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Paper Citation Record · LEDGER

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning

As of 18 August 2026, this Paper Citation Record lists 100 of 103 outbound references and 0 inbound Pith citation observations for arXiv:2507.04790.

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

pith.paper-citation-record.v1
2507.04790 v3

Coverage vector

measured 100 of 103 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:44:59.679839Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

100 of 103 outbound references displayed

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  • verified fuzzy54
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 22254273-7138-4b14-a30a-9e75b813f8e1 · outbound

This paper cites Path planning of mobile robot with improved ant colony algo- rithm and mdp to produce smooth trajectory in grid-based environment.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Path planning of mobile robot with improved ant colony algo- rithm and mdp to produce smooth trajectory in grid-based environment

Reference 1

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Observation 84f1558b-b87d-4bcc-8477-cacbbe0c1c95 · outbound

This paper cites Ensemble of averages: Improving model selection and boosting performance in domain generalization.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Ensemble of averages: Improving model selection and boosting performance in domain generalization

Reference 2

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Observation c0c5db95-195c-4ddd-b038-d84f612f855d · outbound

This paper cites Use of relaxation methods in sampling-based algorithms for optimal mo- tion planning.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Use of relaxation methods in sampling-based algorithms for optimal mo- tion planning

Reference 3

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Observation e7ffbbe4-4d8e-4ec5-a27c-a729977b5e40 · outbound

This paper cites Sit dataset: socially in- teractive pedestrian trajectory dataset for social navigation robots.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Sit dataset: socially in- teractive pedestrian trajectory dataset for social navigation robots

Reference 4

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Observation b853d6a2-f16f-4836-9fd5-aa7887548808 · outbound

This paper cites Grid-based motion planning us- ing advanced motions for hexapod robots.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Grid-based motion planning us- ing advanced motions for hexapod robots

Reference 5

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Observation ed061792-30a4-491c-a51e-9afd1b7796c7 · outbound

This paper cites Crowd-robot interaction: Crowd-aware robot navi- gation with attention-based deep reinforcement learning.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Crowd-robot interaction: Crowd-aware robot navi- gation with attention-based deep reinforcement learning

Reference 6

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Observation b2474b9b-dc4f-4843-8d74-07ec5a5e35db · outbound

This paper cites End-to-end autonomous driving: Challenges and frontiers.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning End-to-end autonomous driving: Challenges and frontiers

Reference 7

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Observation 3e1154e8-7732-488d-8cec-ce8feb9c487a · outbound

This paper cites Ppad: Iterative interactions of prediction and planning for end-to-end autonomous driving.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Ppad: Iterative interactions of prediction and planning for end-to-end autonomous driving

Reference 8

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Observation e69bf807-7e42-41eb-8698-96659e25b24c · outbound

This paper cites Forecast-mae: Self-supervised pre-training for motion forecasting with masked autoencoders.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Forecast-mae: Self-supervised pre-training for motion forecasting with masked autoencoders

Reference 9

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Observation 65c13c57-c054-49a5-96e5-bbb535b3b57c · outbound

This paper cites Fusing finetuned models for better pretraining.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Fusing finetuned models for better pretraining

Reference 10

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Observation b1b68358-84c6-4868-a935-fe53f00ae034 · outbound

This paper cites Adaptive Stochastic Weight Averaging.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Adaptive Stochastic Weight Averaging

Reference 11

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Observation 162ec8f4-6022-4050-8bc8-07ac287c304e · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Imagenet: A large-scale hierarchical image database

Reference 12

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Observation de6c37ac-757e-4318-93cc-38925bd0dd66 · outbound

This paper cites Sparse instance conditioned multimodal trajectory predic- tion.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Sparse instance conditioned multimodal trajectory predic- tion

Reference 13

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Observation 6c6e3c9e-f23b-4b0b-bc34-fba94de1a927 · outbound

This paper cites Recurrent Aligned Network for Generalized Pedestrian Trajectory Prediction.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Recurrent Aligned Network for Generalized Pedestrian Trajectory Prediction

Reference 14

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Source-reported events for the cited work

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Observation 8227b7e8-fc01-4b9e-94ad-a2378997a6a5 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 15

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Observation d54c0dd5-fee1-4ab7-bdc7-b1ee4bb3f87e · outbound

This paper cites Unitraj: A unified framework for scalable vehicle trajectory prediction.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Unitraj: A unified framework for scalable vehicle trajectory prediction

