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Paper Citation Record · LEDGER

DriveLM: Driving with Graph Visual Question Answering

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 65 inbound Pith citation observations for arXiv:2312.14150.

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

pith.paper-citation-record.v1
2312.14150 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 65 of 65 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:24:41.518687Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

4
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 09a61a38-c4c6-449b-bfb4-bf7e01ac4fca · inbound

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

DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models DriveLM: Driving with Graph Visual Question Answering

Reference 60

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arxiv_id, observed 2026-05-12T19:22:35.510642Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-12T19:22:35.305220Z digest=sha256:30a4ead163545a80f9e6e983e7b3c7aca58f64525e6da874879cdaf99f2597b2

Observation e58f663b-80c5-413e-a746-562d82612804 · inbound

MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans? cites this paper.

MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans? DriveLM: Driving with Graph Visual Question Answering

Reference 55

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arxiv_id, observed 2026-05-16T07:59:32.692729Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-16T07:59:32.638758Z digest=sha256:192ea52274b1855313b9002f490b58fc28ecd0efa2f1a49b334d5b88093617e7

Observation 7beae691-896c-4d69-bee3-970cc8d8a687 · inbound

Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving cites this paper.

Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 31

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arxiv_id, observed 2026-05-15T15:24:23.869042Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-15T15:24:23.756052Z digest=sha256:234ebfcff1527ffb9a90758b3620f4711efd12e1f9e5c7e5cb8ab9836dbc8e81

Observation d4abe29f-2083-46ea-b1d6-9ed80ca5e463 · inbound

Explanation for Trajectory Planning using Multi-modal Large Language Model for Autonomous Driving cites this paper.

Explanation for Trajectory Planning using Multi-modal Large Language Model for Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 16

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source=pdf_text observed=2026-08-12T20:09:36.288980Z digest=sha256:fe1efb0a8616737bb39da9dcd8532e2dea3477809eb5c133a87d25100be0c4a6

Observation 84653957-d64b-4755-a4ba-b2c4cc630430 · inbound

LaVida Drive: Vision-Text Interaction VLM for Autonomous Driving with Token Selection, Recovery and Enhancement cites this paper.

LaVida Drive: Vision-Text Interaction VLM for Autonomous Driving with Token Selection, Recovery and Enhancement DriveLM: Driving with Graph Visual Question Answering

Reference 19

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source=pdf_text observed=2026-08-12T17:04:12.484511Z digest=sha256:5b8598b1ac42f4564051c0dae57211ad7e5538e7e78221f27d7bb829a57496fd

Observation 3589922e-01b9-4ece-b053-adb0b559a5b5 · inbound

SURDS: Benchmarking Spatial Understanding and Reasoning in Driving Scenarios with Vision Language Models cites this paper.

SURDS: Benchmarking Spatial Understanding and Reasoning in Driving Scenarios with Vision Language Models DriveLM: Driving with Graph Visual Question Answering

Reference 69

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source=pdf_text observed=2026-08-12T16:53:30.539003Z digest=sha256:73df9349af04863017aad9024893e695d0fcbc78a05a63bee414d4f9d15ebdb5

Observation 9dfd3e7f-7df5-47cd-8adc-14bfcc6173ca · inbound

MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs cites this paper.

MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs DriveLM: Driving with Graph Visual Question Answering

Reference 170

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source=pdf_text observed=2026-08-12T14:31:37.398064Z digest=sha256:d45a8734939b347cd0612abaf85c03961c273db6653da6d7df9d8a6cc3849e0f

Observation f841bdf8-cc15-4c8d-ba61-439b0ac971db · inbound

Monocular Lane Detection Based on Deep Learning: A Survey cites this paper.

