-4c78-9766-29c080c8931e · outbound

This paper cites Dynamicattentionaugmentedgraphnetworkforvideoaccident anticipation.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Dynamicattentionaugmentedgraphnetworkforvideoaccident anticipation

Reference 4

Resolution
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raw_fallback, observed 2026-08-06T16:45:07.924057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:01.869626Z digest=sha256:78c40c29e72f3b62cc43790cb2ffe06c2c11e23f00017927bfe478f306ccc381

Observation 8efadc60-2229-4535-86e9-abaaf9b41270 · outbound

This paper cites Antic- ipating accidents in dashcam videos.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Antic- ipating accidents in dashcam videos

Reference 5

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raw_fallback, observed 2026-08-06T16:45:07.900035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:01.995665Z digest=sha256:b49c498a90ebec4658f5fe2d95c4f13dabb8e061af81e9d64ea55de66694fc40

Observation 6bd849fe-3af0-4e16-9ac6-92b4e36df42f · outbound

This paper cites A dynamic spatial-temporal attention network for early anticipation of traffic accidents.IEEE Transactions on Intelligent Transportation Systems, 23(7):9590–9600, 2022.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving A dynamic spatial-temporal attention network for early anticipation of traffic accidents.IEEE Transactions on Intelligent Transportation Systems, 23(7):9590–9600, 2022

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:02.129743Z digest=sha256:e757226560b3a19635fde69c08e88a979a55a2f3eb60cd6fa1ba1b822ddf4895

Observation d5103560-4c84-4582-bfea-716eb560064f · outbound

This paper cites Spatiotemporal scene-graph embedding for autonomous vehicle collision prediction.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Spatiotemporal scene-graph embedding for autonomous vehicle collision prediction

Reference 7

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raw_fallback, observed 2026-08-06T16:45:07.849960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:02.277163Z digest=sha256:2c561352ae644d2c550e0c3a9e664344275170543b4350831ca56437372679b7

Observation 76ffa486-5768-4706-a7e5-06eda4ce4346 · outbound

This paper cites Global feature aggregation for accident anticipation.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Global feature aggregation for accident anticipation

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:02.438231Z digest=sha256:0159da6733e99056dd9f2ccb4e92f8ac129d70ff57b17ed91c1c15fa3968212d

Observation efd22c88-d6e3-4339-8597-68ccf2056034 · outbound

This paper cites Scene-graph augmented data-driven risk assessment of autonomous vehicle decisions.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Scene-graph augmented data-driven risk assessment of autonomous vehicle decisions

Reference 9

Resolution
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raw_fallback, observed 2026-08-06T16:45:07.792056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:02.523843Z digest=sha256:3986d2d44ba42701b220ad9afe5d6fedcfcac6e863dea46bed016615395e0811

Observation 269ba8a9-5e97-4984-9856-b8684123daf8 · outbound

This paper cites That-net: Two-layer hidden state aggregation based two-stream net- work for traffic accident prediction.Information Sciences, 634:744– 760, 2023.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving That-net: Two-layer hidden state aggregation based two-stream net- work for traffic accident prediction.Information Sciences, 634:744– 760, 2023

Reference 10

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raw_fallback, observed 2026-08-06T16:45:07.754501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:02.635187Z digest=sha256:53656d6e89a8edd2d3f9a2773d2091f75ec269a73786721e400620e35ddd4de3

Observation 5464e5d1-cc6e-44e7-8d9d-78cbcf091689 · outbound

This paper cites When, where, and what? a benchmark for accident anticipation and localization with large language models.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving When, where, and what? a benchmark for accident anticipation and localization with large language models

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:02.716374Z digest=sha256:075f383873cdded1d6134c677ff6da9b49ff1729dea854adbdc8b013604627f2

Observation e0f6e932-4c18-40b8-8cee-f146f503e7ce · outbound

This paper cites Review of graph-based hazardous event detection methods for autonomous driving systems.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Review of graph-based hazardous event detection methods for autonomous driving systems