Reference 16

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Observation f2bca4bf-9d70-45aa-850d-b50f7cc0fd1c · outbound

This paper cites Uncertainty estimation for Cross-dataset performance in Trajectory prediction.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Uncertainty estimation for Cross-dataset performance in Trajectory prediction

Reference 17

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Observation 796cdedb-a635-4ee8-a706-e3000d38043d · outbound

This paper cites Planning-oriented autonomous driving.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Planning-oriented autonomous driving

Reference 18

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Observation 7e2742be-0e1a-4dd4-a8c1-e4f50a454ce4 · outbound

This paper cites Emr-merging: Tuning-free high- performance model merging.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Emr-merging: Tuning-free high- performance model merging

Reference 19

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Observation ad2ed8bb-9a1d-4c94-bd58-555ccf049396 · outbound

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

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving

Reference 20

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Observation 6040f982-b363-468a-986d-2bb5f8dba8cd · outbound

This paper cites Dif- ferentiable integrated motion prediction and planning with learnable cost function for autonomous driving.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Dif- ferentiable integrated motion prediction and planning with learnable cost function for autonomous driving

Reference 21

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Observation f42efff0-6aee-40a1-9053-e9e662001ead · outbound

This paper cites Dtpp: Differentiable joint conditional prediction and cost evaluation for tree policy planning in autonomous driving.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Dtpp: Differentiable joint conditional prediction and cost evaluation for tree policy planning in autonomous driving

Reference 22

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Observation 28bfbd23-4812-4cd8-af38-55fcb1824e7d · outbound

This paper cites Editing Models with Task Arithmetic.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Editing Models with Task Arithmetic

Reference 23

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Observation b3fcb0d2-aeaf-4ecf-97d4-56f08c32da29 · outbound

This paper cites Multi-agent long-term 3d human pose forecasting via interaction-aware trajectory conditioning.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Multi-agent long-term 3d human pose forecasting via interaction-aware trajectory conditioning

Reference 24

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Observation d7bad1c7-eebd-41bd-8e8c-d137c56c8c4b · outbound

This paper cites Multi-modal knowledge distillation-based 9 human trajectory forecasting.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Multi-modal knowledge distillation-based 9 human trajectory forecasting

Reference 25

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Observation 7503ca64-9ec1-4764-afc6-beea26e1a68f · outbound

This paper cites Quantifying task pri- ority for multi-task optimization.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Quantifying task pri- ority for multi-task optimization

Reference 26

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Observation 6923c0e1-325e-4711-98c3-671272a21071 · outbound

This paper cites Selective Task Group Updates for Multi-Task Optimization.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Selective Task Group Updates for Multi-Task Optimization

Reference 27

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Observation f2a6c757-563c-45eb-83cb-05bbbb99d38c · outbound

This paper cites Think twice be- fore driving: Towards scalable decoders for end-to-end au- tonomous driving.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Think twice be- fore driving: Towards scalable decoders for end-to-end au- tonomous driving

Reference 28

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Observation 1b233514-a5bf-427a-97a5-a166ee17915c · outbound

This paper cites Vad: Vectorized scene representa- tion for efficient autonomous driving.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Vad: Vectorized scene representa- tion for efficient autonomous driving

Reference 29

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Observation 75ff8316-8028-4dc3-b38a-ede78f0a2170 · outbound

This paper cites Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task Arithmetic.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task Arithmetic

Reference 30

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Observation df415795-e354-4d9a-9311-f0599979302e · outbound

This paper cites Sampling-based algo- rithms for optimal motion planning.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Sampling-based algo- rithms for optimal motion planning

Reference 31

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Observation 396de47f-0180-4a7a-91e0-5f85bf4ece62 · outbound

This paper cites Probabilistic roadmaps for path planning in high- dimensional configuration spaces.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Probabilistic roadmaps for path planning in high- dimensional configuration spaces

Reference 32

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Observation 8ef6986b-9cbc-432d-af83-1abab8bfe582 · outbound

This paper cites A game-theoretic framework for joint forecasting and planning.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning A game-theoretic framework for joint forecasting and planning

Reference 33

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation c093b9eb-585c-4d0c-9b38-12a92cb42ac5 · outbound

This paper cites Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics

Reference 34

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Observation 0a832340-9739-4fc7-840b-9f68e59b88de · outbound

This paper cites Overcoming catastrophic forgetting in neu- ral networks.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Overcoming catastrophic forgetting in neu- ral networks

