Paper Citation Record · LEDGER

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback

As of 19 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2504.13785.

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

pith.paper-citation-record.v1
2504.13785 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:06:02.206065Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:27:29.806038Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T04:27:30.898741Z

Reference resolution

51 of 51 outbound references displayed

verified exact1
verified fuzzy20
unresolved30
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8924ad95-9970-4d98-a6ef-af1c0b3ea85d · outbound

This paper cites A survey on trajectory-prediction methods for autonomous driving,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback A survey on trajectory-prediction methods for autonomous driving,

Reference 1

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Observation 49f99cca-1593-4161-9c40-7376c1edd184 · outbound

This paper cites Tra- jectron++: Dynamically-feasible trajectory forecasting with heteroge- neous data,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Tra- jectron++: Dynamically-feasible trajectory forecasting with heteroge- neous data,

Reference 2

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Observation 7c8cc005-46a9-4d51-b4ca-1133c9b01477 · outbound

This paper cites Bat: Behavior-aware human-like trajectory prediction for autonomous driv- ing,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Bat: Behavior-aware human-like trajectory prediction for autonomous driv- ing,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.698057Z

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Observation ea7ba515-c82b-47e4-8452-f90ef018c698 · outbound

This paper cites Desire: Distant future prediction in dynamic scenes with in- teracting agents,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Desire: Distant future prediction in dynamic scenes with in- teracting agents,

Reference 4

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Source-reported events for the cited work

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Observation fd601770-d4f7-427c-9d43-db25aff01451 · outbound

This paper cites Hivt: Hierarchical vector transformer for multi-agent motion prediction,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Hivt: Hierarchical vector transformer for multi-agent motion prediction,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T12:06:02.052176Z

Source-reported events for the cited work

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Observation 5ff274ff-639e-4dc6-a67f-24b5891246c6 · outbound

This paper cites Laformer: Trajectory prediction for autonomous driving with lane-aware scene constraints,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Laformer: Trajectory prediction for autonomous driving with lane-aware scene constraints,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T12:06:02.056416Z

Source-reported events for the cited work

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Observation ebd03505-d03b-404d-93a1-1062f36b7da8 · outbound

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

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback nuscenes: A multimodal dataset for autonomous driving,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.673552Z

Source-reported events for the cited work

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Observation 1915c352-3918-4842-9f92-4330cf925a82 · outbound

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

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Argoverse: 3d tracking and forecasting with rich maps,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.664549Z

Source-reported events for the cited work

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Observation b143fa30-68a9-49bb-9a16-577d5e59e463 · outbound

This paper cites Densetnt: End-to-end trajectory pre- diction from dense goal sets,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Densetnt: End-to-end trajectory pre- diction from dense goal sets,

Reference 9

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Source-reported events for the cited work

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Observation 85b8e2b8-60e2-4495-9297-677c94c7c744 · outbound

This paper cites Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Leveraging Future Relationship Reasoning for Vehicle Trajectory Prediction

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T12:06:02.070090Z

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Observation a767523f-550e-42a0-8a7b-d1015c6494ab · outbound

This paper cites Stay on track: A frenet wrapper to overcome off-road trajectories in vehicle motion prediction,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Stay on track: A frenet wrapper to overcome off-road trajectories in vehicle motion prediction,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.650981Z

Source-reported events for the cited work

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Observation a6934c7e-2443-4945-a281-152c03394a33 · outbound

This paper cites Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Prediction.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Prediction

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T12:06:02.076731Z

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Observation 8c22df52-02e0-4d2f-b358-d0b269d754ec · outbound

This paper cites Pioneering se (2)- equivariant trajectory planning for automated driving,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Pioneering se (2)- equivariant trajectory planning for automated driving,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T12:06:02.080056Z

Source-reported events for the cited work

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Observation 05f59851-978e-4f47-aaf8-422bdf423559 · outbound

This paper cites Vectornet: Encoding hd maps and agent dynamics from vectorized representation,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Vectornet: Encoding hd maps and agent dynamics from vectorized representation,

Reference 14

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Source-reported events for the cited work

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Observation 75ae4c76-56b5-4a6d-be9f-d0fc9f20e73a · outbound

This paper cites Learning lane graph representations for motion forecasting,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Learning lane graph representations for motion forecasting,

Reference 15

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Observation b007c93d-98ed-4c81-b9f9-21af797aa872 · outbound

