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Paper Citation Record · LEDGER

Multimodal HD Mapping for Intersections by Intelligent Roadside Units

As of 10 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2507.08903.

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

pith.paper-citation-record.v1
2507.08903 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:24:51.445863Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

  • verified exact0
  • verified fuzzy17
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 57b81c6b-8f6d-4a12-9af1-ce951bc61551 · outbound

This paper cites Hdmapnet: An online hd map construction and evaluation framework,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Hdmapnet: An online hd map construction and evaluation framework,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:53.657719Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:26.056454Z digest=sha256:ecc7f3e5f16c2cd709ac0cca1e5d7abe4346af76bce46a7e2fe2185a360b6674

Observation a9a67dac-b691-4ee8-8aae-8267e8b2f11a · outbound

This paper cites MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:26.077528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:24:26.077528Z digest=sha256:92dd78f3d465e91d59b53d965886d9102905fcb5e9883223f1327d0a07f45799

Observation 41d3311b-95c3-4434-8693-e22acc97dfd1 · outbound

This paper cites Mgmap: Mask-guided learning for online vectorized hd map construction,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Mgmap: Mask-guided learning for online vectorized hd map construction,

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:26.206822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:24:26.206822Z digest=sha256:d1bae4bb79a9d5d6c2da543b8d59ac5bc09ea73e34047fa901fef02b824f393b

Observation d0269ce6-13ad-4dce-84e1-fb7642107ba7 · outbound

This paper cites V2x-seq: A large-scale sequential dataset for vehicle-infrastructure cooperative perception and forecasting,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units V2x-seq: A large-scale sequential dataset for vehicle-infrastructure cooperative perception and forecasting,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:53.557738Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:26.297230Z digest=sha256:8974bd4fa1daaaedcb298b2ee8e483440c9f02ca4dea27027455033fb5d90cc8

Observation 896fccc8-332a-4772-8634-f7cdbbdc50aa · outbound

This paper cites Design and implementation of edge-fog-cloud system through hd map generation from lidar data of autonomous vehicles,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Design and implementation of edge-fog-cloud system through hd map generation from lidar data of autonomous vehicles,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:53.463787Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:26.443079Z digest=sha256:d3dd56d83d347d94b3e6b6ffb2ee9f0e545e8cc2828eebd24203ab7954f98e16

Observation b55f5481-86c2-4ca5-a393-c22aac6fc126 · outbound

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

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Pointpillars: Fast encoders for object detection from point clouds,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:53.364306Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:26.520688Z digest=sha256:22aec43f776066d5792fc75e5ae1b15ad8dc2fd1114c8918778cac0bd3814c58

Observation b7806bf7-a876-4b67-af7e-d5a5872e2d9d · outbound

This paper cites Bevfusion: A simple and robust lidar-camera fusion framework,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Bevfusion: A simple and robust lidar-camera fusion framework,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:53.167091Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:26.583333Z digest=sha256:d59d6b96d770df86f304339cf15f0b4537d4bd678f8c438c6bd24ce65e40597a

Observation 674819aa-0ab0-4d30-8da0-4627cd898ece · outbound

This paper cites Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:53.041741Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:50.359477Z digest=sha256:2beae75d47a823c4bce7a50facbb76163c2fc104d8e4b397c3e3aa9f93970df1

Observation 6d1df1da-06ab-44cd-9133-f6bcf0325db0 · outbound

This paper cites Second: Sparsely embedded convolutional detection,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Second: Sparsely embedded convolutional detection,

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:50.456597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:24:50.456597Z digest=sha256:cc08dd4d4ba61b5a610d5dbb1ba2751ef77892ac7c55ab6708a3e21af21bcb4f

Observation e25fdb6a-2037-4161-9947-febdccd5b2fb · outbound

This paper cites Superfusion: Multilevel lidar-camera fusion for long- range hd map generation,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Superfusion: Multilevel lidar-camera fusion for long- range hd map generation,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:52.963299Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:50.498116Z digest=sha256:dbdcd36a21a4e4aed54f1bbb42e3804ef5c1c1e15c7fe0ec86fd5571aa4ecdc1

Observation 7f350c49-57b5-4c9d-a4ba-7a988f3c23cc · outbound

This paper cites V2x-vit: Vehicle-to-everything cooperative perception with vision transformer,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units V2x-vit: Vehicle-to-everything cooperative perception with vision transformer,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:52.873023Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:50.641296Z digest=sha256:9b234514faf230ca036f526ed1b8cc5b259ad76f115644a6a8b3c124a54a8822

