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Paper Citation Record · LEDGER

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation

As of 15 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 1 inbound Pith citation observation for arXiv:2411.14927.

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

pith.paper-citation-record.v1
2411.14927 v2

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:46:38.537752Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:00:21.036673Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T18:00:21.296338Z

Reference resolution

58 of 58 outbound references displayed

  • verified exact2
  • verified fuzzy22
  • unresolved34
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dab783c2-d499-4f91-8e7a-381d0e4ea443 · outbound

This paper cites Vehicle- to-everything (v2x) services supported by lte-based systems and 5g,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Vehicle- to-everything (v2x) services supported by lte-based systems and 5g,

Reference 1

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raw_fallback, observed 2026-08-12T14:46:40.847600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:37.891915Z digest=sha256:83d65b9343a826c42c757f43c33111f3b096bf7d2ad4b12edca8db971216e262

Observation 9372c08f-202b-482f-bb5b-8ed9967a4d0b · outbound

This paper cites Challenges and solutions for cellular based v2x communications,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Challenges and solutions for cellular based v2x communications,

Reference 2

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raw_fallback, observed 2026-08-12T14:46:40.787972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:37.901343Z digest=sha256:3b38568395f7963e42d4e057db93c8efbc306d052eebf6c4e0f40375963ff609

Observation fb719626-4347-4098-b26d-cfe3a67b032b · outbound

This paper cites Classification of c-its services in vehicular environments,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Classification of c-its services in vehicular environments,

Reference 3

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raw_fallback, observed 2026-08-12T14:46:40.728582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:37.914286Z digest=sha256:83254fe4d313bde2d30bfa995389de1e05e857e38236b84bf4953978923028e5

Observation 2dbee1c6-6095-45ae-a80b-f0e23c557d24 · outbound

This paper cites Motiontrack: end-to-end transformer-based multi-object tracking with lidar-camera fusion,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Motiontrack: end-to-end transformer-based multi-object tracking with lidar-camera fusion,

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-12T14:46:40.659067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:37.922995Z digest=sha256:47d0bcfc77c4c9a7829648fe6ac1b8b9f66076659126953c3fc05e5c07650734

Observation e8b17eca-332a-4d73-8b18-118ca9ed0884 · outbound

This paper cites Track- former: Multi-object tracking with transformers,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Track- former: Multi-object tracking with transformers,

Reference 5

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no resolver link, observed 2026-08-12T14:46:37.937022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:37.937022Z digest=sha256:71b8c013b1615254deca1a291897939951c5c93dfc573774ecdad3cc46e9cfd2

Observation a6ee2987-f30e-41f6-aaa5-acb7aafacacc · outbound

This paper cites Motr: End-to-end multiple-object tracking with transformer,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Motr: End-to-end multiple-object tracking with transformer,

Reference 6

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source=pdf_text observed=2026-08-12T14:46:37.948133Z digest=sha256:84f6892b182ab81e6b451023d1e9711ff9ca9a72ae57af2605a56f9ad6f5a390

Observation 78686c5c-e09e-4754-b491-05bbe2bc2714 · outbound

This paper cites Tracking objects as points,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Tracking objects as points,

Reference 7

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source=pdf_text observed=2026-08-12T14:46:37.965972Z digest=sha256:074977f7c2a262edeb59321cca51b3ca1073c96fffbefc8390a2f98d51bd8084

Observation 27352283-163b-4d03-b8f1-4caed86f71e2 · outbound

This paper cites Center-based 3d object detection and tracking,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Center-based 3d object detection and tracking,

Reference 8

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source=pdf_text observed=2026-08-12T14:46:37.981128Z digest=sha256:2b1c6ec630a7c2a02d63da29fd162d7e129d91221e8c5f6fb0d4820aa271ec0b

Observation b6e37b5f-eb37-4053-b050-f96257d216bd · outbound

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

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation V2x-seq: A large-scale sequential dataset for vehicle-infrastructure cooperative perception and forecasting,

Reference 9

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source=pdf_text observed=2026-08-12T14:46:37.988237Z digest=sha256:b97b1291457041cba7ab57b148815192c20038ad36ae0dc5942b95755f887198

