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Paper Citation Record · LEDGER

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

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

source=pdf_text observed=2026-08-12T14:46:37.891915Z digest=sha256:c0067aa2f05e81439f7d2d5e71087e97a913445b6e451eb7e66252a379fea12c

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

source=pdf_text observed=2026-08-12T14:46:37.901343Z digest=sha256:73788ab948eaa19d442725b08292f74ad1558bdc6a36250ae283bce13187c07c

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

source=pdf_text observed=2026-08-12T14:46:37.914286Z digest=sha256:6c080fb9784166c5cf208de165d5220aee8e735e875ffc30bd51d5799dc220ca

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

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

source=pdf_text observed=2026-08-12T14:46:37.922995Z digest=sha256:eae5c89869d458b02fe97d399cd57d559761c9724f416a7e51abaabdee2ba0b7

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=sha256:986f75c650d211c4dd3b7c0fdf138ea0a67ef2dc2b25601cf3c0c505c8fdc734

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:d5df34da5d9c473382dd50aaea947d997ace42ae2e99d3f651b9a68a4e2efe06

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=sha256:06665d023c50b6619816dd8f64017e32bbc46f7d93663679fc24020f952abb4a

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:d5282439c7c22441e4eab72ed82530a4dd7333ced53a6ea0a7bff2107d2f55e2

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=sha256:389aab1848def4f00eefd127aa02f3084e6f20e28114b31625889671aebfd372

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

source=pdf_text observed=2026-08-12T14:46:37.999157Z digest=sha256:2b6eb3470acaefece7957d854a438e4b9c14c8d2058c0e41432ae9965400eca9

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=sha256:537592813cdb20ac8d190c927fccd6aeda3b52a445b89b504a196c3bd108cbc3

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

source=pdf_text observed=2026-08-12T14:46:38.021404Z digest=sha256:7fb4d3a37e76b79b3408ef3ba6ac79ac803a2730206ac1af73a9a9ec3a3be313

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:a1429f8c4bb82332d8d2f4dbac4b8082a280ab90e3d3938142a54e120ade50c5

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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source=pdf_text observed=2026-08-12T14:46:38.040243Z digest=sha256:44ee732a9f43eeb921372852936c3888914f780e10afdd6f316710bda8785e19

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=sha256:69e7984a2800897a50b63cb21a5c47ed8f300fbd9728f062bf7eac34677220b6

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

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

source=pdf_text observed=2026-08-12T14:46:38.065116Z digest=sha256:449ec6ddec8d9dfb2dc03adbd5f203e23b768cbad7a931247be8c81423917279

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

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

source=pdf_text observed=2026-08-12T14:46:38.074105Z digest=sha256:e9d518c4f23597c8b1cbc71b3b76674eada125da6b2b279fecf69f896d59e569

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

source=pdf_text observed=2026-08-12T14:46:38.087564Z digest=sha256:1b1df908af5cf4d3d7fecef03e1f7de4984d09a6060b8417d4524d38eac394c5

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:f2e1946bcc3d2977f0d6f0f5a2c2a83daa2ae53b93e3963623e8d9ff2925f406

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

source=pdf_text observed=2026-08-12T14:46:38.110628Z digest=sha256:a81d9ca8acc0ea07360d6ba80c7eb2d02346d2f000a012825a279054e6701c01

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:b1e1ab2b643e7bec60b63a473f03f72c6eb5f55053c243b83f0848d06615736b

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

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

source=pdf_text observed=2026-08-12T14:46:38.124204Z digest=sha256:8dcf80ac32d757652ee06f8c914257004595db69702113752c745f2e893e2da8

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

source=pdf_text observed=2026-08-12T14:46:38.135145Z digest=sha256:d89f1622c12071c0d50c11feff89df27dc18de8f84f9b29ed181088ba1feb21f

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:fbcc2444058f50b22087110745b5bd21cae5e780e41f1198566ca333472dee8a

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:b714a96c77be01155c51f7e086f271a5045ffc3343a95e7b52b9a9c8a44dc727

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:cd1de08d9c6accc2e0612bcd8a1a2cff59fd36e61394352aefdc7eceafbcbab8

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=sha256:728ccab617da5d684c8854f69459a6bcbb9aa0c89408338bbddfc2dd193fed97

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-reported events for the cited work

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source=pdf_text observed=2026-08-12T14:46:38.176821Z digest=sha256:5e6de6c4b1c38ca3acd46c81a43ab99377e15d982ddcec2fea89cb51961f8bff

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

source=pdf_text observed=2026-08-12T14:46:38.202676Z digest=sha256:69ad9389b7563755331d06fa87a87aa87ca20a580f51cd61bb55815982629013

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

source=pdf_text observed=2026-08-12T14:46:38.210381Z digest=sha256:9fe2c48983344e59e706ce8dcf94d9be8cda3aedf9ce8a3c40898e1dcd3c40d9