This paper cites Towards consistent and explainable motion prediction using heterogeneous graph attention,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Towards consistent and explainable motion prediction using heterogeneous graph attention,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.626874Z

Source-reported events for the cited work

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Observation f5b8389a-589b-4d66-a3a1-2df79b835a53 · outbound

This paper cites Multimodal motion prediction with stacked transformers,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Multimodal motion prediction with stacked transformers,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.617956Z

Source-reported events for the cited work

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Observation e746a72c-9b27-45ce-9c0e-897eeebe6293 · outbound

This paper cites Transfer learning study of motion transformer-based trajectory predictions,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Transfer learning study of motion transformer-based trajectory predictions,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.608716Z

Source-reported events for the cited work

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Observation 0164f954-d0e1-4f66-bf4a-1ea58279ef42 · outbound

This paper cites Multimodal trajectory predictions for autonomous driving using deep convolutional networks,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Multimodal trajectory predictions for autonomous driving using deep convolutional networks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.598970Z

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Observation c5a9fa0d-2640-455c-93e4-c398a8545229 · outbound

This paper cites Intentnet: Learning to predict intention from raw sensor data,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Intentnet: Learning to predict intention from raw sensor data,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.589828Z

Source-reported events for the cited work

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Observation eaaf4283-a378-4e3f-a943-73eb59e8d4e7 · outbound

This paper cites Covernet: Multimodal behavior prediction using trajectory sets,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Covernet: Multimodal behavior prediction using trajectory sets,

Reference 21

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Source-reported events for the cited work

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Observation 48348f6e-5b49-4832-afd9-8cb0d596cf35 · outbound

This paper cites Agentformer: Agent- aware transformers for socio-temporal multi-agent forecasting,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Agentformer: Agent- aware transformers for socio-temporal multi-agent forecasting,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.570814Z

Source-reported events for the cited work

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Observation 4883c175-cc1e-4155-baa0-e16dd5c5e96d · outbound

This paper cites Trajeglish: Traffic modeling as next-token prediction,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Trajeglish: Traffic modeling as next-token prediction,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.560039Z

Source-reported events for the cited work

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Observation 9325dd18-0d58-4088-bbcc-c737829d903a · outbound

This paper cites Mastering the game of go without human knowledge,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Mastering the game of go without human knowledge,

Reference 24

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Observation f11eebdb-85cd-43a1-b276-588dce980bd5 · outbound

This paper cites V-jepa: Latent video prediction for visual representation learning,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback V-jepa: Latent video prediction for visual representation learning,

Reference 25

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Observation 7b5e9c16-b0ac-4ae5-b037-9702b09565d1 · outbound

This paper cites Robust Autonomy Emerges from Self-Play.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Robust Autonomy Emerges from Self-Play

Reference 26

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Observation 1a1684fb-e510-4c64-88ad-b5f03c62e23e · outbound

This paper cites NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

Reference 27

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Observation 077ee7c6-4f65-4cd2-a2c7-5faaf7ea82e8 · outbound

This paper cites Waymax: An accelerated, data- driven simulator for large-scale autonomous driving research,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Waymax: An accelerated, data- driven simulator for large-scale autonomous driving research,

Reference 28

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Observation 79db766e-07c8-4619-bf4d-9e9fb9755f2e · outbound

This paper cites DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving

Reference 29

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Observation d72a4be2-2c2b-4a98-8205-1ff572f7e0fc · outbound

This paper cites Deepdriving: Learning affordance for direct perception in autonomous driving,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Deepdriving: Learning affordance for direct perception in autonomous driving,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.437053Z

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Observation 07158162-d606-4d82-93cd-4c83fd5d226a · outbound

This paper cites End to End Learning for Self-Driving Cars.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback End to End Learning for Self-Driving Cars

Reference 31

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Observation 738c92ea-a497-486b-9056-9fc53ef472a5 · outbound

This paper cites Exploring the limitations of behavior cloning for autonomous driving,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Exploring the limitations of behavior cloning for autonomous driving,

Reference 32

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Observation 618f4305-7aaa-4ef8-80a6-62a56dd29b04 · outbound

This paper cites A reduction of imitation learning and structured prediction to no-regret online learning,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback A reduction of imitation learning and structured prediction to no-regret online learning,

Reference 33

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Observation 66a8a72a-a021-4b2d-8960-c7130998535e · outbound

