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Paper Citation Record · LEDGER

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model

As of 9 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2509.02659.

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

pith.paper-citation-record.v1
2509.02659 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:32:33.879692Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

26 of 26 outbound references displayed

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External citation measurements

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Outbound references

Observation 3a9f5354-ac8e-4e6d-8ef6-0d7ebe59e43a · outbound

This paper cites an unresolved cited work.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model Unresolved cited work

Reference 1

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T11:32:31.559993Z digest=sha256:77c64913cbe8a361e228ec8f4fb40ccfd0e3f77bbbdae222c8a37eb33ca0e23c

Observation 02388aba-8ea0-45d3-b5fa-5791db777058 · outbound

This paper cites NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

Reference 2

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source=pdf_text observed=2026-08-05T11:32:31.628690Z digest=sha256:eb83c596b074010d8aeaddf5a1ef5093ca16c65615136e8f6e812ac7c3c6e197

Observation 77c316b4-fe87-4df9-8bc9-63ab4c6561bc · outbound

This paper cites InternLM2 Technical Report.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model InternLM2 Technical Report

Reference 3

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source=pdf_text observed=2026-08-05T11:32:31.709766Z digest=sha256:24c4326655ce48dc37a6d4d5219d511d597bc314c5977b234840d76a1c0e16cb

Observation 477b43a1-033b-4aa7-81e0-1f3261d74f2a · outbound

This paper cites VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning

Reference 4

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source=pdf_text observed=2026-08-05T11:32:31.784450Z digest=sha256:034391594303ad25e363eae47d93e2924b6894882c1c12162062e476d341ab9a

Observation a5133449-9421-44f0-897a-2d55ab222cd7 · outbound

This paper cites Holistic Autonomous Driving Understanding by Bird's-Eye-View Injected Multi-Modal Large Models.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model Holistic Autonomous Driving Understanding by Bird's-Eye-View Injected Multi-Modal Large Models

Reference 5

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source=pdf_text observed=2026-08-05T11:32:31.855904Z digest=sha256:675cb27479f91b620d7d375d8e3d269f524c593a2741c33dcd11f71a0bcaf887

Observation 3d42378f-e409-4dac-87f8-d0cfbd615ab6 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 6

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source=pdf_text observed=2026-08-05T11:32:31.953341Z digest=sha256:8199171e27ad41e728cb48d3c33f191f4805d9a602cb5f61d8e4590fb70d723c

Observation 06ab3517-4173-48b2-b5be-f9290b318fe4 · outbound

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

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability

Reference 7

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source=pdf_text observed=2026-08-05T11:32:32.036400Z digest=sha256:06ca653e020eb1ea45e53afc0800311f27548d754456b29c248acf1ebc6b81ba

Observation 1101ea54-1eb7-4f1d-81bf-39fc1ceaec5a · outbound

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

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model GAIA-1: A Generative World Model for Autonomous Driving

Reference 8

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source=pdf_text observed=2026-08-05T11:32:32.129986Z digest=sha256:77d9130c80fa43a08a8236cbe1272288e44f2b6505b1fc5872c9c027d2d1ed31

Observation f41b8a38-698d-4cba-9848-336cf8ad9707 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model Lora: Low-rank adaptation of large language models

Reference 9

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source=pdf_text observed=2026-08-05T11:32:32.184781Z digest=sha256:5a7647a1f87890f0b93c43c2d62afda966418664c3ed99701efbeccb660cf3b6

Observation eb01318a-20cd-47b2-afde-0cbba74be661 · outbound

This paper cites St-p3: End-to-end vision-based au- tonomous driving via spatial-temporal feature learning.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model St-p3: End-to-end vision-based au- tonomous driving via spatial-temporal feature learning

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T11:32:32.248486Z digest=sha256:95e581c87727242d5a0116ea96282043dbff80bc2b68269b79d3159c089d79ea

Observation 55565ac0-998b-4514-9659-3c7a82ad8bb9 · outbound

This paper cites Planning-oriented autonomous driving.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model Planning-oriented autonomous driving

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T11:32:32.365866Z digest=sha256:9c694835b476330675b283260ca7ede9e753190754da377f9b170866919b2c44

Observation ff799187-915a-4601-bfb4-1f3da55b9c8a · outbound

This paper cites DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model

Reference 12

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source=pdf_text observed=2026-08-05T11:32:32.477461Z digest=sha256:f68732e101a63188fdb624eac01344462af68fd37b0c1f9236284f01b5954bc4

Observation dbbd1cc0-788a-47f1-b997-b00c4160468e · outbound

This paper cites WoVoGen: World Volume-aware Diffusion for Controllable Multi-camera Driving Scene Generation.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model WoVoGen: World Volume-aware Diffusion for Controllable Multi-camera Driving Scene Generation

Reference 13

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source=pdf_text observed=2026-08-05T11:32:32.658188Z digest=sha256:2388865baf043f601994f0f56f62a1cceb0492b759000d6af6c3522eeaac8660

