Paper Citation Record · LEDGER

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics

As of 7 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2506.08963.

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

pith.paper-citation-record.v1
2506.08963 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:04:04.373868Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

18 of 18 outbound references displayed

verified exact4
verified fuzzy1
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0999e1b9-85b8-436c-80f3-ae204eb601aa · outbound

This paper cites Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions.

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:03.293571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df87abab-2161-472b-91db-e287eac3154f · outbound

This paper cites Workingpaper, Aston University (1994).

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics Workingpaper, Aston University (1994)

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:03.354114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2acd51e4-fd51-4eb4-a167-3f44fa8e47ae · outbound

This paper cites In: 2024 IEEE International Conference on Robotics and Automation (ICRA).

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics In: 2024 IEEE International Conference on Robotics and Automation (ICRA)

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:03.408381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6920e58e-5735-449e-b9ef-1e2362e19069 · outbound

This paper cites GI_Forum – Journal of Geographic Information Sci- ence7(1), 54–68 (2019).

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics GI_Forum – Journal of Geographic Information Sci- ence7(1), 54–68 (2019)

Reference 4

Resolution: verified exact; doi, observed 2026-08-07T05:04:04.487691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8d782658-afb3-4b74-94a7-753ca67380a0 · outbound

This paper cites Array10, 100057 (2021), https://api.semanticscholar.org/CorpusID:233562996.

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics Array10, 100057 (2021), https://api.semanticscholar.org/CorpusID:233562996

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:04:05.702531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b4439b21-1687-4609-80ef-a37da0d28c71 · outbound

This paper cites A survey on robustness in trajectory prediction for autonomous vehicles.

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics A survey on robustness in trajectory prediction for autonomous vehicles

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:04:05.583651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9a78277b-61fb-42ff-9e70-4d8ce267e28a · outbound

This paper cites Neural Com- put.9(8), 1735–1780 (Nov 1997).

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics Neural Com- put.9(8), 1735–1780 (Nov 1997)

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:03.689755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cfd15d5-2799-43c7-ab6e-4e4a8852f401 · outbound

This paper cites Vision-based Multi-future Trajectory Prediction: A Survey.

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics Vision-based Multi-future Trajectory Prediction: A Survey

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:03.800757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26768b0a-feac-48b4-adf6-613fe9d6366d · outbound

This paper cites IEEE Transactions on Intelligent Ve- hicles7(3), 652–674 (2022).

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics IEEE Transactions on Intelligent Ve- hicles7(3), 652–674 (2022)

Reference 10

Resolution: metadata mismatch; raw_fallback, observed 2026-08-07T05:04:05.539720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1d14b1f2-8041-42e7-92ac-311b1939011f · outbound

This paper cites The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs.

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics The Trajectron: Probabilistic Multi-Agent Trajectory Modeling With Dynamic Spatiotemporal Graphs

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:03.905438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c65b5708-4687-4dc7-83fa-06df0facceb4 · outbound

This paper cites Structural-RNN: Deep Learning on Spatio-Temporal Graphs.

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics Structural-RNN: Deep Learning on Spatio-Temporal Graphs

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:04:05.354004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7f1806cd-f41d-43d0-b325-3f6d3c7cd179 · outbound

This paper cites Conditional Variational Autoencoder for Neural Machine Translation.

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics Conditional Variational Autoencoder for Neural Machine Translation

Reference 13

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T05:04:05.143219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4529f64f-5632-40b0-a4aa-d3fd035e5b2d · outbound

This paper cites Pamukkale University Journal of Engineering Sciences27(06 2020).

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics Pamukkale University Journal of Engineering Sciences27(06 2020)

Reference 14

Resolution: verified exact; raw_fallback, observed 2026-08-07T05:04:04.933316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d27e13d3-86c1-47b6-9b03-fffd6d0cc9f7 · outbound

This paper cites Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data.

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:04.245972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2a8de4a-f2e4-4b62-b642-db4379f76595 · outbound

This paper cites In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC).

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:04.292887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7af13e64-dce0-45bb-ba93-6e8979033492 · outbound

This paper cites In: 2024 IEEE Intelligent Vehicles Symposium (IV).

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics In: 2024 IEEE Intelligent Vehicles Symposium (IV)

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:04.322506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1209f8a6-8999-4e3d-a08b-5f87d9cee973 · outbound

This paper cites TNT: Target-driveN Trajectory Prediction.

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics TNT: Target-driveN Trajectory Prediction

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:04.354061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24bcda46-8c08-41d4-958b-cfaafd0d32f9 · outbound

This paper cites InfoVAE: Information Maximizing Variational Autoencoders.

Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics InfoVAE: Information Maximizing Variational Autoencoders

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:04.373868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.