Observation 87e0189b-6f62-4634-9f94-ac4e33a02456 · outbound

This paper cites Rope3d: The roadside perception dataset for autonomous driving and monocular 3d object detection task,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Rope3d: The roadside perception dataset for autonomous driving and monocular 3d object detection task,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:52.770414Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:50.721459Z digest=sha256:5b2574e28bb3dabd913ac1c28aeb3e10db2efd3776309502a2c5e84ba507c18b

Observation 045c4b44-7f48-4cc4-94be-35680bdbe648 · outbound

This paper cites Dair-v2x: A large-scale dataset for vehicle- infrastructure cooperative 3d object detection,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Dair-v2x: A large-scale dataset for vehicle- infrastructure cooperative 3d object detection,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:52.652573Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:50.780576Z digest=sha256:7ba8ee06a1ec7ff90532eac67e348c714ceeff60ab069abcf6988a2fa413520d

Observation c837b143-21f0-4305-8c65-a3ee1959c8ef · outbound

This paper cites Tumtraf v2x cooperative perception dataset,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Tumtraf v2x cooperative perception dataset,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:52.520925Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:50.875455Z digest=sha256:e49e8b1a1a083e686f51a047e5ecbbabdfda070c3d099909d83beacd7896fbad

Observation b246d3e9-9e32-42ce-aaec-021f269f21f8 · outbound

This paper cites Vi-map: Infrastructure-assisted real-time hd mapping for autonomous driving,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Vi-map: Infrastructure-assisted real-time hd mapping for autonomous driving,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:52.339850Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:50.966862Z digest=sha256:70731e1d4dba0b6b3b914c6e2d838824aa802f7f7c7a1690a5537183a4531f05

Observation 77c874ee-b547-4173-bc96-2ed542be0af7 · outbound

This paper cites Carla: An open urban driving simulator,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Carla: An open urban driving simulator,

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:51.055662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:24:51.055662Z digest=sha256:42f614597c01a1462d5cf653ff6087cb8490e7aeaf265a91bd47d82b73ab368b

Observation 12a86c65-1201-4a2f-9579-59ae405a4d46 · outbound

This paper cites Denoising of a multi- station point cloud and 3d modeling accuracy for substation equipment based on statistical outlier removal,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Denoising of a multi- station point cloud and 3d modeling accuracy for substation equipment based on statistical outlier removal,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:52.065409Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:51.097534Z digest=sha256:898cb84368590cc7ec1131054d48ec900188c487238d6f92139d0b9a3cea5879

Observation ff42cde6-160b-4dba-b54e-039f0988ed64 · outbound

This paper cites Three-dimensional alpha shapes,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Three-dimensional alpha shapes,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:51.914753Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:51.180532Z digest=sha256:c0a1378066bbb1de0f203269701281edec1f5e5a9a593426d8468da8c025552f

Observation 5ad0f6bb-566b-43c4-b9d4-f2d98ddd634b · outbound

This paper cites Least-squares fitting of a straight line,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Least-squares fitting of a straight line,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:51.825713Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:51.254048Z digest=sha256:8217169bfc5988e8cf174975da4949b9bd2c395b4974fb198eb72ddab1fc7833

Observation 1b91af7e-3044-4633-a8bc-6d79fdd2dc21 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units U-net: Convolutional networks for biomedical image segmentation,

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:51.328844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:24:51.328844Z digest=sha256:cbde7d47c4055b520646a25d163b08bd0d28f724e213b02903043abc4d54dc2b

Observation f26aeba6-f6b8-4a64-a31c-69b7c1313734 · outbound

This paper cites Pidnet: A real-time semantic segmentation network inspired by pid controllers,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Pidnet: A real-time semantic segmentation network inspired by pid controllers,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:51.712347Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:51.399138Z digest=sha256:4ea54675864fa40b68b3884d7fa16299e6cd65bd3a2c06430abcdcd36c2c3ef5

Observation 2a6c48d3-ffa3-4088-b46b-8b6a9aedeaa5 · outbound

This paper cites Vision transformer adapter for dense predictions,.

Multimodal HD Mapping for Intersections by Intelligent Roadside Units Vision transformer adapter for dense predictions,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:24:51.594130Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-06T18:24:51.445863Z digest=sha256:f8e8962227f8e133503ef2ecd073d45a12f6993e0b3df4262b41474a700af2ca

Pith citing papers

No inbound Pith citation observations are available.