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Paper Citation Record · LEDGER

Class-Incremental Motion Forecasting

As of 7 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2603.09420.

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

pith.paper-citation-record.v1
2603.09420 v3

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-15T00:16:35.376647Z

measured 52 of 52 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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52 of 52 outbound references displayed

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Outbound references

Observation 88803809-bd61-41a8-82e4-c12efa42ca2f · outbound

This paper cites Lifelong vehicle trajectory prediction frame- work based on generative replay.IEEE Transactions on Intelligent Transportation Systems, 24(12):13729–13741, 2023.

Class-Incremental Motion Forecasting Lifelong vehicle trajectory prediction frame- work based on generative replay.IEEE Transactions on Intelligent Transportation Systems, 24(12):13729–13741, 2023

Reference 1

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Observation e177eb54-968d-4698-9b97-294514dab826 · outbound

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

Class-Incremental Motion Forecasting nuscenes: A multimodal dataset for autonomous driving

Reference 2

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Observation 7de249cf-dd1d-4414-baea-519ccf37d009 · outbound

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

Class-Incremental Motion Forecasting The importance of prior knowledge in precise multimodal prediction

Reference 3

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Observation 62ad439a-0621-42d3-9f6a-094c61614421 · outbound

This paper cites Modeling the background for incremental learning in semantic segmentation.

Class-Incremental Motion Forecasting Modeling the background for incremental learning in semantic segmentation

Reference 4

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Observation 3f55c888-ada4-426a-82fd-b1a325bbfebb · outbound

This paper cites Traphic: Trajectory prediction in dense and heterogeneous traffic using weighted interactions.

Class-Incremental Motion Forecasting Traphic: Trajectory prediction in dense and heterogeneous traffic using weighted interactions

Reference 5

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Observation cfd2b5ac-36cf-4df8-80b7-95419c1ef232 · outbound

This paper cites A new knowledge distillation for incremental object detection.

Class-Incremental Motion Forecasting A new knowledge distillation for incremental object detection

Reference 6

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Observation 9e5ca73c-f299-4c14-976f-8bcb58dc8f42 · outbound

This paper cites Modeling vehicle interactions via modified lstm models for trajectory prediction.Ieee Access, 7:38287–38296, 2019.

Class-Incremental Motion Forecasting Modeling vehicle interactions via modified lstm models for trajectory prediction.Ieee Access, 7:38287–38296, 2019

Reference 7

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Observation 4a82fdc1-a19d-4542-8f4f-7881c975cad0 · outbound

This paper cites Convolutional social pooling for vehicle trajectory prediction.

Class-Incremental Motion Forecasting Convolutional social pooling for vehicle trajectory prediction

Reference 8

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Observation 98f4bd39-9a78-4122-b131-c234df518a74 · outbound

This paper cites Multi-modal tra- jectory prediction of surrounding vehicles with maneuver based lstms.

Class-Incremental Motion Forecasting Multi-modal tra- jectory prediction of surrounding vehicles with maneuver based lstms

Reference 9

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Observation be4b71b7-7020-4426-a447-d6a1ecf9ee91 · outbound

This paper cites Motion forecasting via model-based risk minimization.

Class-Incremental Motion Forecasting Motion forecasting via model-based risk minimization

Reference 10

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Observation 4c78c43b-79f4-4edc-afca-d4c9819b625a · outbound

This paper cites Stochasticity in motion: An information-theoretic approach to trajectory prediction.

Class-Incremental Motion Forecasting Stochasticity in motion: An information-theoretic approach to trajectory prediction

Reference 11

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Observation 6ae319cf-b250-431e-baad-4ed08c6f15a3 · outbound

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

Class-Incremental Motion Forecasting Vectornet: Encoding hd maps and agent dynamics from vectorized representation

Reference 12

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Observation c2bdc36e-c016-4da6-b8dc-21c903eff493 · outbound

This paper cites Vip3d: End-to- end visual trajectory prediction via 3d agent queries.

Class-Incremental Motion Forecasting Vip3d: End-to- end visual trajectory prediction via 3d agent queries

Reference 13

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Observation 9d5e3dd1-7824-4f47-9439-5c7ce7f688d6 · outbound

This paper cites Deep residual learning for image recognition.

Class-Incremental Motion Forecasting Deep residual learning for image recognition

Reference 14

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Observation 6fd15666-36ed-46da-bc8f-e0cbb33cd065 · outbound

This paper cites Taxonomy-aware continual semantic segmentation in hyperbolic spaces for open-world perception.IEEE Robotics and Automation Letters, 10(2):1904–1911, 2024.

Class-Incremental Motion Forecasting Taxonomy-aware continual semantic segmentation in hyperbolic spaces for open-world perception.IEEE Robotics and Automation Letters, 10(2):1904–1911, 2024

Reference 15

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Observation 8cf1916e-7607-449c-a209-3955419de717 · outbound

This paper cites Fiery: Future instance prediction in bird’s-eye view from surround monocular cameras.

Class-Incremental Motion Forecasting Fiery: Future instance prediction in bird’s-eye view from surround monocular cameras

Reference 16

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Observation 9adbfba0-23c7-4640-bf41-9585a894d7ce · outbound

This paper cites Planning-oriented autonomous driving.

Class-Incremental Motion Forecasting Planning-oriented autonomous driving

Reference 17

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Observation 560dd106-1edc-42b7-af25-6b2ed4c74844 · outbound

This paper cites Multi-modal motion prediction with transformer-based neural network for autonomous driving.

