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Paper Citation Record · LEDGER

Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2501.04003.

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

pith.paper-citation-record.v1
2501.04003 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:03:00.946533Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T06:15:00.866473Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 556d24ba-a350-495d-ac42-ddbf5074ee56 · inbound

Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects cites this paper.

Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 114

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no resolver link, observed 2026-08-07T14:03:00.946533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:03:00.946533Z digest=sha256:d81baab51bc867068a169237348671937cf9f48b2331a634a39e7c909c4e297b

Observation 39bd3ae6-0ea5-48c6-a8d7-f6e4be4c5309 · inbound

PixelThink: Towards Efficient Chain-of-Pixel Reasoning cites this paper.

PixelThink: Towards Efficient Chain-of-Pixel Reasoning Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 10

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no resolver link, observed 2026-08-07T12:45:41.564697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:45:41.564697Z digest=sha256:50b00a6917bdc6bfdeaed3f4fec5e526b9d462945dcfbf493e372347fc3fd552

Observation 059b7124-7226-493f-a978-a95d81149207 · 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 Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 74

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no resolver link, observed 2026-08-07T12:43:58.249021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:43:58.249021Z digest=sha256:53223dfe3c8c6fe61dab2f709dbe399093384ae2c4a01199d161067a8e2948cc

Observation 0cff78ce-2c1c-4f43-b8cd-cfcb91d8583e · 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 Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 45

Resolution
verified exact
arxiv_id, observed 2026-05-22T00:20:50.406050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-22T00:16:35.823270Z digest=sha256:47d5fde3e2b974369b37c723678645f2e8b09deec0d1e76b1a668b641e8fedf1

Observation d4d6aed3-8deb-41f1-8318-014e26189f73 · inbound

STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving cites this paper.

STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 65

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no resolver link, observed 2026-08-07T06:02:13.143198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:02:13.143198Z digest=sha256:e69a62cca81334ebd1e77bdecba686fc5398589f6e8c03e8b0778981f706f84c

Observation 3fa4a20c-f417-49e3-8063-05e0c7f4dffa · inbound

AD^2-Bench: A Hierarchical CoT Benchmark for MLLM in Autonomous Driving under Adverse Conditions cites this paper.

AD^2-Bench: A Hierarchical CoT Benchmark for MLLM in Autonomous Driving under Adverse Conditions Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 52

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no resolver link, observed 2026-08-07T04:49:29.348779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:49:29.348779Z digest=sha256:78f08617a28a7dbddd35d2b240d08db5e86aea51d18819cd2237627a0a39bd5d

Observation 0117b600-5726-4636-8dc4-d4769a5f9dc3 · inbound

A Survey on Vision-Language-Action Models for Autonomous Driving cites this paper.

A Survey on Vision-Language-Action Models for Autonomous Driving Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 136

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no resolver link, observed 2026-08-06T21:31:04.802163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:31:04.802163Z digest=sha256:87b3599823b3cfc965c154518ddeadce98dcbb05cd7ca4ba469d537c27436671

Observation 911cd285-ed93-46dd-a23a-b8496d411293 · inbound

Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations cites this paper.

Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 90

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no resolver link, observed 2026-08-06T19:32:35.442839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:32:35.442839Z digest=sha256:3dc30cc1fdd2ce4718ca7c546c4008dc651b03f6946f9e373a699e7e06f712a3

Observation 3d08c49b-b6a6-4b7a-b594-9b0289d2b4c6 · inbound

Monocular Semantic Scene Completion via Masked Recurrent Networks cites this paper.

Monocular Semantic Scene Completion via Masked Recurrent Networks Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 84

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no resolver link, observed 2026-08-06T14:48:19.348528Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:48:19.348528Z digest=sha256:3fc05d092b5a62601ebb8de9da882f516542cab3a22deb723a003016bcd64eda

Observation 39c26dbc-715f-4eae-9615-7e15fb0d062b · inbound

DriveQA: Passing the Driving Knowledge Test cites this paper.

DriveQA: Passing the Driving Knowledge Test Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 86

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no resolver link, observed 2026-08-05T13:58:11.634775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:58:11.634775Z digest=sha256:9514de8eb26fb525fc64063d76d7f3224db91ccdb23234aca93f9aa27362e442

Observation 642a2e69-dd69-4a69-b7aa-812789801d64 · inbound

Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail cites this paper.

Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 101

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arxiv_id, observed 2026-05-18T02:35:13.296616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T02:35:13.126171Z digest=sha256:ecc794e6b04118008d6a051d3afcb492cafbdc3b8353de8b5b1f7209e48b92f8

Observation 90211ad9-1368-4134-9d9b-0d41c0a9faf2 · inbound

Descriptor: Distance-Annotated Traffic Perception Question Answering (DTPQA) cites this paper.