Reference 35

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Observation a1b6d0ce-fe8b-492f-88b7-7d506bcea290 · outbound

This paper cites Rrt-connect: An efficient approach to single-query path planning.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Rrt-connect: An efficient approach to single-query path planning

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.459343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:53.485173Z digest=sha256:ec6436ef21fd4b0c74dd0b87b8b268a4413e3fecfe3c3aef9a255439c7d6d374

Observation 2b2b7ef9-a580-4c52-9620-60848f25b340 · outbound

This paper cites Crowds by example.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Crowds by example

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.453395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:53.566553Z digest=sha256:a32e3618b69e7296ad49338bc05ff1a119ab2fa500bbb400f1eda54eca5b7f15

Observation 49c150be-e217-4894-bcdb-59235451eba5 · outbound

This paper cites An ensemble learning frame- work for vehicle trajectory prediction in interactive scenar- ios.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning An ensemble learning frame- work for vehicle trajectory prediction in interactive scenar- ios

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.447390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:53.647201Z digest=sha256:d8dc30d069b24622c7cc0ded39bdc36921bad5a1491cce88b58edc415aab6e76

Observation bc9cc9b6-b11e-4723-b431-9fac4350e93a · outbound

This paper cites Conflict-averse gradient descent for multi-task learn- ing.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Conflict-averse gradient descent for multi-task learn- ing

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.441029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:53.699418Z digest=sha256:fa5430bad67b9f1d6e425f7d84f204ab298dc989cf10366ad94653f7874ed60e

Observation 575e799a-26c5-4391-888a-b3c8608cefba · outbound

This paper cites Famo: Fast adaptive multitask optimization.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Famo: Fast adaptive multitask optimization

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.434293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:53.785882Z digest=sha256:50c2f27397478ca6b7c5ad97ab3feaa057abfa5c6b0077eb846057e4b29d661c

Observation 5e1c3c10-fe84-4457-95f3-f2e5cef37998 · outbound

This paper cites Famo: Fast adaptive multitask optimization.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Famo: Fast adaptive multitask optimization

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.427551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:53.853797Z digest=sha256:5a0b6b5971c1086e29f25eaad0ae6b4fefc89457b9262798957ce62828d8ee3b

Observation 23c31a9e-10fb-4796-b45a-ea5c3666af9d · outbound

This paper cites Tangent Transformers for Composition, Privacy and Removal.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Tangent Transformers for Composition, Privacy and Removal

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-06T19:44:53.943463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:44:53.943463Z digest=sha256:1b3358f8ffbfa5917b797e21832ff1510f4c48be8a17bce1cb83c6d4ef93858d

Observation 706442db-7b68-4c98-8a87-d65cd2aae3a5 · outbound

This paper cites Jrdb: A dataset and bench- mark of egocentric robot visual perception of humans in built environments.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Jrdb: A dataset and bench- mark of egocentric robot visual perception of humans in built environments

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.421231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:54.012409Z digest=sha256:99212f11275547c515dd36ecebba95d298aacf29624a2e5ab14c0fc36ad13e6d

Observation 47ab21f5-bac1-4224-9633-5d66a264c4aa · outbound

This paper cites Merging models with fisher-weighted averaging.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Merging models with fisher-weighted averaging

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.414510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:54.086412Z digest=sha256:6c26e427fe33c94e8f48265da62b352401bbda65c2e91b6177d1ea09f1da11b3

Observation b74c2b27-86ee-4837-8382-7abd244cdc7d · outbound

This paper cites Multi-Task Learning as a Bargaining Game.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Multi-Task Learning as a Bargaining Game

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-06T19:44:54.197062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:44:54.197062Z digest=sha256:b44a129435c8f7418f42ad994e6e0b7370984ecc71ebf5394913b1e1fce0a36d

Observation 4d7f34a9-2d64-4ad4-a621-e9286ca75f22 · outbound

This paper cites Task arithmetic in the tangent space: Improved editing of pre-trained models.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Task arithmetic in the tangent space: Improved editing of pre-trained models

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.407739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:54.293666Z digest=sha256:294eb494de8932b5d8f05f04ae46ead947c2ac8883b31a46a03a6a5a10025df8

Observation 85621cec-6895-42e8-8137-7a4b4e3cf8c8 · outbound

This paper cites Vlp: Vision language planning for autonomous driving.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Vlp: Vision language planning for autonomous driving