Observation cfe06c04-bd2c-47ba-8810-d5671a991c60 · outbound

This paper cites Resource allocation modes in c-v2x: from lte-v2x to 5g-v2x,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Resource allocation modes in c-v2x: from lte-v2x to 5g-v2x,

Reference 10

Resolution
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raw_fallback, observed 2026-08-12T14:46:40.489787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:37.999157Z digest=sha256:40d04f73a0370c6553955401e676dfe2eb6aad838c973b091958584b631893db

Observation d90dd4a0-99b7-4179-9357-b3869ab3eb6c · outbound

This paper cites Integrated sensing and communications (isac) for vehicular communication networks (vcn),.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Integrated sensing and communications (isac) for vehicular communication networks (vcn),

Reference 11

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source=pdf_text observed=2026-08-12T14:46:38.007920Z digest=sha256:0a10e85cc6c1c2114b0ab385ed7221155263e332c008d5922fafc54cb848b3a1

Observation b47b6eab-e688-4cda-bddb-590eec623c81 · outbound

This paper cites A study on v2i based cooperative autonomous driving,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation A study on v2i based cooperative autonomous driving,

Reference 12

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raw_fallback, observed 2026-08-12T14:46:40.425366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.021404Z digest=sha256:fcbdbb7096ca5deb50d4eb633ad55daf45ae5a9b18286f67a56f9e4778751891

Observation e10efa02-7bb6-4665-af89-f37acf4870d1 · outbound

This paper cites Integrated sensing and communications: Recent advances and ten open challenges,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Integrated sensing and communications: Recent advances and ten open challenges,

Reference 13

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.031445Z digest=sha256:fe65753ecd219fc57b2882ef870d2b4a13d13168f8520670551aecf056b385de

Observation c4216c17-e0b8-400e-852e-641f194f579d · outbound

This paper cites Cooper: Cooperative perception for connected autonomous vehicles based on 3d point clouds,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Cooper: Cooperative perception for connected autonomous vehicles based on 3d point clouds,

Reference 14

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.040243Z digest=sha256:3b04334ff53ee56dbc3b302ece5becdcacfe0a8b4f4a574d21ce8c1439253922

Observation 42b3a0d0-6516-4f4c-afc9-520b9a625602 · outbound

This paper cites Learning distilled collaboration graph for multi-agent perception,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Learning distilled collaboration graph for multi-agent perception,

Reference 15

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source=pdf_text observed=2026-08-12T14:46:38.059450Z digest=sha256:7af67bad5b67ccb755cdc0a7cf6b0561bd8e6e703756b391563d7e4675050e02

Observation 4e3a9679-26eb-41df-9da2-3b62eee2f82d · outbound

This paper cites Opv2v: An open benchmark dataset and fusion pipeline for perception with vehicle-to- vehicle communication,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Opv2v: An open benchmark dataset and fusion pipeline for perception with vehicle-to- vehicle communication,

Reference 16

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raw_fallback, observed 2026-08-12T14:46:40.284212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.065116Z digest=sha256:100825566cfbe6db360f0db25ebc5830633af21595cf5db7626fa0dacf7e8448

Observation f567d167-43b1-4613-a61d-32796ffb531f · outbound

This paper cites F-cooper: Feature based cooperative perception for autonomous vehicle edge computing system using 3d point clouds,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation F-cooper: Feature based cooperative perception for autonomous vehicle edge computing system using 3d point clouds,

Reference 17

Resolution
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raw_fallback, observed 2026-08-12T14:46:40.251076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.074105Z digest=sha256:0fd14a998bae7cbfa2f7a5d768bdf4f92265f3ab8b50b061efb5ebe4ce285e8f

Observation 38ffb164-ece7-48d6-a38d-51e55dc967b3 · outbound

This paper cites V2vnet: Vehicle-to-vehicle communication for joint percep- tion and prediction,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation V2vnet: Vehicle-to-vehicle communication for joint percep- tion and prediction,

Reference 18

Resolution
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raw_fallback, observed 2026-08-12T14:46:40.193989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.087564Z digest=sha256:31a768cc7348989a3ba55d640b13104b18027ddfdb1624daa3239df1643179e6

Observation a53a937b-aa3c-4b87-9e8d-cfc4b7029064 · outbound

This paper cites Coopernaut: End-to- end driving with cooperative perception for networked vehicles,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Coopernaut: End-to- end driving with cooperative perception for networked vehicles,