Reference 12

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:02.838977Z digest=sha256:6257e1f639b75f1cfbfc426f879f0cb6873e8f38462654fbbdb70c795bf293a4

Observation c17d8cbc-8fba-4035-9a46-b4766b24c6e0 · outbound

This paper cites Graph (graph): A nested graph-based framework for early accident antic- ipation.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Graph (graph): A nested graph-based framework for early accident antic- ipation

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:07.671499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:02.992566Z digest=sha256:6b8150413734761777665b162ea4ee2200ead3e493de1a1b6b18c6818f3b5688

Observation b5d9f9f7-1de7-4eec-8b8f-fe6b0ba4359a · outbound

This paper cites Latte: A real-time lightweight attention-based traffic accident anticipation engine.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Latte: A real-time lightweight attention-based traffic accident anticipation engine

Reference 14

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raw_fallback, observed 2026-08-06T16:45:07.646973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:03.156894Z digest=sha256:d3c59c4e26a89a1e2140fc82948a326742f21c8a727048e3b35f6c2e6851153a

Observation aa7fcaf6-85cb-47a5-859f-10f785c893f1 · outbound

This paper cites Crash: Crash recognition and anticipation system harnessing with context-aware and temporal focus attentions.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Crash: Crash recognition and anticipation system harnessing with context-aware and temporal focus attentions

Reference 15

Resolution
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raw_fallback, observed 2026-08-06T16:45:07.620676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:03.308474Z digest=sha256:ef4b13d26bf8bda99b18304d93891aad16d37c3321769ab3ba9b9afc3069003e

Observation 3555c7c5-fc81-4d7a-b819-eadc97cc8dad · outbound

This paper cites Real-time acci- dent anticipation for autonomous driving through monocular depth- enhanced3dmodeling.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Real-time acci- dent anticipation for autonomous driving through monocular depth- enhanced3dmodeling

Reference 16

Resolution
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raw_fallback, observed 2026-08-06T16:45:07.598137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:03.460963Z digest=sha256:caf523829c61f00511304541027c4a946eba7227d138fad671410ff7e69001ec

Observation b5addd15-aab3-4bf5-befa-7cc2043c0e93 · outbound

This paper cites DC-Gaussian: Improving 3D Gaussian Splatting for Reflective Dash Cam Videos.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving DC-Gaussian: Improving 3D Gaussian Splatting for Reflective Dash Cam Videos

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:03.608314Z digest=sha256:d1b5b6d1324f51b8511d818b7643b8ad9ee67c2134730dee15555fa6a657f524

Observation 18004550-c867-424e-bcb4-71fafbb5d985 · outbound

This paper cites Reflection removal under fast forward camera motion.IEEE Transactions on Image Processing, 26(12):6061–6073, 2017.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Reflection removal under fast forward camera motion.IEEE Transactions on Image Processing, 26(12):6061–6073, 2017

Reference 18

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:03.682650Z digest=sha256:8b7d72a4e6009a6dea9e19a129cbcabf10ed2ef28b2ca8068ba24b668b8ee1f8

Observation 3c57e919-6f75-467a-b120-8ca8e891d11a · outbound

This paper cites Real-time automatic traffic accident recognition using hfg.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Real-time automatic traffic accident recognition using hfg

Reference 19

Resolution
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raw_fallback, observed 2026-08-06T16:45:07.511850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:03.718762Z digest=sha256:c9b5f2fa49923a45ae3362746ce65ee87692d05fed76fb4db9f1d3f2beb3eb9f

Observation 577bdb2a-8951-48e8-8f89-70ebc0b20490 · outbound

This paper cites Unsupervisedtrafficaccidentdetectioninfirst-personvideos.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Unsupervisedtrafficaccidentdetectioninfirst-personvideos

Reference 20

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raw_fallback, observed 2026-08-06T16:45:07.478134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:03.824035Z digest=sha256:f3f725a162aca386748a1a6cd2f7523b5b0e35e3f367c1d045489b44e8bd4dde