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.401198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:54.382121Z digest=sha256:a4f562a70ebee11a4553da1d9852c49dc191939460f044299f73f56674056300

Observation 59667282-4773-4602-bfdb-2304e79c664d · outbound

This paper cites Leveraging future relation- ship reasoning for vehicle trajectory prediction.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Leveraging future relation- ship reasoning for vehicle trajectory prediction

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-06T19:44:54.505031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:44:54.505031Z digest=sha256:2913d787a8f3cf464313bb88a5c86a910112b48f990a64f2eef8a88635adb3f4

Observation ac2a0726-ac71-43f8-96fc-12e26f1a9e25 · outbound

This paper cites Improv- ing transferability for cross-domain trajectory prediction via neural stochastic differential equation.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Improv- ing transferability for cross-domain trajectory prediction via neural stochastic differential equation

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.389514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:54.583131Z digest=sha256:cf70588082e3570970f532bf9902d89e683b711ebfb8bb08339f548dbef74dfe

Observation 7e46c673-c7f9-4caf-a219-98769d76ea9a · outbound

This paper cites T4p: Test-time training of tra- jectory prediction via masked autoencoder and actor-specific token memory.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning T4p: Test-time training of tra- jectory prediction via masked autoencoder and actor-specific token memory

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.382279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:54.671953Z digest=sha256:fdae365d622c3dd35d939c5b43d0962af0a75aafef66f9e57e6fa32e20b334a2

Observation 0ffaab49-a7c8-4f4c-8088-9f8861fe75cb · outbound

This paper cites You’ll never walk alone: Modeling social behav- ior for multi-target tracking.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning You’ll never walk alone: Modeling social behav- ior for multi-target tracking

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.375270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:54.775019Z digest=sha256:b758ed375647d8680a4c0ad2fbdc08263f880768e41ca8376d21104a71c5335e

Observation 3cd67087-90b5-401f-972c-cf408939368e · outbound

This paper cites Adaptraj: A multi-source domain generalization framework for multi-agent trajectory prediction.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Adaptraj: A multi-source domain generalization framework for multi-agent trajectory prediction

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.368618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:54.874828Z digest=sha256:b809213f6c24edc185b5a1ce099885b929e301b3ebcffac64b470e4bba2271d3

Observation 45a47920-0990-4d06-b74b-f80f9901eed6 · outbound

This paper cites Th ¨or: Human-robot navigation data collection and accurate motion trajectories dataset.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Th ¨or: Human-robot navigation data collection and accurate motion trajectories dataset

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.361666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:54.959438Z digest=sha256:af2b9b01236a3323e0558bf3634d26950fed430932549ca097aef031402f4f34

Observation 4935f8bc-cf7f-4d91-b309-6e10ec37f8cb · outbound

This paper cites Perceive, predict, and plan: Safe motion planning through interpretable seman- tic representations.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Perceive, predict, and plan: Safe motion planning through interpretable seman- tic representations

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-06T19:44:55.063461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:44:55.063461Z digest=sha256:f0c5a47659195f9cada6cc0f58ba8171b4097ad459e723c88374ccbb2fd2f95f

Observation 7778e2b0-809e-4a4c-b1f3-7b1c17d7912e · outbound

This paper cites Navigation in human flows: planning with adaptive motion grid.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Navigation in human flows: planning with adaptive motion grid

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.351469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:55.153496Z digest=sha256:6532d86e3585d313f7cbe3f8c797bc85775853ed8a1c5e345ec402e114044756

Observation c3b3fdc9-9766-456d-8bbd-c375b470ad51 · outbound

This paper cites Progress & compress: A scalable framework for continual learning.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Progress & compress: A scalable framework for continual learning

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.344964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:55.241690Z digest=sha256:434ae2a23a3ef6c7af2007594e31388a3be92a7b64a8541a9a033c8fd1325d65

Observation 7e94edc4-adac-49cc-a599-eba994c99438 · outbound

This paper cites Independent component alignment for multi-task learning.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Independent component alignment for multi-task learning

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.338395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:55.331224Z digest=sha256:77810ec12851c1e58ec2521c79317a47a09f557f97152f2531012da1de34cce4

Observation f61e033f-cdf2-4170-9af4-ec9c854659ab · outbound

This paper cites Continual learning with deep generative replay.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Continual learning with deep generative replay