Reference 19

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source=pdf_text observed=2026-08-12T14:46:38.096332Z digest=sha256:b6750b776237574522177a8724e1fc261a76f03b21b9cd5ead2230cd0c941677

Observation 049c5cc9-7566-4564-afe7-e33812b05a0c · outbound

This paper cites How2comm: Communication-efficient and collaboration- pragmatic multi-agent perception,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation How2comm: Communication-efficient and collaboration- pragmatic multi-agent perception,

Reference 20

Resolution
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raw_fallback, observed 2026-08-12T14:46:40.088808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.110628Z digest=sha256:47d580ea1d5cccbb4533007f96fbb7ab69af6092f5134cd1d23e50c06eee5d4d

Observation 31617cf9-981b-4c0c-93a0-31e4b4946f09 · outbound

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

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation V2x-vit: Vehicle-to-everything cooperative perception with vision transformer,

Reference 21

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source=pdf_text observed=2026-08-12T14:46:38.118241Z digest=sha256:ea417ef7a20e2bb070caf7e2bafee53e35686f76258254e075a5df18c69a86ff

Observation aa2e474f-05a9-427b-9ebf-e6f6e922e5a8 · outbound

This paper cites Flow-based feature fusion for vehicle-infrastructure cooperative 3d object detection,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Flow-based feature fusion for vehicle-infrastructure cooperative 3d object detection,

Reference 22

Resolution
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raw_fallback, observed 2026-08-12T14:46:40.033846Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.124204Z digest=sha256:5c42df51a06f72cf52f7ea262f94c4c28bb291e6239551dafe001086d0aa9431

Observation 5727eca4-1dd6-4511-bdc2-932c87d954b7 · outbound

This paper cites Leveraging Temporal Contexts to Enhance Vehicle-Infrastructure Cooperative Perception.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Leveraging Temporal Contexts to Enhance Vehicle-Infrastructure Cooperative Perception

Reference 23

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local_arxiv, observed 2026-08-12T14:46:39.104692Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.135145Z digest=sha256:a07c8da8d41bb2c03396e1c1a01fbc5066aaff8bf5d17e402c8323ebb7ca5046

Observation 78a28cc6-c055-4473-add0-94726026977e · outbound

This paper cites Learning Cooperative Trajectory Representations for Motion Forecasting.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Learning Cooperative Trajectory Representations for Motion Forecasting

Reference 24

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source=pdf_text observed=2026-08-12T14:46:38.143692Z digest=sha256:558f9f94f5f5effff4ef626146daac8c0463e23cf6cee15813585b0187300cef

Observation e9472dd1-1189-4d68-80ab-77dfe8a44c11 · outbound

This paper cites Mutr3d: A multi- camera tracking framework via 3d-to-2d queries,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Mutr3d: A multi- camera tracking framework via 3d-to-2d queries,

Reference 25

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source=pdf_text observed=2026-08-12T14:46:38.153946Z digest=sha256:053f3933c54706e4ffa5d75102b484f041a5864e1560841d548503b62e3129cb

Observation a9af536d-5139-4802-87d6-410e9b81c4c9 · outbound

This paper cites Motrv2: Bootstrapping end-to-end multi-object tracking by pretrained object detectors,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Motrv2: Bootstrapping end-to-end multi-object tracking by pretrained object detectors,

Reference 26

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source=pdf_text observed=2026-08-12T14:46:38.159446Z digest=sha256:5861c3631d5df7f0d965df11bdd3b93bfddeec4f9a94e21e595f944cfef9248c

Observation 18e3a445-fca2-42bd-a837-1b29cc0f906b · outbound

This paper cites MOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation MOTRv3: Release-Fetch Supervision for End-to-End Multi-Object Tracking

Reference 27

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source=pdf_text observed=2026-08-12T14:46:38.168377Z digest=sha256:8db232b928aff0e625986525f53e51b1373ca7143fad1fdd4b94b84469c1186f

Observation 7b1083ec-5026-4203-83ba-859228c82e7c · outbound

This paper cites Sparse4D v3: Advancing End-to-End 3D Detection and Tracking.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Sparse4D v3: Advancing End-to-End 3D Detection and Tracking