Monocular Lane Detection Based on Deep Learning: A Survey DriveLM: Driving with Graph Visual Question Answering

Reference 207

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source=pdf_text observed=2026-08-12T13:18:37.400093Z digest=sha256:89c39082f7854081070536bc5258125d1b97e37aeb9c52eba6fa4028012829d0

Observation 6a9efa19-40a6-4052-b479-e5c94bdae33d · inbound

Generating Out-Of-Distribution Scenarios Using Language Models cites this paper.

Generating Out-Of-Distribution Scenarios Using Language Models DriveLM: Driving with Graph Visual Question Answering

Reference 35

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source=pdf_text observed=2026-08-12T13:03:27.816886Z digest=sha256:976bda23ae60e6ab46daa4d3d565527cbe9b7a7b6706a24af048454c884f04be

Observation 2a887f01-4abe-46e7-9b1c-507738389453 · inbound

FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like Autonomous Driving with Adaptive Feedback cites this paper.

FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like Autonomous Driving with Adaptive Feedback DriveLM: Driving with Graph Visual Question Answering

Reference 47

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source=pdf_text observed=2026-08-12T11:41:00.957759Z digest=sha256:b3ad9ff7f0cbaa2665b37a29d6aa7590b25197881ec015aabe80ce87dc37bbd0

Observation b9a3cdfa-faab-44df-9ba4-e424eca91138 · inbound

Visual Adversarial Attack on Vision-Language Models for Autonomous Driving cites this paper.

Visual Adversarial Attack on Vision-Language Models for Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 46

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arxiv_id, observed 2026-05-23T16:35:42.192052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-23T16:35:24.063578Z digest=sha256:50a61f17fe96ffb956f9d33de16707a72a958bfc066c3bdb9bb8419d5ae8d255

Observation 0ed550c3-3902-448a-897d-8843d4c6c0ec · inbound

World knowledge-enhanced Reasoning Using Instruction-guided Interactor in Autonomous Driving cites this paper.

World knowledge-enhanced Reasoning Using Instruction-guided Interactor in Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 40

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source=arxiv_source observed=2026-08-11T19:52:15.221944Z digest=sha256:5e870578846a78c3461370b7473cedb210fc0cd544e7435c73b24d86c5e5413e

Observation ca7d2bfe-77e8-4c88-812c-10abaf53ddef · inbound

Driving with InternVL: Oustanding Champion in the Track on Driving with Language of the Autonomous Grand Challenge at CVPR 2024 cites this paper.

Driving with InternVL: Oustanding Champion in the Track on Driving with Language of the Autonomous Grand Challenge at CVPR 2024 DriveLM: Driving with Graph Visual Question Answering

Reference 4

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source=arxiv_source observed=2026-08-11T18:59:14.173939Z digest=sha256:dc63cb69bde8537b24ecbf7386b78387e4c7b45f78f8ba74dfb1adaf291ad912

Observation ca4a2359-d481-466f-a887-409df766c2a0 · inbound

WiseAD: Knowledge Augmented End-to-End Autonomous Driving with Vision-Language Model cites this paper.

WiseAD: Knowledge Augmented End-to-End Autonomous Driving with Vision-Language Model DriveLM: Driving with Graph Visual Question Answering

Reference 37

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source=pdf_text observed=2026-08-11T16:35:17.458991Z digest=sha256:c9746299937329bdd56612676823989b2df4c1125cccb79d4891baef76908d85

Observation 902b6584-cc48-4afd-9446-753a12d36bdc · inbound

Multi-View Pedestrian Occupancy Prediction with a Novel Synthetic Dataset cites this paper.

Multi-View Pedestrian Occupancy Prediction with a Novel Synthetic Dataset DriveLM: Driving with Graph Visual Question Answering

Reference 38

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source=arxiv_source observed=2026-08-11T13:04:03.459090Z digest=sha256:6c1c8726aad1b95bd5104707c70d622a8426244b0a98a1cddb80d1eec9425a92

Observation 12e54531-f6b2-4026-be10-f912c74b0e55 · inbound

OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving cites this paper.

OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 46

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source=pdf_text observed=2026-08-11T11:35:37.864542Z digest=sha256:455326a0677fdfe41291397675d4057812b66643463857796f15827a6420b91d

Observation 212e760b-a0d1-4a32-82b0-25f62047dee6 · inbound

Visual Large Language Models for Generalized and Specialized Applications cites this paper.

Visual Large Language Models for Generalized and Specialized Applications DriveLM: Driving with Graph Visual Question Answering

Reference 160

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source=pdf_text observed=2026-08-10T22:08:09.512770Z digest=sha256:e2b24be14f27fbd7939e62371699048d7d2d641855e02c82be2274ad3a57c449

Observation 63eb6697-0f24-403d-be2f-b761a6fd909f · inbound

LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process Thinking cites this paper.

LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process Thinking DriveLM: Driving with Graph Visual Question Answering

Reference 37

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source=pdf_text observed=2026-08-10T20:34:11.794365Z digest=sha256:d34e2bedde48e206945d87c51b55f62266a27c79bb9ec92a1523e94ab3c962ea

Observation c2420a18-5892-49ce-9903-002b4a4e042d · inbound

Social-LLaVA: Enhancing Robot Navigation through Human-Language Reasoning in Social Spaces cites this paper.

Social-LLaVA: Enhancing Robot Navigation through Human-Language Reasoning in Social Spaces DriveLM: Driving with Graph Visual Question Answering

Reference 29

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source=pdf_text observed=2026-08-10T23:01:08.423570Z digest=sha256:366d5005eb29996ff28a99c8af17e0922397926d5a2963e616deb1a3ee11aeaf

Observation 4a281461-9294-4aed-b7fb-8b7420e487e5 · inbound

Embodied Scene Understanding for Vision Language Models via MetaVQA cites this paper.

Embodied Scene Understanding for Vision Language Models via MetaVQA DriveLM: Driving with Graph Visual Question Answering

Reference 4

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:15:04.535121Z digest=sha256:bce38d0884d7669b71df6fd87db66f70e5ccf04592e0c0a3c24bde5d61f1f8ab

Observation 061941eb-81b0-4d22-8d1f-5d2f9b351c21 · inbound

Distilling Multi-modal Large Language Models for Autonomous Driving cites this paper.

Distilling Multi-modal Large Language Models for Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 39

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source=pdf_text observed=2026-08-10T19:44:58.475391Z digest=sha256:19a1ee467700dc702c6f8b685df3086bd250a89b6b489109e77d782dffb03d48

Observation 3e083219-b9c8-45a8-a73b-6e47469bc84a · inbound

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving cites this paper.

Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 55

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source=pdf_text observed=2026-08-10T15:55:07.901795Z digest=sha256:d515f3bf4f9eba9709d91e70b9b8fcf28fb3f65222dd1737a224c1ce144848e0

Observation ff8cc251-5a8b-4488-a938-7df748442414 · inbound

Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models cites this paper.

Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models DriveLM: Driving with Graph Visual Question Answering

Reference 138

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source=pdf_text observed=2026-08-10T18:04:34.565468Z digest=sha256:f8be25ce84b034a7b49e70ebe8ad36ca2e6dbe358034e2f09d76537844bff37d

Observation 7d0502a5-f37c-493f-9e56-04f9354ee14c · inbound

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing cites this paper.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing DriveLM: Driving with Graph Visual Question Answering

Reference 2023

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source=pdf_text observed=2026-08-08T16:25:11.684696Z digest=sha256:6879a0a55ebb9ecc355567c1508f5d923e284007ea45917a7475aa02d4ad1b3e

Observation 44bbf082-60ed-4f00-a097-b681b8d9bc5b · inbound

Occ-LLM: Enhancing Autonomous Driving with Occupancy-Based Large Language Models cites this paper.