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-06T19:44:55.409435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:44:55.409435Z digest=sha256:d9690779aec5110ea3523a7db16036b45f8d10fb6e2141c5f83237d8d670383d

Observation 9ce0d22e-d3c2-4ae6-b3e9-4b7ab9d489ef · outbound

This paper cites Incremental learning of object detectors without catas- trophic forgetting.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Incremental learning of object detectors without catas- trophic forgetting

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.327527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:55.470973Z digest=sha256:ea1f721c067ae1955efbba44296279bd3d1b75076b665d2234e36ebbe1414fe7

Observation 7a2f7bd8-acc9-4bae-8764-521d4607639f · outbound

This paper cites Parameter Efficient Multi-task Model Fusion with Partial Linearization.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Parameter Efficient Multi-task Model Fusion with Partial Linearization

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-06T19:44:55.556343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:44:55.556343Z digest=sha256:1580a6b51e388d0dfb66047dbf5502aac07cf6d8509eb15fd0c4b274988dd00a

Observation 25908f3c-6d78-4b01-ad62-f8070256f987 · outbound

This paper cites Efficient evaluation of collisions and costs on grid maps for autonomous vehicle motion planning.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Efficient evaluation of collisions and costs on grid maps for autonomous vehicle motion planning

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.320685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:55.619039Z digest=sha256:c4cc6bd3640d04bc8a38e642af58e7e4134d2dc8ab65be36f04042be55df7c45

Observation 6e67abda-23ff-41f9-9bb9-6a368c1345a2 · outbound

This paper cites Dreamwalker: Mental planning for contin- uous vision-language navigation.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Dreamwalker: Mental planning for contin- uous vision-language navigation

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:07.050989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:55.715650Z digest=sha256:82ecb6c3453758fe11e70bbc20eb4bbc53ab60baa766fe860ed0366a36d7a612

Observation e9b6ae41-1d93-4c9d-8606-1eb7f0294435 · outbound

This paper cites Neural rrt*: Learning-based optimal path planning.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Neural rrt*: Learning-based optimal path planning

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:06.801933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:55.818925Z digest=sha256:c959c36e606e805120a6ad7f190359dc96cf1fd45238dba7436b79203e74b4c9

Observation 6da4c5ed-853c-4c30-bc59-8bdd7080be08 · outbound

This paper cites Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models

Reference 64

Resolution
verified exact
local_arxiv, observed 2026-08-06T19:45:00.402349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:55.891788Z digest=sha256:fb96e5ae574dbbeee8067daaf6fea4ff5d01f5e6ea16e192f59e64d579e3b805

Observation ef27d8f3-a947-4b67-80cd-cccadb5cc6ee · outbound

This paper cites Ganet: Goal area network for mo- tion forecasting.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Ganet: Goal area network for mo- tion forecasting

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:06.645717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:55.990920Z digest=sha256:228909f6c9b4f5693bea2e389e7a0183e2d9227482785274968405e01eea633a

Observation 7332b657-9548-40e9-b9a8-502ba42cbc86 · outbound

This paper cites Driving into the future: Multiview visual forecasting and planning with world model for au- tonomous driving.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Driving into the future: Multiview visual forecasting and planning with world model for au- tonomous driving

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:06.286766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:56.043920Z digest=sha256:d0a11498377fb37b21817c9b84b6c6e5265fafdb99340807f3596c302f74fced

Observation b84aca1f-b8cf-432b-acb1-56e83533f32b · outbound

This paper cites Bridging the gap: Improving domain generalization in trajectory prediction.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Bridging the gap: Improving domain generalization in trajectory prediction

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:06.053678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:56.129034Z digest=sha256:15f92002ebe6329d15cc92c53d88f7a46e03bbbf47230c0593fb098e2bfab5da

Observation 81d97651-c2a5-4582-8d68-e23beca73b30 · outbound

This paper cites Para-drive: Parallelized architecture for real- time autonomous driving.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Para-drive: Parallelized architecture for real- time autonomous driving

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:05.860797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:56.207885Z digest=sha256:27d811c0d4709edbcdd335574c6e31da877d46e87cca01ef3dd9403361e358b3

Observation 53ec0af4-c205-4ef5-94d8-9e1340c56289 · outbound

This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing in- ference time.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing in- ference time

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:05.621285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:56.328351Z digest=sha256:100bc2d2daa04d11791896d31020ec0fea4a39a1deeb08eb5de44e30b30c671f