Reference 28

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source=pdf_text observed=2026-08-12T14:46:38.176821Z digest=sha256:6fdf8ff01d99c2cedd58007bd188ccc89699ee653fb3bb16b19a0843a040fa6b

Observation bbf6da04-060c-4fca-8439-c1a60b0d27f7 · outbound

This paper cites End-to-end 3d tracking with decoupled queries,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation End-to-end 3d tracking with decoupled queries,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:46:39.924941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.202676Z digest=sha256:710fc65bc7a5108870c0c1ab5a6b38f84dc093dd9626780cc2d33e5e937250c7

Observation c399ef7e-892e-4e5c-8a93-105afe1e34d3 · outbound

This paper cites Hydro-3d: Hybrid object detection and tracking for cooperative perception using 3d lidar,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Hydro-3d: Hybrid object detection and tracking for cooperative perception using 3d lidar,

Reference 30

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raw_fallback, observed 2026-08-12T14:46:39.889272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.210381Z digest=sha256:8431414c62762cc0573b5c77fbd44cad0b27b8e29546ee3fc72117e6914f123d

Observation cced8f77-66a8-4399-9e9f-b2c7ee601b51 · outbound

This paper cites Collab- orative multi-object tracking with conformal uncertainty propagation,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Collab- orative multi-object tracking with conformal uncertainty propagation,

Reference 31

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.217412Z digest=sha256:0c6329b9239b50e87fcba95d4cea5cf5feab88caa32cf7c5a5332c1b6c6ae55b

Observation 60d43412-19c4-46ce-b146-87f32ffb1da7 · outbound

This paper cites V2x- sim: Multi-agent collaborative perception dataset and benchmark for autonomous driving,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation V2x- sim: Multi-agent collaborative perception dataset and benchmark for autonomous driving,

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.225763Z digest=sha256:37a0034ccf5bbb13d7654e1c51cff59c1a6353306a0330bd2ab9f63b1c518901

Observation 20ecc933-933a-44c9-b748-0cf2c1554c33 · outbound

This paper cites Cooperative 3d multi-object tracking for connected and automated vehicles with complementary data association,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Cooperative 3d multi-object tracking for connected and automated vehicles with complementary data association,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:46:39.740908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.246401Z digest=sha256:4f2e2dc5a15b0440455ae70235eda9b136259118d86e0aee7549176050dbf9dd

Observation 4fe27ca6-9548-4f91-bf71-1e3a5ac91650 · outbound

This paper cites Probabilis- tic 3d multi-object cooperative tracking for autonomous driving via differentiable multi-sensor kalman filter,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Probabilis- tic 3d multi-object cooperative tracking for autonomous driving via differentiable multi-sensor kalman filter,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:46:39.695162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.260129Z digest=sha256:b037d9e76307db67c6b8d4900ec39f771ae444581754090e5a849a1cb24061b5

Observation 7806866f-612d-4b3b-a4d9-b23169986a91 · outbound

This paper cites V2v4real: A real-world large-scale dataset for vehicle-to-vehicle cooperative perception,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation V2v4real: A real-world large-scale dataset for vehicle-to-vehicle cooperative perception,

Reference 35

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raw_fallback, observed 2026-08-12T14:46:39.654026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.270447Z digest=sha256:c1322e97f93691368957c6445dfc9ea675293cfca791fa32ef8750443280b1f3

Observation 1ae0393c-2194-42ac-8853-0fd3706f6850 · outbound

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

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.283575Z digest=sha256:d0ae44684a6dbe2b5adab5a1836dd39242f515e61423fb889e43c6b97fb27b51

Observation c5802d9d-8378-43d6-9a4b-c0973d409eac · outbound

This paper cites BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection

Reference 37

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.291874Z digest=sha256:a6bee0103d48a2af0ff8e67bec767e5ccacac48a1d948a2f31b29c379bea5180

Observation 9403f319-ebbb-4623-b47f-eb58df533e42 · outbound

This paper cites Bevdepth: Acquisition of reliable depth for multi-view 3d object detec- tion,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Bevdepth: Acquisition of reliable depth for multi-view 3d object detec- tion,

Reference 38

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T14:46:38.299442Z digest=sha256:58ae5ab79d5bcfa6afeca830ba8773a9b2657c7381c7c127418f6df9846a58d8