Occ-LLM: Enhancing Autonomous Driving with Occupancy-Based Large Language Models DriveLM: Driving with Graph Visual Question Answering

Reference 39

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source=pdf_text observed=2026-08-08T15:33:48.435428Z digest=sha256:387dc9f114fe910dbef6741319768c91e7e6e3c279e278f7dcdd03f11a35a33b

Observation 2f342397-317a-445a-b690-75ab64be3803 · inbound

Optimizing GPT for Video Understanding: Zero-Shot Performance and Prompt Engineering cites this paper.

Optimizing GPT for Video Understanding: Zero-Shot Performance and Prompt Engineering DriveLM: Driving with Graph Visual Question Answering

Reference 24

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source=arxiv_source observed=2026-08-07T21:03:13.329200Z digest=sha256:0c446060580c8836a76c9433c10b1ebfa3f7bb26862b95daba277938b14741d5

Observation ae8e2572-5414-49b8-b5bd-d3d6f04ca85d · inbound

AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning cites this paper.

AlphaDrive: Unleashing the Power of VLMs in Autonomous Driving via Reinforcement Learning and Reasoning DriveLM: Driving with Graph Visual Question Answering

Reference 34

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arxiv_id, observed 2026-05-16T20:06:27.230069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-16T20:06:27.136345Z digest=sha256:c258c5d555ce60d4e8e7b61c89b288626736f1fb44b23582b39febf0afee4942

Observation 4a37a0c2-898f-4e1c-ba81-41dcb5bd98d2 · inbound

Explainable Scene Understanding with Qualitative Representations and Graph Neural Networks cites this paper.

Explainable Scene Understanding with Qualitative Representations and Graph Neural Networks DriveLM: Driving with Graph Visual Question Answering

Reference 37

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source=pdf_text observed=2026-08-16T12:24:41.518687Z digest=sha256:495c40b306ee22737c88859984d0df2ace2069a1a2bf31e65ecaa4f5ca483fe4

Observation 6933f228-cc5b-4d3f-965a-90707ce14eaa · inbound

Natural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving cites this paper.

Natural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 38

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source=pdf_text observed=2026-08-15T22:46:49.611878Z digest=sha256:9d74889092d290e6fc2a6b72d7ce0c8c8fa29e60098170cad33706c62f06e907

Observation 6275f835-cafd-4c32-a2c1-3abf496b73c9 · inbound

Generative AI for Autonomous Driving: Frontiers and Opportunities cites this paper.

Generative AI for Autonomous Driving: Frontiers and Opportunities DriveLM: Driving with Graph Visual Question Answering

Reference 297

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source=pdf_text observed=2026-08-15T21:49:13.688413Z digest=sha256:a67dd3c568e84993c13e5a4533f51cee7cb2634ab078370f689d6f608523d64e

Observation 4033ec81-53ff-4a5b-adc5-fd16b3789bfa · inbound

Sage Deer: A Super-Aligned Driving Generalist Is Your Copilot cites this paper.

Sage Deer: A Super-Aligned Driving Generalist Is Your Copilot DriveLM: Driving with Graph Visual Question Answering

Reference 49

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source=arxiv_source observed=2026-08-15T21:17:05.407948Z digest=sha256:9742592d1d28e1310abb8be2efe71bb77a3f2f69f9c8a4bf2ac68d477ca3ffce

Observation 76b7a076-3c3e-44ff-9516-602c024403d8 · inbound

TS-VLM: Text-Guided SoftSort Pooling for Vision-Language Models in Multi-View Driving Reasoning cites this paper.

TS-VLM: Text-Guided SoftSort Pooling for Vision-Language Models in Multi-View Driving Reasoning DriveLM: Driving with Graph Visual Question Answering

Reference 8

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source=pdf_text observed=2026-08-15T20:32:52.148098Z digest=sha256:7faa1e84cb17dd9b586d07e4cd243660b07b0bef7d71847bde9c2c416bc51a75

Observation 2006486c-a1e1-4453-86b7-a508a2a52f9c · inbound

GeoVLM: Improving Automated Vehicle Geolocalisation Using Vision-Language Matching cites this paper.