Observation a25b94ba-87cc-4938-a6f0-cd652deae5f7 · outbound

This paper cites IEEE Transactions on Intelligent Vehicles, 9(1):2249–2261, 2023.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving IEEE Transactions on Intelligent Vehicles, 9(1):2249–2261, 2023

Reference 21

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raw_fallback, observed 2026-08-06T16:45:07.276285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:03.987777Z digest=sha256:15e8e1fa00b3e6207a32af92f6ce62246a536f6c06c826b2f9df82f0c9455ca7

Observation c6984d43-5320-4e9a-b737-af75c23bdd9e · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 22

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verified fuzzy
raw_fallback, observed 2026-08-06T16:45:07.247518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:04.130405Z digest=sha256:0e6ee3679f123289b3a183466f4b75d58e4ff36eba06e04f8bbdc95029b283fa

Observation f68bd6a6-c0b7-4645-a2da-bc5ffe0fff4e · outbound

This paper cites Vision- language models for vision tasks: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Vision- language models for vision tasks: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Reference 23

Resolution
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raw_fallback, observed 2026-08-06T16:45:07.219040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:04.357709Z digest=sha256:78b5265781cc2327e5be743dd840a16d7a7e9103ea6b25e6975f8698d9c78377

Observation 1cd6484d-d2ef-48f8-a2ae-3c6fa853b44c · outbound

This paper cites Learning transferable visual mod- elsfromnaturallanguagesupervision.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Learning transferable visual mod- elsfromnaturallanguagesupervision

Reference 24

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:04.523816Z digest=sha256:d6e21c766697897608b88d26e2b364c9c2dcb1ff35854e7cfe4bf2e42476fe51

Observation c06af918-e24e-4f23-abe6-0c484e334f18 · outbound

This paper cites Sun database: Large-scale scene recognition from abbeytozoo.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Sun database: Large-scale scene recognition from abbeytozoo

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:07.171057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:04.712458Z digest=sha256:38a8338e878065339729d10f8024f97efda72b5d337ea9b59ca3ad909e888bd2

Observation bc243f94-0646-4352-a831-4e1dd006cccc · outbound

This paper cites Enhancing vision-language models with scene graphs for traffic accident understanding.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Enhancing vision-language models with scene graphs for traffic accident understanding

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:07.144391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:04.890842Z digest=sha256:e37be18b6088c8b34443316d124baa7744ae7e85ad231997c869db68cfdc7b0b

Observation d44f8ec8-5efb-433c-bc26-579991118fc9 · outbound

This paper cites Cross-domain traffic scene understanding by integrating deep learn- ing and topic model.Computational intelligence and neuroscience, 2022(1):8884669, 2022.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Cross-domain traffic scene understanding by integrating deep learn- ing and topic model.Computational intelligence and neuroscience, 2022(1):8884669, 2022

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:07.122935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:05.063725Z digest=sha256:2b93d7ea6b0ba827c4d1723c6a0e69be460efda47bbb6635299d797ab7b86bbd

Observation 47053044-4ad4-4841-adfb-2c3c0814e4c1 · outbound

This paper cites World models for autonomous driving: An initial survey.IEEE Transactions on Intelligent Vehicles, 2024.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving World models for autonomous driving: An initial survey.IEEE Transactions on Intelligent Vehicles, 2024

Reference 28

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raw_fallback, observed 2026-08-06T16:45:07.105290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:05.229944Z digest=sha256:6d60ff4ae887d1570e53916c2f2591aebfb2b02ddca2bbfb86bb0840d6194633

Observation 34f9a795-c51a-45a9-8020-ace42ac7d2d4 · outbound

This paper cites GAIA-1: A Generative World Model for Autonomous Driving.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving GAIA-1: A Generative World Model for Autonomous Driving

Reference 29

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unresolved
no resolver link, observed 2026-08-06T16:45:05.420535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:05.420535Z digest=sha256:06ddd609f28a5051eba9d110fc7590a467079a6b619ee0195a94e0071c50e751