Observation e5e0fd13-7d23-4598-8a6c-2a26e17ef89c · outbound

This paper cites Robust fine-tuning of zero-shot models.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Robust fine-tuning of zero-shot models

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:05.455527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:56.376129Z digest=sha256:a899e1097e90af0c0920c0c47687368513c943dd8871fadf057fb9e5a05a4f01

Observation e79b3101-e3c6-4953-ae4c-39b83db13c50 · outbound

This paper cites Adapting to length shift: Flexilength network for trajectory prediction.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Adapting to length shift: Flexilength network for trajectory prediction

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:05.255738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:56.436184Z digest=sha256:d2fcf2d197f9c9aa5e7f12355012f0743df1e0f2255f96789c00a7c1b911e6ac

Observation cd848fc3-dcc4-4606-af4d-2edd38691493 · outbound

This paper cites Adaptive trajectory prediction via transferable gnn.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Adaptive trajectory prediction via transferable gnn

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:04.985247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:56.522332Z digest=sha256:1865e4dddb65cfaa4c929db3950ddfca64b9daf89f26618d7d24d6b3cc693d68

Observation 31c7dddd-296c-4aad-a5f6-f77a4df685ca · outbound

This paper cites Training-free pretrained model merging.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Training-free pretrained model merging

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:04.686054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:56.559859Z digest=sha256:7629659c947cd1aadf0ed3843f39e253a7e852f5ddda6ba0947d612a8108b57e

Observation d4f764d2-f058-436e-ad41-da36f1c5cfb7 · outbound

This paper cites Ties-merging: Resolving interference 11 when merging models.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Ties-merging: Resolving interference 11 when merging models

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:04.340450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:56.660960Z digest=sha256:d11f2418fffb333a028521201384d713f06fc818920cc0b44712a535bee3f035

Observation ab97bdad-d396-4a79-b7f5-8fb47c01de1a · outbound

This paper cites Online learning for human classification in 3d lidar-based tracking.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Online learning for human classification in 3d lidar-based tracking

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:03.963198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:56.731442Z digest=sha256:6bb520211d7cba20c3ff1bd4b64720c87be9f68034ffaa4f78e414cb3c325347

Observation 46420070-056d-4272-b7af-e666d843fe8a · outbound

This paper cites Diffusion-es: Gradient-free planning with diffusion for autonomous and instruction-guided driving.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Diffusion-es: Gradient-free planning with diffusion for autonomous and instruction-guided driving

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:03.708183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:56.806867Z digest=sha256:29da2e90a479ff33955b7236fcf6e98a06960f4fc9bb520f8912948c5e6319b0

Observation edc248ea-5283-4777-b720-bdb69fb45ff1 · outbound

This paper cites AdaMerging: Adaptive Model Merging for Multi-Task Learning.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning AdaMerging: Adaptive Model Merging for Multi-Task Learning

Reference 77

Resolution
unresolved
no resolver link, observed 2026-08-06T19:44:56.889982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:44:56.889982Z digest=sha256:f73fa7784dc0a690ae9b34b8e90e1beb56c2aad3a7ccd7f99b39fded76c5b825

Observation e4d807c1-a1ba-46af-b6bd-d358cefc25d2 · outbound

This paper cites Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities

Reference 78

Resolution
unresolved
no resolver link, observed 2026-08-06T19:44:56.974583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:44:56.974583Z digest=sha256:06c62febcade18b74538e9ef0b43fbbdf47dcb585639b895d13b65e0174a06e4

Observation 70d3a8dc-ec18-4f9f-b525-381629751350 · outbound

This paper cites Path- planning strategy for lane changing based on adaptive-grid risk-fields of autonomous vehicles.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Path- planning strategy for lane changing based on adaptive-grid risk-fields of autonomous vehicles

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:03.409180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:57.063775Z digest=sha256:e2d05559324f97bde6bbe55943c13d26816691d5d00a03361e406f308ec96950

Observation 74777e67-148e-48b5-b5c1-93a8cced2fe2 · outbound

This paper cites Improv- ing the generalizability of trajectory prediction models with frenet-based domain normalization.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Improv- ing the generalizability of trajectory prediction models with frenet-based domain normalization

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:03.114204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:57.177705Z digest=sha256:a993fddf1e2f34647a34c0f9d28faddf8edd351abad7502e5d6cccc5df66a10e

Observation 3f2b23fe-6e4a-41a6-a166-700475f4620d · outbound

This paper cites Gradient surgery for multi-task learning.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Gradient surgery for multi-task learning