Observation 3024932b-ac1c-431e-b789-419bd6ae2c44 · outbound

This paper cites Bevstereo: Enhancing depth estimation in multi-view 3d object detection with temporal stereo,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Bevstereo: Enhancing depth estimation in multi-view 3d object detection with temporal stereo,

Reference 39

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verified fuzzy
raw_fallback, observed 2026-08-12T14:46:39.561334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.306894Z digest=sha256:b064dad812589c42ef9048f7e431e5395eb87a70cafae1cf96118492fb90a5ed

Observation d6e20cf0-8447-4be9-9543-2f670308c4f0 · outbound

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

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,

Reference 40

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verified fuzzy
raw_fallback, observed 2026-08-12T14:46:39.504267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.320615Z digest=sha256:dbf9cf3bd250029d586614022e2c5e78bc33df5efea754b9b1c0ca04c2af5a39

Observation b5b8bdcc-debd-428c-bb26-f2bdd9d2c3b8 · outbound

This paper cites Simple- bev: What really matters for multi-sensor bev perception?.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Simple- bev: What really matters for multi-sensor bev perception?

Reference 41

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verified fuzzy
raw_fallback, observed 2026-08-12T14:46:39.474557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.337883Z digest=sha256:ddeb9bb29069af4851a82af0d65f8a14330974e6cd8b92655637478cb6d75b7b

Observation 7cc4f136-1540-428c-9cc9-2133160a475d · outbound

This paper cites Fast-bev: A fast and strong bird’s-eye view perception baseline,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Fast-bev: A fast and strong bird’s-eye view perception baseline,

Reference 42

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.356745Z digest=sha256:ca516fb4adb08da5f390a24a13029319b88061f5764d40ddbb652e04d2a903da

Observation 201f4dc9-80e0-4b74-a368-b42a10cb48b9 · outbound

This paper cites Matrixvt: Efficient multi- camera to bev transformation for 3d perception,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Matrixvt: Efficient multi- camera to bev transformation for 3d perception,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:46:39.393385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.367567Z digest=sha256:f3bb55019e3202c9c5f63919c14f68d980e54fdee7d3be097ce1eb9fcdc58e82

Observation b954fd0d-66d5-4ea4-b80e-f0bba083834d · outbound

This paper cites Is-fusion: Instance-scene collaborative fusion for multimodal 3d object detection,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Is-fusion: Instance-scene collaborative fusion for multimodal 3d object detection,

Reference 44

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.380783Z digest=sha256:65e468251491329fa0a8216ba48b0405023dde4b1de4f7b1f1853b76e5ac8f2c

Observation 3763e69c-62f3-435b-9845-e62723d5ee64 · outbound

This paper cites Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation,

Reference 45

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.387977Z digest=sha256:3fb0769658e634c13c9cb58e1e56c54615b79f85c10612b9b93ecadd1f9cb77e

Observation e4d44034-2b40-4f62-90de-e330ab2b0798 · outbound

This paper cites FusionFormer: A Multi-sensory Fusion in Bird's-Eye-View and Temporal Consistent Transformer for 3D Object Detection.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation FusionFormer: A Multi-sensory Fusion in Bird's-Eye-View and Temporal Consistent Transformer for 3D Object Detection

Reference 46

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no resolver link, observed 2026-08-12T14:46:38.394421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.394421Z digest=sha256:4d1a9387c2220b8b9ace88cb99ffaecbf673b3c14b93c8e38de2629b7db886d0

Observation 31a2e362-bc4d-4106-ad93-8af703662f2e · outbound

This paper cites Uniformer: Unifying convolution and self-attention for visual recogni- tion,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Uniformer: Unifying convolution and self-attention for visual recogni- tion,

Reference 47

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.402005Z digest=sha256:3fb1030499d21946a437179eca5c9bd31cfeb9e4edc208e079e2db076f576abd

Observation 09f21062-cdcb-4118-ba9d-6027a58a3464 · outbound

This paper cites Planning-oriented autonomous driving,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Planning-oriented autonomous driving,

Reference 48

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.414349Z digest=sha256:13f9a1e7f862ac0c78b881b4bc1ab241a6bac10dcad3676bf640b2e9bed21b70

