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Paper Citation Record · LEDGER

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving

As of 23 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2505.16805.

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

pith.paper-citation-record.v1
2505.16805 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:58:29.469849Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

49 of 49 outbound references displayed

  • verified exact1
  • verified fuzzy25
  • unresolved23
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 276b09f3-8f89-4601-b597-57c55ec5358b · outbound

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

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Flamingo: a visual language model for few-shot learning

Reference 1

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source=pdf_text observed=2026-08-07T14:58:25.629317Z digest=sha256:cd6b3f03f5efb46ec2934e441c5c0c0d3bd671d115958f8293a7cd99840e35a4

Observation a6730d38-5352-4701-b83f-702588a0ccfe · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 2

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source=pdf_text observed=2026-08-07T14:58:25.674610Z digest=sha256:099f59f710143b58c292b120f445d59e3ebdbca9c93a59ed79252a4a8d574c87

Observation da28aac1-5ac8-4f0d-890c-6f0285f9d013 · outbound

This paper cites nuscenes: A mul- timodal dataset for autonomous driving.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving nuscenes: A mul- timodal dataset for autonomous driving

Reference 3

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source=pdf_text observed=2026-08-07T14:58:25.768833Z digest=sha256:2c1d74f51b44cc9e1078042ef117da2a94ef421e0f0d37d6e49ec3d3ec0ee581

Observation 2fc13091-b70c-40bf-b23e-1dcb2bad557a · outbound

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

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

Reference 4

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Observation 658e3384-6dee-4d1c-80ac-f32e8f943178 · outbound

This paper cites Hierarchical adaptive path-tracking control for au- tonomous vehicles.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Hierarchical adaptive path-tracking control for au- tonomous vehicles

Reference 5

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raw_fallback, observed 2026-08-07T14:58:34.430878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation f008c154-31b4-4555-8a8e-3baa57277ba9 · outbound

This paper cites Driving with llms: Fusing object-level vec- tor modality for explainable autonomous driving.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Driving with llms: Fusing object-level vec- tor modality for explainable autonomous driving

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:26.005972Z digest=sha256:8cf7685145a94d76ebfb8ef024441b836ebfa053ed41130261b5942aa6d826d4

Observation 2ddb1823-cd2b-4e0f-9cbc-cde92dabd8e4 · outbound

This paper cites End-to-end autonomous driving: Challenges and frontiers.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving End-to-end autonomous driving: Challenges and frontiers

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation c6b23201-5027-46c4-9605-165d5077954c · outbound

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

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning

Reference 8

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source=pdf_text observed=2026-08-07T14:58:26.198547Z digest=sha256:b97339b39981fabe36bf686d69ff7136017efc6046e1c00a56780867f2381fdf

Observation 35a88492-c3c6-4ed6-b00b-561898cd854c · outbound

This paper cites Asynchronous large language model en- hanced planner for autonomous driving.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Asynchronous large language model en- hanced planner for autonomous driving

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:26.289949Z digest=sha256:04f2122679f852c7d27b25c7210ada156c7d2f571aa325d810430e8c5663b063

Observation bdbd7eb1-d76e-4bbb-b024-8f3c4ed19269 · outbound

This paper cites Causal confusion in imitation learning.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Causal confusion in imitation learning

Reference 10

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:26.408433Z digest=sha256:8978f8727fa1b9896d2668f618ce8310ba6f3c429eb8eba422feb5cd90147cb8

Observation bc04035f-fc92-4942-9dde-be290ae4159d · outbound

This paper cites Large scale interactive mo- tion forecasting for autonomous driving: The waymo open motion dataset.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Large scale interactive mo- tion forecasting for autonomous driving: The waymo open motion dataset

Reference 11

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:26.476847Z digest=sha256:9c933e4927026c742dc55ae69c74596a4f832dfdc10297b2c0ac300f2f410ddb

Observation 0d0205fd-355d-48d1-af6a-9274136f3409 · outbound

This paper cites Eva-02: A visual representation for neon genesis.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Eva-02: A visual representation for neon genesis

Reference 12

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source=pdf_text observed=2026-08-07T14:58:26.544589Z digest=sha256:9522c65a95fff1633da9d596ba6a1d5d166968fc84b9e68daa41ca5f2df647bc

Observation ea53a400-5b55-402c-894c-d7973c02efe3 · outbound

This paper cites Drive like a human: Rethinking autonomous driving with large language models.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Drive like a human: Rethinking autonomous driving with large language models

