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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 2026-08-14T06:32:32.682623+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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  • malformed identifier0
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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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.538588Z digest=sha256:245d2adece202a6af9e88f756c5c0b207898aaed47e46b88d1e0189952bde82c

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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raw_fallback, observed 2026-08-11T21:55:12.367940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.557114Z digest=sha256:6f97417120adc11fa52e13496b790f35666a3def48514e45ab493548f2e3a64c

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.

source=pdf_text observed=2026-08-11T21:55:11.561855Z digest=sha256:f85c9a6cdd4d6d35a8f9db136d179ebb8f4ed2204a185b87a09f9558dbf8dc22

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.566374Z digest=sha256:96d1fd185b82d532d2320d82052bcad891f41985169cc85e91ac8d885bb71c38

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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.588047Z digest=sha256:a219c9a94b332b0f4e58048fe30910f33e10dae8b391c7dc04b675e89450d103

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.596956Z digest=sha256:f09f608a71c5b7816b93069b590924a44bd87375a4458a0c8c3de7333d968840

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.601466Z digest=sha256:c84d8ecc91dc0ab1404e7ef9faec1f928a1841126d682deac9a69133a2058ad9

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.612863Z digest=sha256:df481fd3e7fb0fcfe11597c26f8a6bbe5606a2da52c1773263d4ea8a191b9f7b

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 2026-08-14T06:32:32.682623+00:00.

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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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.622833Z digest=sha256:94d9db8bdda20f970d6d328059d985963cd6c7cf1cf9b95ac56cf7bf6cc69542

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.627652Z digest=sha256:c5962316bb659a57aedb070a7b6b006eca5ca061303e85eb429ee2fd483fa786

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.632531Z digest=sha256:feb9a5e44976b4f805ce33acc0c7ca2e68eb20e56b459f1ed0fa94bc8731b44a

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.637293Z digest=sha256:3d9017217aae7a4d4a8e549fc0cfdfb6cba1805d684bc1dbbad40ca23ac35635

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.642038Z digest=sha256:064d61a5fa1c4ed21818ea7a8a6f3cf59b07e7290334900e8f5e73b0298acc53

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.646987Z digest=sha256:03714e14c3f2895522bef7ec2db348dd4335a47d9a77d208de69cc380918cdfe

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.656735Z digest=sha256:e958d47d818dd66c368e996fedbc1c2da7030bc8fcbdf11efc92c4ca31632f8a

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.661045Z digest=sha256:1ca84100db98b0eb7da13ce7e31b7700de204763683d0c61781b2188ac3ef3c6

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.665994Z digest=sha256:7f1aff4b6fab069e0eb6ac560b92605727d6727a8f2b7e5dc8f2c41f7366bd6a

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.670559Z digest=sha256:550f3db43ddb3993ee21e7c6d094ebed2a13557642790aa3e40e07d4cc2bbd41

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.675197Z digest=sha256:f17d0ed9a2d815ac5c0ba9ad3b669a91ab9e64f745cac5dc8a7ad37b5c5663aa

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.685378Z digest=sha256:9d565863264dac9013b08785c8d20bec75e4adaf89419ea0487cb1c135edfe32

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-11T21:55:11.690517Z digest=sha256:2672ec2cd6f8867041c19432532234c2e837c1366f10dce3b66cce332be8afc0

Pith citing papers

No inbound Pith citation observations are available.