GeoVLM: Improving Automated Vehicle Geolocalisation Using Vision-Language Matching DriveLM: Driving with Graph Visual Question Answering

Reference 41

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source=pdf_text observed=2026-08-15T20:14:56.447906Z digest=sha256:c338f83e51ba193106ceff6c7155a178a2a39fb30f50083fa24cd7d83b4e77d3

Observation 8953d76b-f3b1-4388-8460-12ce2453092c · inbound

ALN-P3: Unified Language Alignment for Perception, Prediction, and Planning in Autonomous Driving cites this paper.

ALN-P3: Unified Language Alignment for Perception, Prediction, and Planning in Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 25

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source=arxiv_source observed=2026-08-07T15:26:18.380536Z digest=sha256:15b64d3e0b2700ea6087a12ede26bc9388e89dbc1645d8eff0eed27ba4781283

Observation 876aa8d1-78ce-433f-9c25-7c8c9fffd000 · inbound

DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving cites this paper.

DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 14

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verified exact
arxiv_id, observed 2026-05-22T14:31:40.662059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-22T14:30:47.787654Z digest=sha256:0fd45d856c96007b617fd46f6ad6b0b6bc70fd703d02e1b955a6092fce69a0d7

Observation dc6d790b-42d9-46c8-9d8e-93723f85bdc5 · inbound

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving cites this paper.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 36

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:58:28.336819Z digest=sha256:cccacb47ebfac272a694a1c5c7882f2ededf8b0571913e433b6c227661d41bf8

Observation 978a1620-4859-41b4-81dc-f2ffacb256c4 · inbound

CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning cites this paper.

CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning DriveLM: Driving with Graph Visual Question Answering

Reference 12

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no resolver link, observed 2026-08-07T14:37:05.401540Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:37:05.401540Z digest=sha256:9b7c4c9c534be95be7579010b85f393d64505cba6be5aa211b59443807fe0a6c

Observation 693779d1-1163-444d-9fa1-52bc156838c0 · inbound

Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Models cites this paper.

Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Models DriveLM: Driving with Graph Visual Question Answering

Reference 53

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no resolver link, observed 2026-08-07T12:43:56.830698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:43:56.830698Z digest=sha256:686d12f4eb87b8a0462662ed18bd655754ce6a99520250a285e617f3242190b3

Observation ba3bd234-77d9-4c20-b061-2f2412f81c17 · inbound

Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving cites this paper.

Structured Labeling Enables Faster Vision-Language Models for End-to-End Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-22T00:20:50.519821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-22T00:16:35.823270Z digest=sha256:3000f45442710a8ababe99e91f1ab34583ee00c4bb20f3a1c3dbc093a29d00c1

Observation 6be7e58b-d9ca-4819-a9cf-fad27244db36 · inbound

Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving cites this paper.

Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 44

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no resolver link, observed 2026-08-07T04:46:48.531358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:46:48.531358Z digest=sha256:a46660f0377a18875a370e71e05ebbdd7d660a80156cd1249bdd88ca70682fdb

Observation 784a5c1f-93ff-4c00-ba03-7b28fe203bdd · inbound

RoCA: Robust Cross-Domain End-to-End Autonomous Driving cites this paper.

RoCA: Robust Cross-Domain End-to-End Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 32

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unresolved
no resolver link, observed 2026-08-07T04:40:59.567958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T04:40:59.567958Z digest=sha256:1ba2d021109e9efec106d751563917a16fd0489b9fb43920bec135fc9012ff21

Observation 272b89b2-ed3e-4fca-a7ed-ee1c47c5a304 · inbound

NetRoller: Interfacing General and Specialized Models for End-to-End Autonomous Driving cites this paper.