Observation 78502dd1-739f-46c8-ae10-8f26628bc66d · outbound

This paper cites Driving into the future: Multiview visual fore- casting and planning with world model for autonomous driving.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Driving into the future: Multiview visual fore- casting and planning with world model for autonomous driving

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:07.083181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:05.580335Z digest=sha256:671220cd6ae97268ee21531912b423cce963cf8f04d46337ade32ccd38cdc645

Observation cb935754-98bb-4210-af1e-dce71b325508 · outbound

This paper cites Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability

Reference 31

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no resolver link, observed 2026-08-06T16:45:05.769443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:05.769443Z digest=sha256:8a19c074a0af1ba1ba3cfde311f1e5b4950480776db1eab1179cf5cce103b15b

Observation 83fb52bd-b8c9-4764-adad-922f06a9abcb · outbound

This paper cites Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

Reference 32

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no resolver link, observed 2026-08-06T16:45:05.944449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:05.944449Z digest=sha256:5dd50d7c7f2b5b8aae6b8fdcd36607fcec699d2d303f14ca5157f2ecee6d2e24

Observation 5dce0c73-439b-4a66-9d31-69e362b70445 · outbound

This paper cites DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving

Reference 33

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source=pdf_text observed=2026-08-06T16:45:06.101252Z digest=sha256:59cbc86a0eabe057fa847eb5584cc292d08c39b8261bcc063fd6a5a0ad8c8a2a

Observation f254a01f-a9d8-4430-bd22-f34e135e9dd3 · outbound

This paper cites Recurrentworldmodelsfacilitate policyevolution.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Recurrentworldmodelsfacilitate policyevolution

Reference 34

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verified fuzzy
raw_fallback, observed 2026-08-06T16:45:07.061495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:06.118211Z digest=sha256:480226957e5fd6c4289aa3309814b183edb815971d21f763a589e333a13c0324

Observation 0a439c4f-0eb5-40cd-b1a9-591d9997b720 · outbound

This paper cites Mastering Atari with Discrete World Models.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Mastering Atari with Discrete World Models

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:06.129037Z digest=sha256:0adcaf871b3450e43b95d5cadd7b672b25980eaddc9c8d02315efb2c8dbe3fe5

Observation 916b8675-d1e5-4ab7-89e7-9915a5bfeaec · outbound

This paper cites Video-LLaVA: Learning United Visual Representation by Alignment Before Projection.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

Reference 36

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T16:45:06.134462Z digest=sha256:b8317d5206dfd65d743681079235c59c7ba18dcaf291375996e0de17ec7dd794

Observation a4592372-be5d-4b25-96b5-1382302a2ab4 · outbound

This paper cites Openstreetmap: User- generatedstreetmaps.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Openstreetmap: User- generatedstreetmaps

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:07.044507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:06.140035Z digest=sha256:ca4c6eb3b44ebad8eb90ce02bc3405867ccffc221f4f9546e69cc632d3f65fe0

Observation 3ccbdf2d-0154-4462-bb2d-7dc09d945992 · outbound

This paper cites Recent development and applications of sumo-simulation of urban mobility.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Recent development and applications of sumo-simulation of urban mobility

Reference 38

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verified fuzzy
raw_fallback, observed 2026-08-06T16:45:07.023593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:06.144904Z digest=sha256:73a842c650adc43a1c751b41798bf2d29df5604ab7436b09a06a590c82a6e3f4

Observation bb60698c-619d-4876-9a02-d982ed4fa9df · outbound

This paper cites Planning-oriented autonomous driving.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Planning-oriented autonomous driving

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:07.001727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:06.151611Z digest=sha256:4e16a56f29c01efe3f5d3fadc6ea94ede2c49b3a3f5057a41284d482e81ced95

Observation 9fe8f151-b643-45cc-8878-190e5f2e7440 · outbound

This paper cites nuscenes: A multimodal dataset for autonomousdriving.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving nuscenes: A multimodal dataset for autonomousdriving

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:06.975409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:06.157540Z digest=sha256:2e122d82b0c0d14a5c05c867e0e8933b4565ec71913deb7dd877c717de00e93d