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:26.610928Z digest=sha256:48f2d32c92a94564a345320c34d9697bef8f4fecb07e6aa4997bc0a8f32251c2

Observation d3cff8ea-29db-40ea-8638-5f49bde2d487 · outbound

This paper cites Densetnt: End-to-end trajectory prediction from dense goal sets.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Densetnt: End-to-end trajectory prediction from dense goal sets

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:26.680757Z digest=sha256:9218bd00918fed3c2dc362eb64fad491b3ea505aa85f64eff08d14ae6db2abc0

Observation dd497a81-cdc4-4ea8-ae67-325beb9ad1da · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving LoRA: Low-Rank Adaptation of Large Language Models

Reference 15

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source=pdf_text observed=2026-08-07T14:58:26.737286Z digest=sha256:4ea5f3ccfdd3567a5a9d5f25474573468be894e423bcafcad2e3610914a98862

Observation 7b48be0e-41ba-4567-994b-50cc90dd02d8 · outbound

This paper cites Planning-oriented autonomous driving.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Planning-oriented autonomous driving

Reference 16

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:26.793646Z digest=sha256:f5baf174c1c8d8435fd0ac55d1c16be05840fdb1d330ec68dcbf5b92ee0757b0

Observation 2d24b8f7-be9d-4f82-a6ad-1d1745cfbd25 · outbound

This paper cites EMMA: End-to-End Multimodal Model for Autonomous Driving.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving EMMA: End-to-End Multimodal Model for Autonomous Driving

Reference 17

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source=pdf_text observed=2026-08-07T14:58:26.838099Z digest=sha256:770683704dc5b172c90ee8c32222374760fb63693b841ae4e62ef0a34a0e3438

Observation bea5467c-00ee-49bf-b0d8-963504c6763d · outbound

This paper cites Vad: Vectorized scene representation for efficient autonomous driving.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Vad: Vectorized scene representation for efficient autonomous driving

Reference 18

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:26.911877Z digest=sha256:fbd1363096cfb5d36e60e2526758d58bc316f33e92e9fda0654272ebc2f97335

Observation 4942fa4d-557b-40fd-b20c-5d609be22567 · outbound

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

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving

Reference 19

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source=pdf_text observed=2026-08-07T14:58:26.983248Z digest=sha256:646d17c88bfed52e896a72ff4e8de76819419e26a37b68556d8de6f2123ea9c9

Observation 03e0d999-d6e0-493d-9841-d6215ad40ef6 · outbound

This paper cites Inaction: Interpretable action decision making for au- tonomous driving.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Inaction: Interpretable action decision making for au- tonomous driving

Reference 20

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:27.040413Z digest=sha256:ce3c85593c9c5ee5d996e57765c929da8dfea8553b04ac4109ce94629459ba94

Observation 80953946-e365-43b6-99f3-324e2a9e316c · outbound

This paper cites Au- tonomous driving at ulm university: A modular, robust, and sensor-independent fusion approach.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Au- tonomous driving at ulm university: A modular, robust, and sensor-independent fusion approach

Reference 21

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:27.092747Z digest=sha256:f476333a14e7fc01ae0ce2b3a5444971d3a0aad9c5fb87fdbd0d160aa06ae589

Observation adc9cb75-6b75-48d5-865e-e91fa89796ad · outbound

This paper cites Pointpillars: Fast encoders for object detection from point clouds.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Pointpillars: Fast encoders for object detection from point clouds

Reference 22

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:27.148730Z digest=sha256:ac1e66042f283f408796bc2d71c7a56bf0e8df996b7887747deb933a671d18ad

Observation 40e301da-a670-4a90-807d-d533d53dd8e1 · outbound

This paper cites Exploring the Causality of End-to-End Autonomous Driving.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Exploring the Causality of End-to-End Autonomous Driving

Reference 23

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local_arxiv, observed 2026-08-07T14:58:29.723569Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:27.226916Z digest=sha256:24beace25d57462c9029c83726b67044a986c885b01c0edea1e320065566696d

Observation 67f12da4-7886-4733-93d2-73a475b83fd1 · outbound

This paper cites BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Reference 24

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source=pdf_text observed=2026-08-07T14:58:27.296305Z digest=sha256:6b0a8466bcb3257b1cace24d2bc77310cd3549d5a972924618a6258fa9b3204f

Observation 657b68b0-2bd9-4fd0-a9c0-e881d35f135c · outbound

This paper cites Deep learning for lidar point clouds in autonomous driving: A review.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Deep learning for lidar point clouds in autonomous driving: A review

