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Paper Citation Record · LEDGER

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic

As of 15 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2412.04020.

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

pith.paper-citation-record.v1
2412.04020 v3

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:55:11.690517Z

measured 44 of 44 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 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

44 of 44 outbound references displayed

  • verified exact2
  • verified fuzzy33
  • unresolved9
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7ef0d8d8-56c3-4963-a8cd-924df57b075e · outbound

This paper cites ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0b692432-15f5-44df-9b5c-09b22c20d5bb · outbound

This paper cites Long-range pattern forecasting performance (20m+).

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Long-range pattern forecasting performance (20m+)

Reference 2

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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.

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Observation a69318c9-a650-4c72-9b5d-7f892fe0a12b · outbound

This paper cites Argoverse: 3d tracking and forecasting with rich maps.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Argoverse: 3d tracking and forecasting with rich maps

Reference 3

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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.

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Observation fdf1e809-3eac-447f-aebb-c84536db70a6 · outbound

This paper cites Exploiting more information in sparse point cloud for 3d single object tracking.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Exploiting more information in sparse point cloud for 3d single object tracking

Reference 4

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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.

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Observation ad7e54db-adcd-447b-959a-dbc17526745a · outbound

This paper cites Uncertainty-aware short-term motion prediction of traffic actors for urban transportation systems.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Uncertainty-aware short-term motion prediction of traffic actors for urban transportation systems

Reference 5

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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.

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Observation 1e6159da-dfe6-4bd3-bf19-89c652d6d20a · outbound

This paper cites Scaling Motion Forecasting Models with Ensemble Distillation.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Scaling Motion Forecasting Models with Ensemble Distillation

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e7d42538-3087-4252-b5dd-4a1948e51afd · outbound

This paper cites Tpnet: Trajectory proposal network for motion prediction.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Tpnet: Trajectory proposal network for motion prediction

Reference 7

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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.

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Observation 483da1fb-3cbb-48f6-b37d-67783f7be95d · outbound

This paper cites Hplflownet: Hierarchical permutohedral lattice flownet for scene flow estimation on large-scale point clouds.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Hplflownet: Hierarchical permutohedral lattice flownet for scene flow estimation on large-scale point clouds

Reference 8

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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.

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Observation 2eed5e39-2ad4-4115-918d-47fa008396f5 · outbound

This paper cites Social gan: Socially acceptable tra- jectories with generative adversarial networks.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Social gan: Socially acceptable tra- jectories with generative adversarial networks

Reference 9

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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.

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Observation 0c50a809-47df-438c-ba89-5c031e2f51ca · outbound

This paper cites Diffmap: Enhancing map segmentation with map prior using diffusion model.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Diffmap: Enhancing map segmentation with map prior using diffusion model

Reference 10

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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.

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Observation 7e6180bf-27b4-4378-914a-456e392ae7d0 · outbound

This paper cites P-mapnet: Far-seeing map generator enhanced by both sdmap and hdmap priors.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic P-mapnet: Far-seeing map generator enhanced by both sdmap and hdmap priors

Reference 11

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Source-reported events for the cited work

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

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Observation 3c5454a0-5b9e-4952-9a13-c93d2193c156 · outbound

This paper cites Motion segmentation & multiple object tracking by correlation co-clustering.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Motion segmentation & multiple object tracking by correlation co-clustering

Reference 12

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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=2026-08-11T21:55:11.538588Z digest=sha256:fc3dc55466c5f296d5c89855556a80b206d9e148d82fdc5bfa1acaa5c2c14c82

Observation 25045596-ec64-4f53-9889-17561f075a07 · outbound

This paper cites Kingma and Jimmy Ba.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Kingma and Jimmy Ba

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T21:55:11.543434Z digest=sha256:dd5f0ad8a40d464c8beaa61fc50dcd351e6ce263d7357aa03db8377c274b0c1a

Observation 4c76df80-bdc5-45d4-a0f3-41d3f2ece05f · outbound

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

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Pointpillars: Fast encoders for object detection from point clouds

Reference 14

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Source-reported events for the cited work

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

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Observation 2cd27416-7791-4ccc-96c9-6762e736e3b1 · outbound

This paper cites A survey on motion prediction and risk assessment for intelli- gent vehicles.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic A survey on motion prediction and risk assessment for intelli- gent vehicles

Reference 15

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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.