Observation f955ef42-576d-48e6-a867-2d9c27fc32d2 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving High-resolution image synthesis with latent diffusion models

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:06.943371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:06.162433Z digest=sha256:25f74fbb37bcfcfa93a0bdfcd13a7e6574d0d2727de367dcd4bc732add92e5ce

Observation 5a0906ad-ae6e-45c4-98de-e213bc1142c3 · outbound

This paper cites WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens

Reference 42

Resolution
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no resolver link, observed 2026-08-06T16:45:06.168043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:06.168043Z digest=sha256:f6d7b5f892234e70f18f833ce2d583f6df977b9abfd6808881e461515fabc9e6

Observation 9c295ebb-757a-4210-8e5c-4102c0ed4139 · outbound

This paper cites Fvd: A new metric for video generation.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Fvd: A new metric for video generation

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:06.923016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:06.173825Z digest=sha256:6182d492ad4bacf0e720faf4b6e9a315d2ca36e30e35bd181bc4b7c1f61301bc

Observation 8cdc0715-ff9d-4dc5-9e1b-732ec334c070 · outbound

This paper cites Frechetinceptiondistance(fid) for evaluating gans.China University of Mining Technology Beijing Graduate School, 3(11), 2021.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Frechetinceptiondistance(fid) for evaluating gans.China University of Mining Technology Beijing Graduate School, 3(11), 2021

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:06.893289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:06.179014Z digest=sha256:4f555652a4a6f650c989e63737944fd7a378a7ca912f1ec28af23b0337006522

Observation 29769738-58bc-49ef-8cac-17a2ec0cc649 · outbound

This paper cites NMS Strikes Back.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving NMS Strikes Back

Reference 45

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:06.183922Z digest=sha256:0b4dfdf7853c0b739172b2a7bb922e3951b93640808bbf564432f2675a549cab

Observation 808eef8b-efcb-47fb-b066-acab1729db91 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 46

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source=pdf_text observed=2026-08-06T16:45:06.188820Z digest=sha256:1d3aacc1818190d51a44098e6465ba8d2cdbce0037a0097ea224978c26881dd2

Observation 5e1313e8-5a9e-44bb-822c-8515d501145d · outbound

This paper cites ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth

Reference 47

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no resolver link, observed 2026-08-06T16:45:06.194055Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T16:45:06.194055Z digest=sha256:2b341c464e4504eeb82ba28c1e9ada0a9c4fb9357b99d52906a1c9db48f7bbc5

Observation 3bbaf938-8b7a-4b30-9754-0a23b55bd41e · outbound

This paper cites InProceedings of the IEEE conference on computer vision and pattern recognition, pages 3061–3070, 2015.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving InProceedings of the IEEE conference on computer vision and pattern recognition, pages 3061–3070, 2015

Reference 48

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verified fuzzy
raw_fallback, observed 2026-08-06T16:45:06.872247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:06.199471Z digest=sha256:576b929990cdc76f36aeb8a9ec66149d053fdb6b515932b4b01871d2aaed8b90

Observation e8b99a8d-edd5-4674-aab3-29f51b4b7385 · outbound

This paper cites Long short-term memory.Neural Computation MIT- Press, 1997.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Long short-term memory.Neural Computation MIT- Press, 1997

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:06.849649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:06.205519Z digest=sha256:46caa12965c182dc3bedd53f41717b8c3918a2da13cef5bb1e67f27bcf68d274

Observation e0c72947-23c9-40e8-b39f-9f0fd12be8d2 · outbound

This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 50

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:06.210131Z digest=sha256:9cd02039851c77a9ff17187e33d35fb3c7d3ad5803a4e64ae32da186de44b73d

Observation 16b08676-5239-48cf-aad2-3eaa7fdbda5f · outbound

This paper cites Temporal convolutional networks for action segmen- tation and detection.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Temporal convolutional networks for action segmen- tation and detection

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:06.826306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:06.214884Z digest=sha256:33c1b374e40aa7c80ec777a69d46bf6abc093357d909f2ccb70a8561b20c78ac