Observation 2b6e33bd-1fe5-4d78-b65a-c65dd6c276dc · outbound

This paper cites GPT-Driver: Learning to Drive with GPT.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model GPT-Driver: Learning to Drive with GPT

Reference 14

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source=pdf_text observed=2026-08-05T11:32:32.772087Z digest=sha256:4e823b79539b1db02faf4d856cdaf5dc8c9fe5ffae4f519f08b45513834c0452

Observation a2d0feb9-2402-4a2e-8873-ef91e037f8c5 · outbound

This paper cites A Language Agent for Autonomous Driving.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model A Language Agent for Autonomous Driving

Reference 15

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source=pdf_text observed=2026-08-05T11:32:32.889451Z digest=sha256:7bdd8c6c37dd17f280edc7eb46fc5cec6a98cbccc77de9498add78abbfafbc2c

Observation 7c8d7078-2c7b-4cad-a178-ced6218b842c · outbound

This paper cites LMDrive: Closed-Loop End-to-End Driving with Large Language Models.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model LMDrive: Closed-Loop End-to-End Driving with Large Language Models

Reference 16

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source=pdf_text observed=2026-08-05T11:32:33.037882Z digest=sha256:eda5e52142b0d51791e7eca3c7a315fa4c5bc1cf9400effaba5f399d3cc361ce

Observation b2ae9594-cad5-49bd-9a16-a76fed16ae4f · outbound

This paper cites DriveLM: Driving with Graph Visual Question Answering.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model DriveLM: Driving with Graph Visual Question Answering

Reference 17

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source=pdf_text observed=2026-08-05T11:32:33.177237Z digest=sha256:f9e51fba98f1592a8528ad562c1fb77b9ff05c62b4c54bd43abb9e824633864a

Observation 2179ba5a-0dac-4d84-bbfa-adf14a5408c1 · outbound

This paper cites DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models

Reference 18

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source=pdf_text observed=2026-08-05T11:32:33.325982Z digest=sha256:21b77749d4a93886a161f5235da7e3aeb5db165269dd44304602d145709bfb36

Observation 7ae66473-ab6e-4ae1-9747-d44bb5680f2f · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model LLaMA: Open and Efficient Foundation Language Models

Reference 19

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source=pdf_text observed=2026-08-05T11:32:33.394298Z digest=sha256:3e33f711065e3229df778508bb8651bec83e32308cf75d6d79c5d03844c0729c

Observation ea714b6e-32d4-4fbe-ba32-9d22e10bc6f7 · outbound

This paper cites OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning

Reference 20

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source=pdf_text observed=2026-08-05T11:32:33.451445Z digest=sha256:956ada99438f5067a9a43a0cbf3cab308d9a35babdd383df52a99d7808835172

Observation 9662b523-70cd-4b18-b8de-b4088bb281fd · outbound

This paper cites Drivemlm: Aligning multi-modal large language models with behavioral planning states for au- tonomous driving.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model Drivemlm: Aligning multi-modal large language models with behavioral planning states for au- tonomous driving

Reference 21

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source=pdf_text observed=2026-08-05T11:32:33.511426Z digest=sha256:b392703e51075ea91788707661d1d48c5fd13d4e7a70fcaf22dac356c9e4151b

Observation 6479f533-0276-4b16-ae5e-c90d51c72f15 · outbound

This paper cites Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving

Reference 22

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source=pdf_text observed=2026-08-05T11:32:33.582916Z digest=sha256:55b486891830336c8bf904a65e1e1f133b81d6a4dfab2c89d0074dfb76bd39d9

Observation b37e2534-9a3c-4119-a971-7c5fd99e9bab · outbound

This paper cites DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model

Reference 23

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source=pdf_text observed=2026-08-05T11:32:33.666881Z digest=sha256:1e0f931b1e1b620ff88666dbf23da1a6b7b8bd1dc428d327253835c2fde21f6c

Observation 941b6ca7-1a71-4991-8db2-71c21777da0d · outbound

This paper cites GenAD: Generalized Predictive Model for Autonomous Driving.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model GenAD: Generalized Predictive Model for Autonomous Driving

Reference 24

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source=pdf_text observed=2026-08-05T11:32:33.747271Z digest=sha256:a686a2a95ed296b93bc01a3259ed57c594ef6e00ba5566fc04168b20c398d4c5

Observation 824d40cc-f742-4dd8-b044-8fde1d28e71a · outbound

This paper cites GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving

Reference 25

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source=pdf_text observed=2026-08-05T11:32:33.826657Z digest=sha256:71467d0696292715f0273142fef1d67a3e70f65b8023679596a92094a4ee69fd

Observation 6eadc52c-e9ad-4945-9c71-f6e2e3c7f6fd · outbound

This paper cites DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation.

2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation

Reference 26

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source=pdf_text observed=2026-08-05T11:32:33.879692Z digest=sha256:e1f3094f974c79debdef3772997aee460703420391716f85f5b377b35e586517

Pith citing papers

No inbound Pith citation observations are available.