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Observation fa900620-973c-4b8b-8826-b5099840d2ee · outbound

This paper cites Weakly supervised class-agnostic motion prediction for urban transportation systems.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Weakly supervised class-agnostic motion prediction for urban transportation systems

Reference 16

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raw_fallback, observed 2026-08-11T21:55:12.337775Z

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=2026-08-11T21:55:11.557114Z digest=sha256:0cfef99e1d66a63be0fedc62212fb989da84606fe82100402d76ce367af11807

Observation b4135cca-7437-423b-8a68-66e6746129d3 · outbound

This paper cites Pnpnet: End-to-end per- ception and prediction with tracking in the loop.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Pnpnet: End-to-end per- ception and prediction with tracking in the loop

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation a3bfa411-d03d-41d1-982c-e57e78d866a1 · outbound

This paper cites Flownet3d: Learning scene flow in 3d point clouds.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Flownet3d: Learning scene flow in 3d point clouds

Reference 18

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raw_fallback, observed 2026-08-11T21:55:12.314443Z

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=2026-08-11T21:55:11.566374Z digest=sha256:724c470dd3933cd3547e276e959b0fd5ca639099670ee416c43d384ab3f557b9

Observation 79a3bf11-1828-4e55-84c0-798c37e55177 · outbound

This paper cites Self- supervised pillar motion learning for urban transportation systems.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Self- supervised pillar motion learning for urban transportation systems

Reference 19

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raw_fallback, observed 2026-08-11T21:55:12.299415Z

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.

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Observation 19f6caf7-815a-487f-b086-327d7b5de783 · outbound

This paper cites Multi- modal fusion transformer for end-to-end urban transporta- tion systems.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Multi- modal fusion transformer for end-to-end urban transporta- tion systems

Reference 20

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raw_fallback, observed 2026-08-11T21:55:12.284233Z

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.

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Observation 564c077b-bc50-4d35-a88d-b471073344eb · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T21:55:11.580048Z digest=sha256:b04f9e07770828f05266643eb52d6c15ec0cc2e3b2fb9f47b5921c0e4267b4b4

Observation f936440b-778b-498c-93ec-4e9854e1aa26 · outbound

This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Pointnet++: Deep hierarchical feature learning on point sets in a metric space

Reference 22

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Unavailable: canonical work link unavailable.

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Observation 252b1567-6454-471a-8875-6e133de3d3ab · outbound

This paper cites Long-term occupancy grid prediction using recurrent neural networks.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Long-term occupancy grid prediction using recurrent neural networks

Reference 23

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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=2026-08-11T21:55:11.588047Z digest=sha256:97bd99557a0b02d0992c3b73349c3d34e9dd66c054e8631ea2d0c1d38940f8ec

Observation 90ec5310-e91f-4a48-a7d1-0998c32242fc · outbound

This paper cites Beyond pixels: Leveraging geom- etry and shape cues for online multi-object tracking.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Beyond pixels: Leveraging geom- etry and shape cues for online multi-object tracking

Reference 24

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raw_fallback, observed 2026-08-11T21:55:12.235517Z

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=2026-08-11T21:55:11.592239Z digest=sha256:98cc120940184a02c9f95d4697dabeaaa807527ae1892114a8ac24c98edfe03f

Observation 95703aa7-5a47-4929-a3fd-ae3524f7900b · outbound

This paper cites Pointr- cnn: 3d object proposal generation and detection from point cloud.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Pointr- cnn: 3d object proposal generation and detection from point cloud

Reference 25

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raw_fallback, observed 2026-08-11T21:55:12.221184Z

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.