Observation 8355cdaa-e90c-4651-b367-ae035c133ffe · outbound

This paper cites Multi-Scale Context Aggregation by Dilated Convolutions.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Multi-Scale Context Aggregation by Dilated Convolutions

Reference 52

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no resolver link, observed 2026-08-06T16:45:06.219714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:06.219714Z digest=sha256:23e629b75a22c6b801dd179e33a26f5883b52b04f2c2b93a94442ef3fbdcdb33

Observation a08b9b57-0e0d-4940-86b5-e3ad952d79df · outbound

This paper cites IEEEtransactionsonpatternanalysisandmachine intelligence, 45(1):444–459, 2022.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving IEEEtransactionsonpatternanalysisandmachine intelligence, 45(1):444–459, 2022

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:06.801757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:06.225821Z digest=sha256:d3ef673cdefb7b6d7306b850826aac8cb10eae5783f4879c505b678a06e397e0

Observation 38917848-a6b1-48e6-9f02-70f082ce9ef5 · outbound

This paper cites Bdd100k: A diverse driving dataset for heterogeneous multitask learning.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Bdd100k: A diverse driving dataset for heterogeneous multitask learning

Reference 54

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no resolver link, observed 2026-08-06T16:45:06.230694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:06.230694Z digest=sha256:d46a0d38aefb83a30e0ecd07890a2510a09729289abbce8bd33f115c4576de47

Observation 9b6ede26-8a2f-4f9a-9c97-d539306b3cc1 · outbound

This paper cites Uncertainty-basedtrafficaccident anticipation with spatio-temporal relational learning.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving Uncertainty-basedtrafficaccident anticipation with spatio-temporal relational learning

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:06.759278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:06.236771Z digest=sha256:1df1caf8b79e0a91b825ffbd627d4d32c9c330fef9c4d70aa9337a2b103b75f2

Observation 7ed96e37-7c0f-43c2-ae9e-6ad0442be7f8 · outbound

This paper cites A review onthelongshort-termmemorymodel.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving A review onthelongshort-termmemorymodel

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:06.732504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:06.241963Z digest=sha256:530fa0c7ed1458647ea3a7417da0e2bd14a23f7abf44c30b2b4cd216ad8f9817

Observation c65184e7-79a3-4378-8369-f4f4c1fdbf4e · outbound

This paper cites At- tention is all you need.Advances in neural information processing systems, 30, 2017.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving At- tention is all you need.Advances in neural information processing systems, 30, 2017

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-06T16:45:06.247296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:06.247296Z digest=sha256:0cd52fdc8b917009cf93ed1696bbd184b1814e57782e0c9e674afd498e1d339e

Observation 1da858b1-965f-444c-b518-8fdc2e66a16e · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 58

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no resolver link, observed 2026-08-06T16:45:06.252312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:45:06.252312Z digest=sha256:e52d28f702062c496a6e581cd8e88b9038157b36d0deaf26eda6c640e54b0718

Observation 368ba664-8337-48f4-8dc6-851fbab27e42 · outbound

This paper cites In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3521–3529, 2018.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3521–3529, 2018

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:45:06.688782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:06.257085Z digest=sha256:ae48d7d19f8e88ce0727ba0809ff4599fc8136e81b185a6d009492e27db4ea0e

Observation 3464c829-afbd-43d9-ae8e-aa28437565a5 · outbound

This paper cites A multi- modal architecture with spatio-temporal-text adaptation for video- basedtrafficaccidentanticipation.

World Model-Based End-to-End Scene Generation for Accident Anticipation in Autonomous Driving A multi- modal architecture with spatio-temporal-text adaptation for video- basedtrafficaccidentanticipation

Reference 60

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malformed identifier
raw_fallback, observed 2026-08-06T16:45:06.662684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T16:45:06.262282Z digest=sha256:fc7d8c80be35d29620321a4ea0d0914e2d6f94d0f4303a57a7a4f0a10eb47b8a

Pith citing papers

No inbound Pith citation observations are available.