Reference 25

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source=pdf_text observed=2026-08-07T14:58:27.363399Z digest=sha256:1a551f7cec798c9f5e68749dea8f756783cc53369cbe674d5c824c8c3f9f8a5f

Observation 6d192736-d6a9-4225-b5d0-72f4e7859d1a · outbound

This paper cites Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers

Reference 26

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source=pdf_text observed=2026-08-07T14:58:27.430400Z digest=sha256:8fe8110881d37c4202fbac00407df46318dcea15848c258eda10045385640bb6

Observation 2ccf52e2-d0ca-4a61-b625-4716c19e1401 · outbound

This paper cites Is ego status all you need for open-loop end-to-end autonomous driving? In CVPR, pages 14864–14873, 2024.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Is ego status all you need for open-loop end-to-end autonomous driving? In CVPR, pages 14864–14873, 2024

Reference 27

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raw_fallback, observed 2026-08-07T14:58:31.486803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:27.527232Z digest=sha256:7b5b9812e57b9ab87ad06b2f1001bf700c002b7a10743fbfc765827864cff042

Observation ddf4c21a-3518-4e07-a259-3590e10edb27 · outbound

This paper cites Visual instruction tuning.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Visual instruction tuning

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:27.612862Z digest=sha256:9f0f70c9ec35d4e7fb4ee67da8048f019488f0fe22c199f84d77be8d2561f336

Observation e8132cdb-2cdd-4921-94e5-b6813b232c60 · outbound

This paper cites Multimodal motion prediction with stacked transformers.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Multimodal motion prediction with stacked transformers

Reference 29

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raw_fallback, observed 2026-08-07T14:58:31.213795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:27.720052Z digest=sha256:17b466ab3b1e4c4d9cb7e7e6bcdb2cb60dad844b56927714d2869fa5af01049f

Observation 9d0dcdad-23d7-4ce8-917a-6b7016eab980 · outbound

This paper cites A Language Agent for Autonomous Driving.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving A Language Agent for Autonomous Driving

Reference 30

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source=pdf_text observed=2026-08-07T14:58:27.809926Z digest=sha256:87b84edd43aeb299539f083a9f166fdbe76be1e918576e262d2966a6a3cb8fd9

Observation eb765c33-6312-4629-89a1-caade7f13a43 · outbound

This paper cites Deep learning-based vehicle behavior prediction for autonomous driving applica- tions: A review.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Deep learning-based vehicle behavior prediction for autonomous driving applica- tions: A review

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:27.906795Z digest=sha256:6d80e69924c4f9094bf7a292d4ecfb3145097cb32894090cf161967174f48e4a

Observation c701a53e-8ef7-4e4d-a225-ce68ce2206ed · outbound

This paper cites Deep learning for safe autonomous driving: Current challenges and future direc- tions.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Deep learning for safe autonomous driving: Current challenges and future direc- tions

Reference 32

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raw_fallback, observed 2026-08-07T14:58:30.921292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:27.989836Z digest=sha256:923bad7abb70b92e69587185139ff375c2f6b3ca6323bc38dc9a0d7ecc72f1f8

Observation 07be542c-de1e-4962-9894-8ceaae8ad4be · outbound

This paper cites Decision-making framework for automated driving in highway environments.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Decision-making framework for automated driving in highway environments

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:28.083078Z digest=sha256:768032232a331242d5aa59053a93c5516adff90301c8b5a761f64b41e7e51bda

Observation 047da4be-6fae-4175-9278-52358047bac2 · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:58:28.171672Z digest=sha256:86e26a90bf626b57e425f2dbf40769e03bf2450f0eaf677693ea0a646d4b3b82

Observation 5f732ff8-fa63-4203-8438-4801f7bcd602 · outbound

This paper cites Safety-enhanced autonomous driving using inter- pretable sensor fusion transformer.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Safety-enhanced autonomous driving using inter- pretable sensor fusion transformer

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:28.249472Z digest=sha256:5f629abbc77dc2683ae69952bfdc2873c021bbe5268153b8a4726330af73e6c7

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

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

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

Observation cebb49ea-8182-4acb-8b74-db2d5f7aa3d4 · outbound

This paper cites Pip: Planning- informed trajectory prediction for autonomous driving.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Pip: Planning- informed trajectory prediction for autonomous driving

Reference 37

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source=pdf_text observed=2026-08-07T14:58:28.410535Z digest=sha256:dde410521776c0374311fb1ea395a8680856a762e6840a439f3d7d08f4cbc70d