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=sha256:813f3171da124980eebc6dd890129e4fcfb914aa7b2cd766f8c8b03d4d102c9f

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

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source=pdf_text observed=2026-08-12T14:46:38.225763Z digest=sha256:0a62bd7c145923c340ea47569760a6d02fca478d971c088d64174b5171bd821f

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

source=pdf_text observed=2026-08-12T14:46:38.246401Z digest=sha256:6831712ed917a58d16916233f54751410b42e5e62029cc38857354f50ad38534

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

source=pdf_text observed=2026-08-12T14:46:38.260129Z digest=sha256:ed228626909d8b1b52ecfdea9b26947396de5333d63b34f35801aea56b5ed9f3

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

source=pdf_text observed=2026-08-12T14:46:38.270447Z digest=sha256:c13a1896e97b181e487757d976ac0e0e251561bac64c3c701ca150b257e55e63

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=sha256:386632c777029a1c1e96141e79f900ee2954361bf6c4ea40e0e1505df4dbc921

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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source=pdf_text observed=2026-08-12T14:46:38.291874Z digest=sha256:20ff5f224e32c6dd140dd0608c667c180d133930581352ac875fdb2bf4fa8c69

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=sha256:91a51af6048b15a18d962d2c3699b31bc8c19d1371c2f14fa605a787c7a9cd37

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
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Source-reported events for the cited work

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

source=pdf_text observed=2026-08-12T14:46:38.306894Z digest=sha256:70c5246da3cc2b801307ef6d8d62e351b4a7b87ca0855a9f58312a4eacff7039

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

source=pdf_text observed=2026-08-12T14:46:38.320615Z digest=sha256:9a7bdedef4549b4a22c6bb9695b8a061a64cc5a1f94e5759fe84436c6781c1c6

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

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

source=pdf_text observed=2026-08-12T14:46:38.337883Z digest=sha256:342b2982b6d2482446a53c37279d7659f696a59510cbedcbb723b8e83af19ea1

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=sha256:0bff2d494213c0f5b05cce90d8a03c80f9d51dcff00b81e3baa48179202aa464

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

source=pdf_text observed=2026-08-12T14:46:38.367567Z digest=sha256:31f3a515e727c95861b1b5be144d6ce5fc0a60081f65e70b85106a800557c739

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=sha256:9feaa136a9e2061d4ba2f00661d8e22501fa5d026aab39cc62c71e53128cb4f4

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:f08efe5ac35251fbb1ba940ef69209e7e15900aa90c1d37ce8d5f56095ff237c

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.394421Z digest=sha256:ff745554d3616f1fcf3f0075c618e344ae572800484a671ae5ad0733b052d37a

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

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source=pdf_text observed=2026-08-12T14:46:38.402005Z digest=sha256:ed0ff167fad0830c9f108f828271203c5d67fcc66d32796c668d69ac398e56c2

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:c4508adf5892337432776d0a01960df3384f092f9f6a6a3f2b780229a43885d0

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:c6f2560a8a25fd0032ce338541ea6cad36dec8f0167141035f6ef219fcd5d039

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

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source=pdf_text observed=2026-08-12T14:46:38.441888Z digest=sha256:acfe77786edfcd554a9ddb83ed380d9108041a498a4861051399cfcc72b9b3ad

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

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source=pdf_text observed=2026-08-12T14:46:38.455914Z digest=sha256:bebb686782f9cd81c3b5048bdbfb0ce5f554a3f1a4907f8adb76dc457e502a6c

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:f0141dc47c934ed4041691ffb5132f96e2f8fc18c421d75d6c8ab5395cf633a5

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:46:38.491167Z digest=sha256:d32c14f816bf89afd7ffa58177acbc096562ef7a6eda55456bbbfcaae18fcdf6

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

source=pdf_text observed=2026-08-12T14:46:38.499128Z digest=sha256:47f9d05eaa41f349e3064aab4cc914559fabc0c33f32dee49ab969d16ba9439e

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

source=pdf_text observed=2026-08-12T14:46:38.509989Z digest=sha256:aec3cc5028a54f0153040eb3de1a90cbbab9296db232d2bda2c32c18d83b3456

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=sha256:61fc93b9cd8a73cce7dfd0e0011ce742a674304401ca15c157ed621275ba6f1e

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=sha256:886418327d4d6acf88130d2858668a426476b34352d3bb22cc5ef918a9883ef1

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

source=pdf_text observed=2026-08-12T14:46:38.537752Z digest=sha256:ec2753a3e420c1dc943e7dab3b680d76f88cd8517876d56646d07c99e95e91a8

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

source=pdf_text observed=2026-08-15T18:00:21.036673Z digest=sha256:5826519ffa814d0feac4c5a90e0917c7010b20d61594c5b9c117bf9cf3308fe1