Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning | Paper Citation Record

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

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:51.845247Z

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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

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:45:00.571730Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:51.945286Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:52.120379Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:52.558930Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:52.623740Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:52.690999Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:52.759610Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:07.479384Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:45:07.473159Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:53.253567Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-06T19:44:53.356117Z

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

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

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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

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

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

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

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

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

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

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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 2026-08-07T06:34:17.273281+00:00.

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

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Unavailable: canonical work link unavailable.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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.

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.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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.

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 2026-08-07T06:34:17.273281+00:00.

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.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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 2026-08-07T06:34:17.273281+00:00.

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Reference resolution

Outbound references

Pith citing papers