This paper cites End-to-end driving via conditional imitation learning,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback End-to-end driving via conditional imitation learning,

Reference 34

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Observation 14d09fd9-3d4c-4594-aaee-f6476e4d7c19 · outbound

This paper cites Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes

Reference 35

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Observation b794a14f-ae2e-4cfe-93f7-62526f1bd838 · outbound

This paper cites The integration of prediction and planning in deep learning automated driving systems: A review,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback The integration of prediction and planning in deep learning automated driving systems: A review,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.412301Z

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Observation e15e1551-decc-4974-a586-36c341ea9b21 · outbound

This paper cites Motion forecasting in continuous driving,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Motion forecasting in continuous driving,

Reference 37

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Source-reported events for the cited work

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Observation 5a57b9c6-0f0b-4ce0-b63c-6d0ed067296a · outbound

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

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Motr: End-to-end multiple-object tracking with transformer,

Reference 38

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Observation 0aa4e8c9-2163-4a9f-87bf-37312afdda51 · outbound

This paper cites Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models

Reference 39

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Observation df632d15-00d5-4bc4-9307-089f77115d4c · outbound

This paper cites Conditional Unscented Autoencoders for Trajectory Prediction.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Conditional Unscented Autoencoders for Trajectory Prediction

Reference 40

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Observation 0df93a56-32ff-45c6-8007-cba8753ae870 · outbound

This paper cites Deep Kalman Filters.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Deep Kalman Filters

Reference 41

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This paper cites Deep radar sensor models for accurate and robust object tracking,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Deep radar sensor models for accurate and robust object tracking,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.388170Z

Source-reported events for the cited work

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Observation 0449a827-5ddb-43e9-a923-e64550fd4feb · outbound

This paper cites Attention is all you need,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Attention is all you need,

Reference 43

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Source-reported events for the cited work

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Observation 04294bcc-9642-4454-92f0-599b7a1ae969 · outbound

This paper cites Is ego status all you need for open-loop end-to-end autonomous driving?.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Is ego status all you need for open-loop end-to-end autonomous driving?

Reference 44

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Observation 932c4c17-e06d-4394-a78d-32c3ff8058aa · outbound

This paper cites Lapred: Lane-aware prediction of multi-modal future trajectories of dynamic agents,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Lapred: Lane-aware prediction of multi-modal future trajectories of dynamic agents,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.368972Z

Source-reported events for the cited work

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Observation edaf25f3-55b8-4e7e-acd0-3f414f0485c7 · outbound

This paper cites THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-16T12:06:02.189124Z

Source-reported events for the cited work

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Observation 1b9a23fc-9c93-418b-a3d5-eb40a1d3ff26 · outbound

This paper cites Multimodal trajectory prediction conditioned on lane-graph traversals,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Multimodal trajectory prediction conditioned on lane-graph traversals,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.359532Z

Source-reported events for the cited work

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Observation 83493ab4-49bc-4661-a5ce-0f2824e90b4c · outbound

This paper cites Q-eanet: Implicit social modeling for trajectory prediction via experience-anchored queries,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Q-eanet: Implicit social modeling for trajectory prediction via experience-anchored queries,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.348554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f8489e92-150a-4169-9b6f-82f566ac7278 · outbound

This paper cites Goal-lbp: Goal-based local behavior guided trajectory prediction for autonomous driving,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Goal-lbp: Goal-based local behavior guided trajectory prediction for autonomous driving,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:06:02.339324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0a232919-c0c2-4394-bbc1-a0081177195f · outbound

This paper cites Tnt: Target-driven trajectory prediction,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Tnt: Target-driven trajectory prediction,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T12:06:02.203025Z

Source-reported events for the cited work

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Observation 812a2d1e-60b7-4814-a612-f9afb9510421 · outbound

This paper cites Tpcn: Temporal point cloud networks for motion forecasting,.

Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback Tpcn: Temporal point cloud networks for motion forecasting,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-16T12:06:02.206065Z

Source-reported events for the cited work

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Pith citing papers

Observation 8b5c315c-d480-435d-84ac-a50798c6409d · inbound

DoublyAware: Dual Planning and Policy Awareness for Temporal Difference Learning in Humanoid Locomotion cites this paper.

DoublyAware: Dual Planning and Policy Awareness for Temporal Difference Learning in Humanoid Locomotion Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:27:30.909236Z

Source-reported events for the cited work

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