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Observation 8cdd7592-ec61-42cb-8e8c-01a2040e219e · outbound

This paper cites Pv-rcnn: Point- voxel feature set abstraction for 3d object detection.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Pv-rcnn: Point- voxel feature set abstraction for 3d object detection

Reference 26

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raw_fallback, observed 2026-08-11T21:55:12.207046Z

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=2026-08-11T21:55:11.601466Z digest=sha256:026f066fc8385a00c1e87026aa0342db5a330631be406bab634b606077367c46

Observation 1119728d-d6b3-4afa-bbed-603646174297 · outbound

This paper cites Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T21:55:11.605799Z digest=sha256:4275a0413e8285c8d296eaa79eab64afd9f5138ec2f6f854b0dfe799dd20ad7e

Observation 71f71aa1-e90e-481e-9172-edafeb8b5a96 · outbound

This paper cites Multi-object tracking with quadruplet convolutional neu- ral networks.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Multi-object tracking with quadruplet convolutional neu- ral networks

Reference 28

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raw_fallback, observed 2026-08-11T21:55:12.192289Z

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=2026-08-11T21:55:11.612863Z digest=sha256:ee10e57aac69a7ddbe4f7b7dab0da50a10a2a8751796f7db5530b33a4b871f41

Observation 69c9f22c-2b70-439b-ba9b-7dfb62bcd737 · outbound

This paper cites Attentiongan: Unpaired image-to-image transla- tion using attention-guided generative adversarial networks.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Attentiongan: Unpaired image-to-image transla- tion using attention-guided generative adversarial networks

Reference 29

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raw_fallback, observed 2026-08-11T21:55:12.178084Z

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=2026-08-11T21:55:11.617977Z digest=sha256:31c95b8a0d442756f9b589f4db5ed8399240dac8aea032712c8725fdd6361932

Observation b5e67b9a-efe4-425e-8eee-03b200d20b5f · outbound

This paper cites Monocular plan view net- works for urban transportation systems.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Monocular plan view net- works for urban transportation systems

Reference 30

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raw_fallback, observed 2026-08-11T21:55:12.164721Z

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=2026-08-11T21:55:11.622833Z digest=sha256:ec0f920717d4b25ab22acc65a49c2a641d0ed832a3e646d7b22998d142c46e15

Observation a229b186-2d49-4aff-83f9-b58774818bea · outbound

This paper cites Self- supervised class-agnostic motion prediction with spatial and temporal consistency regularizations.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Self- supervised class-agnostic motion prediction with spatial and temporal consistency regularizations

Reference 31

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raw_fallback, observed 2026-08-11T21:55:12.150456Z

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=2026-08-11T21:55:11.627652Z digest=sha256:5d644ace59e5d4c9d25b96461b6b0be7e814c6db890d7776ec52a9d24571091d

Observation e6dbe1c8-e2ed-4060-95d6-0f3e34cf30f9 · outbound

This paper cites Semi-supervised class-agnostic motion prediction with pseudo label regenera- tion and bevmix.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Semi-supervised class-agnostic motion prediction with pseudo label regenera- tion and bevmix

Reference 32

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raw_fallback, observed 2026-08-11T21:55:12.136391Z

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=2026-08-11T21:55:11.632531Z digest=sha256:8861af7990866d873c1575237392c954ad252eb2457a55f9cfd4e4554b83bf4c

Observation 238b1f55-77ab-4ac3-95ca-d508da760e13 · outbound

This paper cites Be-sti: Spatial-temporal integrated network for class-agnostic motion prediction with bidirectional enhancement.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Be-sti: Spatial-temporal integrated network for class-agnostic motion prediction with bidirectional enhancement

Reference 33

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raw_fallback, observed 2026-08-11T21:55:12.121625Z

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=2026-08-11T21:55:11.637293Z digest=sha256:b8b6d641046295fef07d71912a03f6f209531904e83604f8f0dad10b5b2ee536

Observation 5e9223d5-1790-409b-ab6b-47bd743bb6b2 · outbound

This paper cites Spatiotemporal transformer attention network for 3d voxel level joint segmentation and motion prediction in point cloud.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Spatiotemporal transformer attention network for 3d voxel level joint segmentation and motion prediction in point cloud

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:55:12.107068Z

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.