NetRoller: Interfacing General and Specialized Models for End-to-End Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-15T19:56:08.091183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T19:56:08.091183Z digest=sha256:ead7787d4105aad987c95b59704501bdc5d9c8ca50de69b6ab7b964ab30899c2

Observation 6c8c6933-5b42-4e55-9051-05c4ff1d36db · inbound

Demystifying the Visual Quality Paradox in Multimodal Large Language Models cites this paper.

Demystifying the Visual Quality Paradox in Multimodal Large Language Models DriveLM: Driving with Graph Visual Question Answering

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-06T23:56:38.206737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T23:56:38.206737Z digest=sha256:81571165aa06578df5fb1a082ad8e2ac2822c8c5cb5d5c84d7124c1f57c10819

Observation 7b0ccec8-4697-4c39-8247-7b775dd50560 · inbound

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects cites this paper.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects DriveLM: Driving with Graph Visual Question Answering

Reference 113

Resolution
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no resolver link, observed 2026-08-15T18:27:59.589752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:27:59.589752Z digest=sha256:cd03a62d3364eeb2b6003e1d1cddc1f388788d87421a4b9a98b42ba8d95c7601

Observation 0c24cdbb-c189-4fd6-91ed-9a675f5aaab5 · inbound

Box-QAymo: Box-Referring VQA Dataset for Autonomous Driving cites this paper.

Box-QAymo: Box-Referring VQA Dataset for Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 24

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unresolved
no resolver link, observed 2026-08-06T21:17:18.850748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:17:18.850748Z digest=sha256:3b8e95d07d408fbc9f092bb99eba7841b30f4ea74a9e09b2d500502574ba9b1b

Observation 5dc6ad1a-1bb3-4de8-b500-1bbac38a3ea4 · inbound

LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving cites this paper.

LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 11

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unresolved
no resolver link, observed 2026-08-06T19:24:28.857251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:24:28.857251Z digest=sha256:83344c668b46401e455154b9f1074e7ad2fc874195394fa48be13c4ad391bc05

Observation 21e183e7-d9bf-463b-b53d-feccc26bf41e · inbound

MoSE: Skill-by-Skill Mixture-of-Experts Learning for Embodied Autonomous Machines cites this paper.

MoSE: Skill-by-Skill Mixture-of-Experts Learning for Embodied Autonomous Machines DriveLM: Driving with Graph Visual Question Answering

Reference 34

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unresolved
no resolver link, observed 2026-08-06T18:37:45.568121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:37:45.568121Z digest=sha256:f927295b41db17899c99a69d551e40d3808ec2e41d3542c672b453156872d27f

Observation ae7b551c-13a4-44c9-ac95-77ba4b1a8538 · inbound

VRU-Accident: A Vision-Language Benchmark for Video Question Answering and Dense Captioning for Accident Scene Understanding cites this paper.

VRU-Accident: A Vision-Language Benchmark for Video Question Answering and Dense Captioning for Accident Scene Understanding DriveLM: Driving with Graph Visual Question Answering

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T17:52:02.743420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:52:02.743420Z digest=sha256:644a043288bfd2be9de38e7faaf39029d1b8f3d0ef519d891de687a8cb65eb6c

Observation 8f98ee6c-7d80-407f-a3e8-2db48c1b13e1 · inbound

B4DL: A Benchmark for 4D LiDAR LLM in Spatio-Temporal Understanding cites this paper.

B4DL: A Benchmark for 4D LiDAR LLM in Spatio-Temporal Understanding DriveLM: Driving with Graph Visual Question Answering

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-19T00:11:55.934515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-19T00:09:57.236162Z digest=sha256:ea8ea445d1c22a9e7e1f2dd02de3c90955e668a83ad422abedc693d38e753718

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

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

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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no resolver link, observed 2026-08-05T11:32:33.177237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:32:33.177237Z digest=sha256:e2b8e5be0ce8d30beb0073ab4d77ff02c201f4f46a394738add2162bd1e24ea2

Observation 7005ad3d-7bf9-463a-93ab-86378df1370d · inbound

Rashomon in the Streets: Explanation Ambiguity in Scene Understanding cites this paper.