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:02.832766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:57.328392Z digest=sha256:99d01ec5b39985376a85dd65b433cd121f6a874c8bf7b483e2341124be0ffba5

Observation ec14b624-c118-438e-b288-6974e20dd15a · outbound

This paper cites A survey of autonomous driving: Common practices and emerging technologies.IEEE access, 8:58443– 58469, 2020.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning A survey of autonomous driving: Common practices and emerging technologies.IEEE access, 8:58443– 58469, 2020

Reference 82

Resolution
unresolved
no resolver link, observed 2026-08-06T19:44:57.456733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:44:57.456733Z digest=sha256:bbb253c52fc3bd1cb022e4549452a5d1ffd67b3dcef2ace61e2a365d82d78111

Observation 5769351f-4a4d-4343-9c18-347594d644e9 · outbound

This paper cites Dsdnet: Deep structured self-driving network.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Dsdnet: Deep structured self-driving network

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:02.746114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:57.588427Z digest=sha256:877d67685e668ff3985d7732ba23b3041dcdf8835263de12de5c623e280d367c

Observation 31c72611-0dcd-4176-ad13-5d35b6b5ea67 · outbound

This paper cites Genad: Generative end-to-end au- tonomous driving.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Genad: Generative end-to-end au- tonomous driving

Reference 84

Resolution
unresolved
no resolver link, observed 2026-08-06T19:44:57.725896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:44:57.725896Z digest=sha256:8a6191ed40ccf4b7f4e7a6d8043fc60235bd5932c5dc667c33b7d6becd0ee54f

Observation 5539862e-6320-4316-aeb0-e79c78ff7ffa · outbound

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

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Hivt: Hierarchical vector transformer for multi-agent motion prediction

Reference 85

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:02.623588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:57.825796Z digest=sha256:53a0968ab15e4b6255479ff55a4f4c80528289fd016d40ceb52c9aaa95aacf73

Observation 5be1268b-b7b2-4cf9-956b-2c99cdfcadf2 · outbound

This paper cites Query-centric trajectory prediction.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Query-centric trajectory prediction

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:02.463778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:57.943281Z digest=sha256:7740917e55d07af2d8c72d9a747a28d570bc87a2d24403487dfcb07af62a63ae

Observation fd3a9dc9-fe78-4d51-9bd6-7a99430fef57 · outbound

This paper cites Avatargpt: All- in-one framework for motion understanding planning gener- ation and beyond.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Avatargpt: All- in-one framework for motion understanding planning gener- ation and beyond

Reference 87

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:02.345226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:58.053779Z digest=sha256:4c452c19996b9715c761636e9385bb7acc72f16d412e2f56cea0b0d64a523ac4

Observation b7547318-d14b-4970-b882-d1d11120ab07 · outbound

This paper cites Unitraj: Universal human trajec- tory modeling from billion-scale worldwide traces.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Unitraj: Universal human trajec- tory modeling from billion-scale worldwide traces

Reference 88

Resolution
unresolved
no resolver link, observed 2026-08-06T19:44:58.209728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:44:58.209728Z digest=sha256:b603d43dfb19907dca6e5873e9857ef822523c8d1ec039634604d55d91d04843

Observation 9632e5db-45ba-4ed6-aa84-f98ca83a7598 · outbound

This paper cites We alternately select 4 out of 5 scenes to form the train- ing and validation datasets, and train a separate model for each configuration.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning We alternately select 4 out of 5 scenes to form the train- ing and validation datasets, and train a separate model for each configuration

Reference 89

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:02.264290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:58.313944Z digest=sha256:e3a5ec7100c8d50d35e94492e6755c8fb4996644169b43c2e02afb2bc227b9c6

Observation aa633df5-1705-4e76-8982-27b61a549d18 · outbound

This paper cites To model human interactions, the dataset first generates human movements by employing the ORCA algorithm, allowing agents to reach their desti- nations while avoiding collisions.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning To model human interactions, the dataset first generates human movements by employing the ORCA algorithm, allowing agents to reach their desti- nations while avoiding collisions

Reference 90

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:02.166554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:58.435603Z digest=sha256:7075483904545b4e3482197a3fcb4e9aaeed40a64da43352e7e78c7a211915aa