Descriptor: Distance-Annotated Traffic Perception Question Answering (DTPQA) Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 2

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arxiv_id, observed 2026-05-17T21:45:17.947913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-17T21:43:30.762291Z digest=sha256:97230c1c8be808c5708f4126f6c1fd13db09077efaf7a6612decc2061ec46659

Observation 9d724f94-ef08-4c92-a021-4f8311afebed · inbound

RoadBench: Benchmarking MLLMs on Fine-Grained Spatial Understanding and Reasoning under Urban Road Scenarios cites this paper.

RoadBench: Benchmarking MLLMs on Fine-Grained Spatial Understanding and Reasoning under Urban Road Scenarios Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 39

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no resolver link, observed 2026-08-03T20:52:04.413608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T20:52:04.413608Z digest=sha256:88fb00516962557fbad2645bd6a5b6e7f6533b3594631d3a1f0c85b192e2bbf2

Observation 24576f47-ccf1-47d2-9b51-37242486d3ec · inbound

MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs cites this paper.

MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 84

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arxiv_id, observed 2026-05-15T20:16:34.533036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-15T20:12:46.385646Z digest=sha256:94daa16acdb2941e0e4f507018ae6626a0cdb1950530a3647c7a66cdb5c13a28

Observation add1c3a3-3b95-4e73-84ae-fae51fca165b · inbound

MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs cites this paper.

MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 84

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verified exact
arxiv_id, observed 2026-05-22T10:31:25.210000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-22T10:30:06.829915Z digest=sha256:386ab1eab9eecd47d2759ee74ebdd3d47422ec3b5acce15e325f1ec4582f3235

Observation 1cad9375-d121-48b9-8ed3-a5f7ef3dd87d · inbound

MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs cites this paper.

MapTab: A Diagnostic Benchmark for Long-Horizon Multi-Criteria Multimodal Reasoning on Heterogeneous Topological Graphs Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 83

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unresolved
no resolver link, observed 2026-08-02T22:00:11.292826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T22:00:11.292826Z digest=sha256:ecb37a71dc0e5e71f2a8ad94bf80b5a30b04e74aed8eac9fd4c70bd8ec400c46

Observation 76d456c0-6e86-4d93-9b3b-5e9da9396a31 · inbound

The Blind Spot of Adaptation: Quantifying and Mitigating Forgetting in Fine-tuned Driving Models cites this paper.

The Blind Spot of Adaptation: Quantifying and Mitigating Forgetting in Fine-tuned Driving Models Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 46

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verified exact
arxiv_id, observed 2026-05-10T22:15:50.159939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T20:04:46.144856Z digest=sha256:b3f1ebae0f2cde06a5b76fd00af6c0bf094c4586b718dcf579b1117adb152740

Observation 551b4c2e-6412-4a8e-ad7a-845e30c47251 · inbound

Steadily moving semi-infinite fracture in plane poroelasticity cites this paper.

Steadily moving semi-infinite fracture in plane poroelasticity Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 102

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local_arxiv, observed 2026-07-05T11:41:02.599005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-05T11:39:05.686584Z digest=sha256:147e6462aba4f942a423f6dc10000d9b4001fe36da7af0e5113f913461a7539a

Observation 854593ca-21fc-4297-a1f6-d4d81528ae87 · inbound

XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments cites this paper.

XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 102

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arxiv_id, observed 2026-05-10T05:51:10.333418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T05:46:36.865150Z digest=sha256:5631ce91e72529a8a9186cbb3ad1094ce8a0557bba3514f9bcedffc4d5b410ef

Observation 384f5715-3c9c-48e1-bd5a-03e24215ce75 · 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 Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 24

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arxiv_id, observed 2026-05-09T20:17:04.502659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-09T20:13:06.376037Z digest=sha256:4d5ff356f7f1794171b3be0c82b99c910845806f25e7e049ad326207def6b397

Observation 28ac769d-8bc6-4dff-b4c4-b4d879a81dd3 · inbound

From Accuracy to Visual Dependence: Auditing and Filtering Modality Collapse in Traffic VideoQA cites this paper.

From Accuracy to Visual Dependence: Auditing and Filtering Modality Collapse in Traffic VideoQA Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 18

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metadata mismatch
arxiv_id, observed 2026-06-30T06:14:18.584457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-30T06:14:11.109840Z digest=sha256:200d3a895d077b81769e2e9c170e5c304a347509ade5b3923af32579a7c7f13c

Observation 27726ca1-8c25-41f8-8d9b-faabb255d8e4 · inbound

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective cites this paper.

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives

Reference 104

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verified exact
local_arxiv, observed 2026-07-09T00:25:48.544589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-09T00:16:03.334057Z digest=sha256:66cb89cbbd427a831f6ac2ac439ee8c22d658879d4b21365b138b1c4cf8936fc