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Paper Citation Record · LEDGER

Multimodal HD Mapping for Intersections by Intelligent Roadside Units

As of 23 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 2026-08-23T06:30:58.430688+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

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:26.056454Z digest=sha256:7c1fb3e52b33053cca9db8b4ac2a664fcc993a5a32eb685325ca5a58cbbca6e7

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:b0ac82d65a8048727ec21dfec50b5606fd5ac795c0c0b9dd5571217e563b6ce8

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:dc911603d51f8974b41e92f3c1ebfb66de7c2ec9442fe3ac09008406e3bc23f9

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:26.297230Z digest=sha256:043fac34feb446c74dfaccfd3d9aed05d04a6a3ea77ec18ea2e9bc26e8678c60

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:26.443079Z digest=sha256:5b763afc8a2df3a0be6350bfa35cc80e75d5e731fcf41418d6c735d6e8136fc7

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:26.520688Z digest=sha256:0480658bfec10910a45ffca78f950844382a01c1e96d5ef3d144cd950eb308ae

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:26.583333Z digest=sha256:57753e4e1496abc987bc6c8126f663c1918b89b3573e83446e211657f8105f26

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:50.359477Z digest=sha256:5ce0b96a3f18e8db58af8b46b3c02c5e2e9b6ce9b9b8cc70b78ec9eefbac726c

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=sha256:3fd4818a2956fe57b68b29b6546ee91d5db9e7b0330ae1515c6de92dfde730c1

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:50.498116Z digest=sha256:a91e41f04c03cd27a442bd760481d38178eadcd8c9d5df896aecf55b577dbdb4

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:50.641296Z digest=sha256:e30a4369175c155b2020ec395431067157e9f0e6cec04c666239930e84c0b567

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:50.721459Z digest=sha256:62494a5ad32c702c80a9e86fc142a964f0af636f5c30e4ceffa86bb88add2329

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:50.780576Z digest=sha256:2aef65b5712ee5b3960bb2b1f26ee066deb726b0906a8699251c7daff0ca10a7

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:50.875455Z digest=sha256:9ef46ee61a385ae2867aeaa39c50b45255558147670216acbedbf2cdb7da2675

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:50.966862Z digest=sha256:972b5a8f54d8440d5dfdc227990da11765fa96361c60d8dbfa710f3d155b096b

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=sha256:5df4dfe6322e1e353062e0e30211225e358594355f171c7683e4b1b51a576954

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:51.097534Z digest=sha256:34cf021fc77cabbef93ed67284ff023359a12fa0153fd5260a0e5a31bfdec2d2

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:51.180532Z digest=sha256:ca8d9fa879d9fe0f1b9812811f031f2c2ed246891c4054504fdc133458c5685b

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:51.254048Z digest=sha256:e9935b931f6bae97e03ff0566aad975094b2e36af314973cddd3dcbf1dcac07e

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=sha256:242d5b9f98641bbf775aec1943811c6b6785faf7d13b24afaa3b7d0e74a26c58

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:51.399138Z digest=sha256:42a685ce06b77ac63f4a3ac253391f17e0d2a1484d15123ca6aae3f2e1a4ec4a

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T18:24:51.445863Z digest=sha256:5cf783ac5483774fa15f8caae86c20c787416dc27f9b6f21214350619273c2ad

Pith citing papers

No inbound Pith citation observations are available.