Observation 4969b2b1-e7cc-443a-896d-94234cde9a08 · outbound

This paper cites GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving

Reference 49

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.433890Z digest=sha256:1f600ff031d8bb215832619141b341b364b0aa9dc427e9cdd9c142bebc9ff523

Observation d01a7497-f968-4248-813f-e047c1c63d52 · outbound

This paper cites FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving

Reference 50

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.441888Z digest=sha256:7036d6583e313bb152d16f29e1da089a45eda2df6933c3016b3cd7fb85d0de3e

Observation 4edaf08d-d176-43f3-a16b-c244309e7ebe · outbound

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

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation

Reference 51

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.455914Z digest=sha256:0cf348ea2d37d03845baf34f10a1e45da6433ae7608f147a3c2be7fcd5e30402

Observation 07ec7b84-9866-4d6d-86bc-e843eb3e7903 · outbound

This paper cites End-to-End Autonomous Driving through V2X Cooperation.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation End-to-End Autonomous Driving through V2X Cooperation

Reference 52

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.480861Z digest=sha256:c3f78a7d922066989a78476070fc130b8ddd43ed83b770f3d1b86c7a49395992

Observation f5f58ff5-41ef-4a87-b76b-719c8c476044 · outbound

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

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Pointpillars: Fast encoders for object detection from point clouds,

Reference 53

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no resolver link, observed 2026-08-12T14:46:38.491167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.491167Z digest=sha256:b7391683b27bdd02dd8991fe9b7602802538a0238745d61aa49e114c8d738a71

Observation a69eee38-0458-4cb6-bcfb-a8dbd7bed722 · outbound

This paper cites QUEST: Query Stream for Practical Cooperative Perception.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation QUEST: Query Stream for Practical Cooperative Perception

Reference 54

Resolution
verified exact
local_arxiv, observed 2026-08-12T14:46:38.623702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.499128Z digest=sha256:59146acda3ac1be49f8cc1a6010a4ad68468bbef3f8fec1f62b2de30811488ce

Observation 685e9fa4-f3e1-4812-ac13-bfade3dcff1f · outbound

This paper cites Transiff: An instance-level feature fusion framework for vehicle-infrastructure cooperative 3d detection with trans- formers,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Transiff: An instance-level feature fusion framework for vehicle-infrastructure cooperative 3d detection with trans- formers,

Reference 55

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verified fuzzy
raw_fallback, observed 2026-08-12T14:46:39.253501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.509989Z digest=sha256:e74f26a622047d59e46e9819d7309d3e15eee02aa2d6d4d7a70884c9236db015

Observation 00db6aa5-0bc5-4767-8a29-b4adfeedab5d · outbound

This paper cites nuscenes: A multimodal dataset for autonomous driving,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation nuscenes: A multimodal dataset for autonomous driving,

Reference 56

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.524276Z digest=sha256:db130df1afbbbf9f3769f58e8bacd2ecd95125defef58faee3d9083177abd4fe

Observation ae8ec155-f0be-481b-b6d2-4ccd508eb76f · outbound

This paper cites Vision meets robotics: The kitti dataset,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation Vision meets robotics: The kitti dataset,

Reference 57

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.530691Z digest=sha256:6ba1926e01a83ec5ba010032ac70402ed7e5e56d8dedf50c12d4809d34e7cc4a

Observation 88d56270-ecb6-4d3d-9e63-5e921dfb8616 · outbound

This paper cites 3d multi-object tracking: A baseline and new evaluation metrics,.

LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation 3d multi-object tracking: A baseline and new evaluation metrics,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:46:39.140366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T14:46:38.537752Z digest=sha256:a14d20d9d62dc56771cd0b72e4f00d6649f5f560cd49412fa1c90f9e37b563c7

Pith citing papers

Observation a7e2cf67-c0a3-4272-a8d5-2dcfd6bf9680 · inbound

CoopTrack: Exploring End-to-End Learning for Efficient Cooperative Sequential Perception cites this paper.

CoopTrack: Exploring End-to-End Learning for Efficient Cooperative Sequential Perception LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation

Reference 65

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verified exact
local_arxiv, observed 2026-08-15T18:00:21.302661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T18:00:21.036673Z digest=sha256:b8e4dd44cce60e282cf3a8c1c16c3d8802bd08a5c864dec531dfb1c1182e6bed