Paper Citation Record · LEDGER

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses

As of 23 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2411.19747.

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

pith.paper-citation-record.v1
2411.19747 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:56:36.250832Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 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

36 of 36 outbound references displayed

verified exact3
verified fuzzy27
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ee8c2500-cda4-4c95-abf1-2c3715ecedfd · outbound

This paper cites Wayformer: Motion forecasting via simple & efficient attention networks,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Wayformer: Motion forecasting via simple & efficient attention networks,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.923830Z

Source-reported events for the cited work

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

Observation 33205dfb-c967-4bb2-950d-c2fd83966467 · outbound

This paper cites Safety-aware motion prediction with unseen vehicles for autonomous driving,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Safety-aware motion prediction with unseen vehicles for autonomous driving,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.909414Z

Source-reported events for the cited work

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

Observation 7346f8e7-cb75-4612-a4bd-8784255d6aca · outbound

This paper cites Prank: motion prediction based on ranking,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Prank: motion prediction based on ranking,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.894885Z

Source-reported events for the cited work

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

Observation b5fcdf1f-fc2a-47fd-928e-3570ab38aeed · outbound

This paper cites Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.880630Z

Source-reported events for the cited work

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

Observation 09708fe1-227a-4b41-b3a2-f02d24b8cb58 · outbound

This paper cites Rules of the road: Predicting driving behavior with a convolutional model of semantic interactions,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Rules of the road: Predicting driving behavior with a convolutional model of semantic interactions,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.866184Z

Source-reported events for the cited work

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

Observation 8e4fc924-4e36-4663-8119-5f8417c81b5c · outbound

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

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Intentnet: Learning to predict intention from raw sensor data,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.851489Z

Source-reported events for the cited work

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

Observation 2a89cc88-436e-4f82-857a-bd487233e7e7 · outbound

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

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Learning lane graph representations for motion forecasting,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.834961Z

Source-reported events for the cited work

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

Observation 2cc1d751-56cf-4db6-a38a-907b825b88f6 · outbound

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

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Vectornet: Encoding hd maps and agent dynamics from vectorized representation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.820643Z

Source-reported events for the cited work

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

Observation ea9e3e9b-833b-4cbc-8ab2-54ac383f39e3 · outbound

This paper cites Learning heterogeneous interaction strengths by trajectory prediction with graph neural network,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Learning heterogeneous interaction strengths by trajectory prediction with graph neural network,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.807572Z

Source-reported events for the cited work

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

Observation c2a5f46a-0ac1-48ff-b2a6-6a0b8957ea46 · outbound

This paper cites Graph-based spatial transformer with memory replay for multi-future pedestrian trajectory prediction,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Graph-based spatial transformer with memory replay for multi-future pedestrian trajectory prediction,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.793477Z

Source-reported events for the cited work

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

Observation 2b4003f9-1034-4b1b-9ae0-7ff18a66d95f · outbound

This paper cites Diverse and admissible trajectory forecasting through multimodal context understanding,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Diverse and admissible trajectory forecasting through multimodal context understanding,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.779226Z

Source-reported events for the cited work

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

Observation a833771d-6bb8-4130-bfd9-4db689acd946 · outbound

This paper cites Goal-driven self-attentive recurrent networks for trajectory prediction,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Goal-driven self-attentive recurrent networks for trajectory prediction,

Reference 12

Resolution: verified exact; raw_fallback, observed 2026-08-12T05:56:36.483905Z

Source-reported events for the cited work

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

Observation 02c924f5-863f-49a7-a7db-9955b1f3d6d2 · outbound

This paper cites Vehicle trajectory prediction using LSTMs with spatial-temporal attention mechanisms,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Vehicle trajectory prediction using LSTMs with spatial-temporal attention mechanisms,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T05:56:36.146719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e3b5add-494b-4c9e-b777-fb39b6e32730 · outbound

This paper cites Vehicle trajectory prediction based on lstm recurrent neural networks,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Vehicle trajectory prediction based on lstm recurrent neural networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.755722Z

Source-reported events for the cited work

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

Observation e8fb461d-0c8d-4c0f-99bc-127a69650ba0 · outbound

This paper cites Latent variable sequential set transformers for joint multi-agent motion prediction,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Latent variable sequential set transformers for joint multi-agent motion prediction,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.740823Z

Source-reported events for the cited work

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

Observation 2da872bc-4d13-4adf-b9e3-9a0cc2f0a4f3 · outbound

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

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Laformer: Trajectory prediction for autonomous driving with lane-aware scene constraints,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T05:56:36.159521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0644effa-10e9-463d-9982-00000d2ec2c9 · outbound

This paper cites Motion transformer with global intention localization and local movement refinement,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Motion transformer with global intention localization and local movement refinement,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.716668Z