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Observation e8f4b388-0b27-42a0-af0d-7a077e616785 · outbound

This paper cites Spatiotemporal transformer attention network for 3d voxel level joint segmentation and motion prediction in point cloud.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Spatiotemporal transformer attention network for 3d voxel level joint segmentation and motion prediction in point cloud

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:55:12.092254Z

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=2026-08-11T21:55:11.646987Z digest=sha256:f828c8194725b2c726b8b6ff4aa3abc0088bbb5dab884193329b9bf3ddc1cab3

Observation 030f390a-30c8-42dd-94f9-d165d4701ef5 · outbound

This paper cites FIMP: Future Interaction Modeling for Multi-Agent Motion Prediction.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic FIMP: Future Interaction Modeling for Multi-Agent Motion Prediction

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-11T21:55:11.651925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T21:55:11.651925Z digest=sha256:7acb3cf07e0884fd6881d8defffc8a4a4d84a77729088bf75963a450af528701

Observation 63e3c27f-11a1-4bca-a708-2771bd43506e · outbound

This paper cites Mo- tionnet: Joint perception and motion prediction for urban transportation systems based on bird’s eye view maps.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Mo- tionnet: Joint perception and motion prediction for urban transportation systems based on bird’s eye view maps

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:55:12.077061Z

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=2026-08-11T21:55:11.656735Z digest=sha256:7401dee97eadac41e1109cb5abccd7078696868fb4d50d697772c066479d9120

Observation 4ca831ec-c1bc-4bf1-aebb-36a3e88a695c · outbound

This paper cites Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network

Reference 38

Resolution
verified exact
local_arxiv, observed 2026-08-11T21:55:11.749163Z

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=2026-08-11T21:55:11.661045Z digest=sha256:4507762e611652285095634a2518aea6bdd87fd27e446a071ab1ddb7c30a5923

Observation 418c70da-d7d6-4030-ab87-e922bd486de3 · outbound

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

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Center- based 3d object detection and tracking

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:55:12.061522Z

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=2026-08-11T21:55:11.665994Z digest=sha256:5f0bae750f0375686731d4c80e4bb8d4bc3a4be88b70cf123a9df6f603d45d88

Observation 63dea5b5-f012-47a2-92fe-506491f271ad · outbound

This paper cites Trajgen: Generating realistic and diverse trajectories with re- active and feasible agent behaviors for urban transportation 7 systems.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Trajgen: Generating realistic and diverse trajectories with re- active and feasible agent behaviors for urban transportation 7 systems

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:55:12.046582Z

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=2026-08-11T21:55:11.670559Z digest=sha256:f5da32bc18a3d87f723becde03e283f6221df6111aadb37e01eb3dc45584e6f1

Observation 889124c6-aa15-4d74-a95b-e5b0f789e589 · outbound

This paper cites Tnt: Target-driven trajectory pre- diction.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Tnt: Target-driven trajectory pre- diction

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:55:12.030696Z

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=2026-08-11T21:55:11.675197Z digest=sha256:8b2b4bfff2aa65c63d4cfdfb55d02931622ae7d5353d9388e8032d198b570da0

Observation 9a881ea0-113f-4995-91b9-8f178bcfce87 · outbound

This paper cites GenAD: Generative End-to-End Autonomous Driving.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic GenAD: Generative End-to-End Autonomous Driving

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-11T21:55:11.679735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T21:55:11.679735Z digest=sha256:3f7818fd0370660ff74177981ebedf89723906fc0b8cdc80dd46fa4321b80a2d

Observation f63d6f95-468b-4e5b-93ec-8a7d3697216b · outbound

This paper cites Point rcnn: An angle-free framework for rotated object detection.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Point rcnn: An angle-free framework for rotated object detection

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:55:12.015404Z

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=2026-08-11T21:55:11.685378Z digest=sha256:bcb8401d6e56b1b4b166e890a091313fc8c2d9ebf155ff56425a2e977ca719da

Observation 96802fe7-5149-473a-a6c4-a4f566f7dcb8 · outbound

This paper cites Mapprior: Bird’s-eye view perception with generative mod- els, 2023.

How Cars Move: Analyzing Driving Dynamics for Safer Urban Traffic Mapprior: Bird’s-eye view perception with generative mod- els, 2023

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T21:55:12.000184Z

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=2026-08-11T21:55:11.690517Z digest=sha256:18ad4e641ca2db30d716089e52ad637922054029a60ed71a6b6d50dd6645580e

Pith citing papers

No inbound Pith citation observations are available.