Observation 1095fd23-e170-4e26-bfe9-9ca0b51e3ef1 · outbound

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

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Scalability in perception for autonomous driving: Waymo open dataset

Reference 38

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:28.484616Z digest=sha256:0c533136d82be03c2bba587cfbae31992f193fff9162ad3e35b43fc8444c1b82

Observation faa6b63c-bab3-42d3-ab00-6301e11b668e · outbound

This paper cites SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation

Reference 39

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T14:58:28.552906Z digest=sha256:27ad6fdc33de7ee964b20fa70479e4bd752de434c7a8b0322753b46c800846eb

Observation 888daba1-1c78-444b-b48d-22f1a99c9662 · outbound

This paper cites Motion planning for autonomous driv- ing: The state of the art and future perspectives.IEEE Trans- actions on Intelligent Vehicles, 8(6):3692–3711, 2023.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Motion planning for autonomous driv- ing: The state of the art and future perspectives.IEEE Trans- actions on Intelligent Vehicles, 8(6):3692–3711, 2023

Reference 40

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:28.630419Z digest=sha256:c0120fb7a40473744754f4331dd2f4bb04f35da923fe4fa74c3b2c5b433ac52d

Observation db53d814-cd28-4d22-a610-7f10c58984b5 · outbound

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

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models

Reference 41

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source=pdf_text observed=2026-08-07T14:58:28.686168Z digest=sha256:ddc78525d9eb027cab8385b9cae48975f292629d6f1141cc41d89620e05c0e09

Observation bf955cfa-3aef-4610-92a8-4814669c64ae · outbound

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

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving LLaMA: Open and Efficient Foundation Language Models

Reference 42

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source=pdf_text observed=2026-08-07T14:58:28.769780Z digest=sha256:71fbe7a9b92cca37452fc6721da533a91dd1e99357b0eaaf4638c48a96125bb7

Observation 923377ca-ffd3-496e-bda5-beadd6e834b8 · outbound

This paper cites Exploring object-centric temporal modeling for efficient multi-view 3d object detection.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Exploring object-centric temporal modeling for efficient multi-view 3d object detection

Reference 43

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:28.844406Z digest=sha256:fa75b4a723ca0b30b679c52565b13d558dc01101729b084a78a292f5f540ffdd

Observation 1b964573-7bcc-4c09-9b42-4e4732e2c4c5 · outbound

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

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning

Reference 44

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:58:28.924701Z digest=sha256:dda66ef75b32a677cc4d72a0805c00b579f38974f011912dc5b92784dff03d83

Observation 625b22fd-226e-4b2c-af43-dd1c3446eb2d · outbound

This paper cites DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Driving.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Driving

Reference 45

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source=pdf_text observed=2026-08-07T14:58:28.991762Z digest=sha256:21413a8b81fd7213321c5e89f9fad36abe26c9dd8e52795dc1224958baf6dfc9

Observation b2194ebb-152c-40f3-9ea3-804ecf462d76 · outbound

This paper cites Drive anywhere: Generalizable end-to-end au- tonomous driving with multi-modal foundation models.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Drive anywhere: Generalizable end-to-end au- tonomous driving with multi-modal foundation models

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T14:58:30.210154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:29.135428Z digest=sha256:7e1a147ea64d9aba74d3777e4cb2a6909a9b1e2343f635ff1f6f22d86faee83b

Observation 272ee550-210e-4146-87cb-9bf0cc3d9c90 · outbound

This paper cites Para-drive: Parallelized architecture for real- time autonomous driving.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Para-drive: Parallelized architecture for real- time autonomous driving

Reference 47

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T14:58:29.250475Z digest=sha256:bacfa452311638d469a50261b5a248aa94a67fa34f754a4012b31c09db9513e7

Observation 1d920b16-32fa-4835-90c6-cabdb2bba28e · outbound

This paper cites Drivegpt4: Interpretable end-to-end autonomous driving via large language model.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Drivegpt4: Interpretable end-to-end autonomous driving via large language model

Reference 48

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-07T14:58:29.382547Z digest=sha256:7bec83b5ba5b6855b536600687fcfdb9b8904c224fbb400bb4301abf3151ea96

Observation 4ce299dd-84a5-45bc-942f-47655291a668 · outbound

This paper cites Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes.

SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes

Reference 49

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source=pdf_text observed=2026-08-07T14:58:29.469849Z digest=sha256:eb58d448ac859031e5ddef0b87209c76f3bded68c118ce3b1c6d124fb49b3ffa

Pith citing papers

No inbound Pith citation observations are available.