Source-reported events for the cited work

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

Observation 01ed562a-74cf-4bdb-a2e9-6a211298bb50 · outbound

This paper cites Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.702411Z

Source-reported events for the cited work

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

Observation e049c086-1f10-4c13-8142-3ba21c8e47ea · outbound

This paper cites Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.686826Z

Source-reported events for the cited work

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

Observation 282ce2dc-cf39-442e-b542-2c41aa74018e · outbound

This paper cites Gohome: Graph-oriented heatmap output for future motion estimation,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Gohome: Graph-oriented heatmap output for future motion estimation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.672447Z

Source-reported events for the cited work

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

Observation 70e25614-18db-4155-b31f-b06fecb973d0 · outbound

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

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Densetnt: End-to-end trajectory pre- diction from dense goal sets,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T05:56:36.182094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15fefeb9-4af0-41c2-9a63-33a58e6120bc · outbound

This paper cites Ltp: Lane-based trajectory prediction for autonomous driving,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Ltp: Lane-based trajectory prediction for autonomous driving,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.650130Z

Source-reported events for the cited work

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

Observation ef5a58f4-4e50-4f81-9b9f-9fec3c366477 · outbound

This paper cites Diverse multiple trajectory prediction using a two-stage prediction network trained with lane loss,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Diverse multiple trajectory prediction using a two-stage prediction network trained with lane loss,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.637178Z

Source-reported events for the cited work

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

Observation 3507947a-a862-4e80-8009-70170f83685c · outbound

This paper cites Scene compliant trajectory forecast with agent-centric spatio-temporal grids,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Scene compliant trajectory forecast with agent-centric spatio-temporal grids,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.622396Z

Source-reported events for the cited work

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

Observation 46949ae5-011a-4e1b-878e-6d972d7c906d · outbound

This paper cites Improving movement prediction of traffic actors using off-road loss and bias mitigation,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Improving movement prediction of traffic actors using off-road loss and bias mitigation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.606322Z

Source-reported events for the cited work

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

Observation bbd29735-ce7f-419a-a521-8726786af726 · outbound

This paper cites Motion Prediction using Trajectory Sets and Self-Driving Domain Knowledge.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Motion Prediction using Trajectory Sets and Self-Driving Domain Knowledge

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-12T05:56:36.327849Z

Source-reported events for the cited work

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

Observation 7e7d07d7-ad11-422b-bba1-fa4dcb6d0396 · outbound

This paper cites Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-12T05:56:36.307954Z

Source-reported events for the cited work

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

Observation e352ef65-c563-4e17-865f-fe93b1db68fd · outbound

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

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Argoverse: 3d tracking and forecasting with rich maps,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T05:56:36.213741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5be24774-e289-4bad-a3fb-81e84358b525 · outbound

This paper cites Ellipse loss for scene-compliant motion prediction,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Ellipse loss for scene-compliant motion prediction,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.581497Z

Source-reported events for the cited work

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

Observation 52758081-f3eb-4d98-9a42-365e5107dd61 · outbound

This paper cites Trajectory prediction in au- tonomous driving with a lane heading auxiliary loss,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Trajectory prediction in au- tonomous driving with a lane heading auxiliary loss,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.565839Z

Source-reported events for the cited work

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

Observation 34f0ff45-6aff-491c-8ffd-7c555a67c7f7 · outbound

This paper cites The importance of prior knowledge in precise multimodal prediction,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses The importance of prior knowledge in precise multimodal prediction,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.550482Z

Source-reported events for the cited work

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

Observation abe6997e-df92-4719-a3d9-32b95926be61 · outbound

This paper cites O’Rourke, Computational Geometry in C , 2nd ed.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses O’Rourke, Computational Geometry in C , 2nd ed

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.537078Z

Source-reported events for the cited work

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

Observation 024acede-cc9f-4eac-a534-aa2ac6d96956 · outbound

This paper cites Vehicle trajectory prediction works, but not everywhere,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Vehicle trajectory prediction works, but not everywhere,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.523713Z

Source-reported events for the cited work

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

Observation 4685ecd6-fba1-489d-8b75-64f7cebe634e · outbound

This paper cites UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T05:56:36.241566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2aafaeed-d89c-4cbd-9c72-cd515fdfe486 · outbound

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

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses nuscenes: A multimodal dataset for autonomous driving,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:56:36.509173Z

Source-reported events for the cited work

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

Observation 73d5a8c2-5b83-4aba-9afb-f2430ea11e7a · outbound

This paper cites Argoverse 2: Next generation datasets for self-driving perception and forecasting,.

A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses Argoverse 2: Next generation datasets for self-driving perception and forecasting,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T05:56:36.250832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.