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Paper Citation Record · LEDGER

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

As of 19 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 2026-08-18T06:34:40.430872+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-reported events for the cited work

Unavailable: canonical work link unavailable.

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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source=pdf_text observed=2026-08-07T14:58:25.833342Z digest=sha256:9250a8aaae9d4e09f8d91df66244304fcb511df2dad3047d6a6493c1310f7df3

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:25.931970Z digest=sha256:023b51300c38cca3d5a3415b7da9ced79b872f2ef0a169743f3f35be9f47c2c2

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:26.005972Z digest=sha256:6cbebbc351a61dee3eed187b23beb413d9b261c8added5b5265dcf643a18d920

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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raw_fallback, observed 2026-08-07T14:58:33.924638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:26.104090Z digest=sha256:127edbba15629ea24072638197f0ef68c011d8125f318c7bb42868e64353cea6

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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raw_fallback, observed 2026-08-07T14:58:33.715728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:26.289949Z digest=sha256:25f0104bc44a2685fd006dccdb96dbc0283b8c44101e31d206222f2c92c3a0a2

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:26.408433Z digest=sha256:bcd6a772b1f40b32a726658f36792caa6fd6454626e5875acb26703956120e4f

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:26.476847Z digest=sha256:13f8c3837a4d85cfc83cec5c6c32c96c953ed769357e02e711e71c36098c86fb

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

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:26.610928Z digest=sha256:7e66651b003c48f7c0827ee913809d5e313ad9035fa1806910e329f7065c0aa0

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:26.680757Z digest=sha256:dd720ccb4e3db789b93302cbebbfc24c42391e726cbe8702f8fb1aa7f268e42d

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:ee9cd4190225dd06b1f108c57ce1b82e45a9a06184ea71fdb5701129435be52f

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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raw_fallback, observed 2026-08-07T14:58:32.328108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:26.793646Z digest=sha256:0d8e6407885e1cb3be0cee8757f5fccdf111bd299b03e714181a33f27024a4aa

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=sha256:85b7d4b1f5148f947324b41f73981db5e68c47a20607875b9ff6b96fb516d988

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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raw_fallback, observed 2026-08-07T14:58:32.033789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:26.911877Z digest=sha256:4b749517e8661f9be55f18409d66ac4c8451c78d114a4fd40ff65344469acff7

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:27.040413Z digest=sha256:950ee422e4cfa3b24a292d55e9596d547c9dad08296287b8fe33dbc5630db760

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:27.092747Z digest=sha256:ee9e73009508907c57f23375913efef0a4ede177281e1680fa3cc814ed3c0565

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:27.148730Z digest=sha256:0c857eee834bc6f0d2016acff5296d1578c15cedb3fc78bfc4d5433dd19183f4

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:27.226916Z digest=sha256:c723e6ffc2263290138237d2b2f30132e266d6daf1686baa7bd03ae1d6fb8d5b

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:58:27.296305Z digest=sha256:16a605ba2036c56802a26b9eda7ebdf1708eb8b827694ff26304795310ee44a0

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:27.527232Z digest=sha256:14ca6bdd8c5c06a2403d14db7a49d6cbec6f78c9347ca47d2eed897f2f37b5bb

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:27.612862Z digest=sha256:533876d5d5e25560c2e710e3cbe94d7a2fc88d30b0181fee3566b303dc40d7ad

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

Resolution
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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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:27.720052Z digest=sha256:ec6447ba1c941d7670b9777729116bd21615159a8e52cd39c2f4e0f54edf608b

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=sha256:4f188172c0fbaa780ccd3dd73c9387eae1e6a972a1dd6aa81d7a0eec08a411b1

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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raw_fallback, observed 2026-08-07T14:58:31.057707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:27.906795Z digest=sha256:a4ab2757e598c672a82070188ca6975cef0f392df74141740d656d4723c0d80b

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

Resolution
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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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:27.989836Z digest=sha256:3be71efd653b783f7b62af4494641be03208b1cc3b409fc98479d5261d043384

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

Resolution
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raw_fallback, observed 2026-08-07T14:58:30.787438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:28.083078Z digest=sha256:e3338962ca9a6b8534d11bb1ba81fd890d6cb94bffd10e10a13feca4de5a537d

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:28.249472Z digest=sha256:fc1b5492c67e2babaa1ac71457b7de6f0ea30728c6d2b381b00d84a6f320b929

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=sha256:2fd034ad1e9613c116533107572d15268051dd19e6fa265284dd0d0a55e433a5

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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raw_fallback, observed 2026-08-07T14:58:30.575750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:28.484616Z digest=sha256:f0dd8353e20a52f7614690b096065ed2db39cce5765c553c4a95dd8067b03924

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=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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:28.630419Z digest=sha256:91a54642c9145bbd98178f01096556f521fba596662751e04eb93dca8a567506

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-reported events for the cited work

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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-reported events for the cited work

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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
verified fuzzy
raw_fallback, observed 2026-08-07T14:58:30.367821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:28.844406Z digest=sha256:317a29b7c4ea04c9d6c815d1dcc91f21e150ca83ebc1d4294378d406b84feea5

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

Resolution
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no resolver link, observed 2026-08-07T14:58:28.924701Z

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 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:29.135428Z digest=sha256:8f2ed43332d20725f2acfdba49cee42fbdb39e1714a4f3188988e97b724c129b

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
raw_fallback, observed 2026-08-07T14:58:30.014278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T14:58:29.382547Z digest=sha256:77c646a03da141a4a806e3e3274134cc3a880de3bcf11f749a7684360e03f57c

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=sha256:3db9b38e39fc8932a567c0e2bd8fa6790f90b9166acbf8b14d456b349661eda6

Pith citing papers

No inbound Pith citation observations are available.