Rashomon in the Streets: Explanation Ambiguity in Scene Understanding DriveLM: Driving with Graph Visual Question Answering

Reference 30

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no resolver link, observed 2026-08-05T11:09:48.116321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T11:09:48.116321Z digest=sha256:6a83eb20d33f8a32a5031de6f76ce0b82f6fd4fa4e8db3e09ac02d93e2c3a5b2

Observation 49283034-e0dd-4ff2-ac0f-707a1bba92b5 · inbound

MITS: A Large-Scale Multimodal Benchmark Dataset for Intelligent Traffic Surveillance cites this paper.

MITS: A Large-Scale Multimodal Benchmark Dataset for Intelligent Traffic Surveillance DriveLM: Driving with Graph Visual Question Answering

Reference 57

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unresolved
no resolver link, observed 2026-08-04T20:33:35.483420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T20:33:35.483420Z digest=sha256:265f1d21d47135e97ca18d688faec50c497451b67f988d5b0837345196ece702

Observation 99ae3a90-ed1d-4270-b1fa-490d23c3eeee · inbound

The System Description of CPS Team for Track on Driving with Language of CVPR 2024 Autonomous Grand Challenge cites this paper.

The System Description of CPS Team for Track on Driving with Language of CVPR 2024 Autonomous Grand Challenge DriveLM: Driving with Graph Visual Question Answering

Reference 11

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no resolver link, observed 2026-08-04T17:11:16.266948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T17:11:16.266948Z digest=sha256:ef6cf558c20695d1c07e64fc230f01bccd3647db02ddcc77c72064357d2e7f27

Observation 130f7f11-fe50-4878-a24d-315f3df4f5b1 · inbound

OmniDrive-R1: Reinforcement-driven Interleaved Multi-modal Chain-of-Thought for Trustworthy Vision-Language Autonomous Driving cites this paper.

OmniDrive-R1: Reinforcement-driven Interleaved Multi-modal Chain-of-Thought for Trustworthy Vision-Language Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-16T22:38:37.855821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-16T22:34:00.895252Z digest=sha256:75b29701b79706afd6c4f59d12677140d642f2887df6a1e450e0f5353199434f

Observation 3d8c5845-fb10-4cb0-ac89-1b1178910c48 · inbound

From Representational Complementarity to Dual Systems: Synergizing VLM and Vision-Only Backbones for End-to-End Driving cites this paper.

From Representational Complementarity to Dual Systems: Synergizing VLM and Vision-Only Backbones for End-to-End Driving DriveLM: Driving with Graph Visual Question Answering

Reference 2023

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unresolved
no resolver link, observed 2026-08-04T06:07:07.991904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T06:07:07.991904Z digest=sha256:10cd565345fbbdd7aca3d7ad21a6594f0139febecb5a501098315e4f98237cc7

Observation e8c61c05-ce62-431a-9d42-9fe6fdf14a58 · inbound

Beyond the Beep: Scalable Collision Anticipation and Real-Time Explainability with BADAS-2.0 cites this paper.

Beyond the Beep: Scalable Collision Anticipation and Real-Time Explainability with BADAS-2.0 DriveLM: Driving with Graph Visual Question Answering

Reference 15

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verified exact
arxiv_id, observed 2026-05-11T00:25:50.376664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-10T18:33:16.047297Z digest=sha256:911a1ab101e0deca62da1bb2c18d889144ad4223eed4c6039ce8252fde5a9508

Observation a73e39cb-9031-4e49-8181-e2df90c6408b · inbound

OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models cites this paper.

OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models DriveLM: Driving with Graph Visual Question Answering

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-05-11T11:56:25.086289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-10T04:27:24.653711Z digest=sha256:533c1ec1712ba44f34a3875f7de4dd841395e13152fd65dc5e9225cf7f0686db

Observation a7cfe32a-1c0b-4f7d-ac71-03f6c0cff581 · inbound

CCTVBench: Contrastive Consistency Traffic VideoQA Benchmark for Multimodal LLMs cites this paper.

CCTVBench: Contrastive Consistency Traffic VideoQA Benchmark for Multimodal LLMs DriveLM: Driving with Graph Visual Question Answering

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:31:05.488460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-10T01:31:40.267429Z digest=sha256:bc8338c51ba5e3671b1a6e3a5b4110bf7bbd18289d992bf28316184e77d46922

Observation a191e450-f9fa-48a3-9ef6-506ea61c086d · inbound

EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving cites this paper.

EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving DriveLM: Driving with Graph Visual Question Answering

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-10T00:19:47.225470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-10T00:09:18.068337Z digest=sha256:b69f8833588a05d03235543cd7fa6c7a495ff1b243784053af2598b2bbf2f92f

Observation 84c51a34-ee49-4b49-8d40-202c7726edc1 · inbound

TRIP-Evaluate: An Open Multimodal Benchmark for Evaluating Large Models in Transportation cites this paper.

TRIP-Evaluate: An Open Multimodal Benchmark for Evaluating Large Models in Transportation DriveLM: Driving with Graph Visual Question Answering

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-09T20:17:04.496759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-09T20:13:06.376037Z digest=sha256:9047596a13e384ae5a8424f9f9734b5d0fe2557a16dcb9d337836cd25a6b8b43

Observation 4bce54b4-283b-4444-9efa-97c5b8abed7b · inbound

EponaV2: Driving World Model with Comprehensive Future Reasoning cites this paper.

EponaV2: Driving World Model with Comprehensive Future Reasoning DriveLM: Driving with Graph Visual Question Answering

Reference 60

Resolution
verified exact
arxiv_id, observed 2026-05-15T05:15:02.561817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-15T05:14:28.714494Z digest=sha256:50e81eb82a2fcac79e03b4b50c8f7148fa54d2a6d5fa9fda0401093a90cad7c9

Observation 1c1c84cf-2d65-45d7-b5e0-ff903a848dbe · inbound

ChainFlow-VLA: Causal Flow Planning with Vision-Language Models cites this paper.

ChainFlow-VLA: Causal Flow Planning with Vision-Language Models DriveLM: Driving with Graph Visual Question Answering

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-25T04:40:23.776537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-25T04:37:56.487048Z digest=sha256:0012c807f270602c7fb6f70dd7a43dc706e4dec6754d3cf9ac68dd7962b45746

Observation 04a2be31-f9df-4bc2-8ed0-b4bd39c7f42e · inbound

LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding cites this paper.

LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding DriveLM: Driving with Graph Visual Question Answering

Reference 7

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T17:53:46.756827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-29T17:52:59.346637Z digest=sha256:9d8608c2bd08280ecde1bfa2894418607ba2f27d3a1900633e4dfdaf833c820f

Observation 495e8542-65ba-441e-a483-bfad53d224c9 · inbound

OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving cites this paper.

OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving DriveLM: Driving with Graph Visual Question Answering

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-07-04T06:29:38.033869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-26T14:24:07.496389Z digest=sha256:687e1e0245a63b6842af6ad48e9b5921d12e6a3950affb68abed601850431b25

Observation 6ad2b717-5d22-4ca6-bd10-fc8d39291973 · inbound

FOLIO: Focused Semantic Memory for Streaming Video Understanding cites this paper.

FOLIO: Focused Semantic Memory for Streaming Video Understanding DriveLM: Driving with Graph Visual Question Answering

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-02T05:40:46.637545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T05:40:46.637545Z digest=sha256:618171c62308ba9dde960a837b83539fe1c26f285705db3af6f9d2d8ba0c84e6