Observation 9bffedda-ee11-4bb3-82b9-6946548f9a02 · outbound

This paper cites The data was gath- ered in an indoor space measuring 8.4 × 18.8 m, with various fixed obstacles placed throughout.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning The data was gath- ered in an indoor space measuring 8.4 × 18.8 m, with various fixed obstacles placed throughout

Reference 91

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:02.072606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:58.564656Z digest=sha256:2949f16ae69c57328de40b1328e14eb6141d5c2319ed412d2ed8463f5d6f0827

Observation 2419a538-c348-4c81-946a-f339d20c93c8 · outbound

This paper cites ADE computes the L2 distance between every time step of the plan and the corresponding GT point, and then averages these dis- tances.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning ADE computes the L2 distance between every time step of the plan and the corresponding GT point, and then averages these dis- tances

Reference 92

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:01.950260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:58.689872Z digest=sha256:c902c0e809714421c969de4d145697799cefdb668605642d5de5e8c03f71b324

Observation cf730c09-8ba0-4835-b98a-09915abf94ff · outbound

This paper cites It considers a collision to oc- cur when the distance between certain waypoints in the generated plan and the ground truth plan is below a spec- ified threshold.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning It considers a collision to oc- cur when the distance between certain waypoints in the generated plan and the ground truth plan is below a spec- ified threshold

Reference 93

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:01.759853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:58.836710Z digest=sha256:39e3d572dee75e40ad2b18021ccdad958ea8fbd074d8941fbd4339e7216b4b4f

Observation d2f8b317-0c85-4f36-93c2-f019c74ad8f6 · outbound

This paper cites It calculates the L2 distance between the position at the final time step of the generated ego agent’s plan and the destina- tion.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning It calculates the L2 distance between the position at the final time step of the generated ego agent’s plan and the destina- tion

Reference 94

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:01.678227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:59.011148Z digest=sha256:63be436e91c25510f08c766c1dc0e1bb8cf28ea4518bea92e4728d98805b2e78

Observation c5209ee7-3e49-4004-85e4-46e0e06ffaa6 · outbound

This paper cites We compute the L2 distance Table 4.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning We compute the L2 distance Table 4

Reference 95

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:01.597298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:59.148447Z digest=sha256:ba850123999da45eeb5f4f34b0180137acb22a3b7cdf652f9e60da7bddf471ad

Observation a13a2f5d-2784-4b18-93fd-a283a3c3eb98 · outbound

This paper cites Specifically, we select the checkpoints where ADE, CR, FDE, and MR achieve their best values and store them in the checkpoint poolP.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Specifically, we select the checkpoints where ADE, CR, FDE, and MR achieve their best values and store them in the checkpoint poolP

Reference 96

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:01.503815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:59.258768Z digest=sha256:0183d489c336893ce9247691c97c8f498d01f9736594bd1dd8a58c7d5964fe0f

Observation 136e00d0-54c7-44c6-bf3e-37402910504c · outbound

This paper cites an unresolved cited work.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning Unresolved cited work

Reference 97

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:45:01.349722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:59.368015Z digest=sha256:5d6c807d56b8af2e0eb5880fc0eebd7b62ec25dcc0a107e4afc7952ef4c1f12d

Observation e70d7800-ac73-4850-90b5-027eaa42e52e · outbound

This paper cites As shown in Tab.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning As shown in Tab

Reference 98

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:01.244861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:59.516160Z digest=sha256:f9de008cd13aff7b115575b1a2b5d4c1d1d70fce28f77da4cbc48f99d7893a5c

Observation b136d19f-e642-43d3-82c2-1b92f99ec7a8 · outbound

This paper cites 6, when the GameTheoretic model tar- gets the SIT domain, we evaluated performance across different checkpoint intervals C.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning 6, when the GameTheoretic model tar- gets the SIT domain, we evaluated performance across different checkpoint intervals C

Reference 99

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:01.152867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:59.603312Z digest=sha256:dca19c285388e12a21305b2659379945f5d606d7d269f2108bd9933db2fb34de

Observation a0934d06-2fea-4dec-a881-154bb81ae20e · outbound

This paper cites A, the robot motion datasets differ in ego agent type and interaction mechanisms.

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning A, the robot motion datasets differ in ego agent type and interaction mechanisms

Reference 100

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:45:01.026238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-06T19:44:59.679839Z digest=sha256:bfa676c02c0be39f4cb59bae8a87492455662178bb1daa5eb8dcb4aa82d87a07

Pith citing papers

No inbound Pith citation observations are available.