Class-Incremental Motion Forecasting Multi-modal motion prediction with transformer-based neural network for autonomous driving

Reference 18

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Observation ea01a4f3-8e99-447d-8a75-603341f60acb · outbound

This paper cites Perceive, Interact, Predict: Learning Dynamic and Static Clues for End-to-End Motion Prediction.

Class-Incremental Motion Forecasting Perceive, Interact, Predict: Learning Dynamic and Static Clues for End-to-End Motion Prediction

Reference 19

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Observation 585513ed-8d0f-407e-bcd1-20a5f3e52797 · outbound

This paper cites Far3d: Expanding the horizon for surround-view 3d object detection.

Class-Incremental Motion Forecasting Far3d: Expanding the horizon for surround-view 3d object detection

Reference 20

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Observation f2141bf8-1631-4d32-920a-40338722f288 · outbound

This paper cites Towards open world object detection.

Class-Incremental Motion Forecasting Towards open world object detection

Reference 21

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Observation 29a14985-3a92-410a-8995-4ea31ccb6b3b · outbound

This paper cites Con- tinual learning for motion prediction model via meta- representation learning and optimal memory buffer reten- tion strategy.

Class-Incremental Motion Forecasting Con- tinual learning for motion prediction model via meta- representation learning and optimal memory buffer reten- tion strategy

Reference 22

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Observation 80a52b01-fb85-4fb9-b527-81af802a46d0 · outbound

This paper cites An energy and gpu-computation efficient backbone network for real-time object detection.

Class-Incremental Motion Forecasting An energy and gpu-computation efficient backbone network for real-time object detection

Reference 23

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Observation 5026ae4e-2d80-4338-82ff-3f31431fd0d6 · outbound

This paper cites DECODE: Domain-aware Continual Domain Expansion for Motion Prediction.

Class-Incremental Motion Forecasting DECODE: Domain-aware Continual Domain Expansion for Motion Prediction

Reference 24

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Observation e266b087-f408-4919-946d-ae15c4fc6f5a · outbound

This paper cites End-to- end contextual perception and prediction with interaction transformer.

Class-Incremental Motion Forecasting End-to- end contextual perception and prediction with interaction transformer

Reference 25

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Observation f45e8875-5b2d-4fd3-b392-76b878d84992 · outbound

This paper cites Learning lane graph representations for motion forecasting.

Class-Incremental Motion Forecasting Learning lane graph representations for motion forecasting

Reference 26

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Observation 98545164-889c-4543-ba78-9b8eba6a263c · outbound

This paper cites Sparse4D v3: Advancing End-to-End 3D Detection and Tracking.

Class-Incremental Motion Forecasting Sparse4D v3: Advancing End-to-End 3D Detection and Tracking

Reference 27

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Observation 6f7413ba-50ad-42ea-8796-043192961c8c · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection.

Class-Incremental Motion Forecasting Grounding dino: Marrying dino with grounded pre-training for open-set object detection

Reference 28

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Observation f3998bd9-b66d-4a2d-a8ee-0780a836b515 · outbound

This paper cites Multi-Task Incremental Learning for Object Detection.

Class-Incremental Motion Forecasting Multi-Task Incremental Learning for Object Detection

Reference 29

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Observation fd253679-7d14-439e-80f2-3947c9816682 · outbound

This paper cites Continual detection transformer for incremen- tal object detection.

Class-Incremental Motion Forecasting Continual detection transformer for incremen- tal object detection

Reference 30

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Observation 67ebb54e-167e-47b7-811b-61af08d7350d · outbound

This paper cites Multimodal motion prediction with stacked transformers.

Class-Incremental Motion Forecasting Multimodal motion prediction with stacked transformers

Reference 31

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Observation a65f2e22-9821-42a3-ba58-c7aa30e5372c · outbound

This paper cites Cooler: class-incremental learning for appearance-based multiple object tracking.

Class-Incremental Motion Forecasting Cooler: class-incremental learning for appearance-based multiple object tracking

Reference 32

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Observation 102b294f-b881-482c-a080-75bcfc3d4df7 · outbound

This paper cites Decoupled weight decay regularization.

Class-Incremental Motion Forecasting Decoupled weight decay regularization

Reference 33

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Observation 31f3a86a-f93e-41f9-872b-77bbe7fc8349 · outbound

This paper cites Evidential uncertainty estimation for multi-modal trajectory prediction.

Class-Incremental Motion Forecasting Evidential uncertainty estimation for multi-modal trajectory prediction

Reference 34

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Observation ccf2fc91-ccf9-4eca-8331-f788d92e42d4 · outbound

This paper cites Wayformer: Motion Forecasting via Simple & Efficient Attention Networks.

Class-Incremental Motion Forecasting Wayformer: Motion Forecasting via Simple & Efficient Attention Networks

Reference 35

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Observation 4c81af6e-76bd-4576-9808-1dacdc8cf087 · outbound

This paper cites Forecasting from lidar via future object detection.

Class-Incremental Motion Forecasting Forecasting from lidar via future object detection

Reference 36

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Observation f916142f-8135-4e18-9218-93869c430781 · outbound

This paper cites Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks.

Class-Incremental Motion Forecasting Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

Reference 37

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Observation f2cbabfe-3f48-4683-aa59-cabeb83a6823 · outbound

This paper cites Incremental learning of object detectors without catastrophic forgetting.

Class-Incremental Motion Forecasting Incremental learning of object detectors without catastrophic forgetting

Reference 38

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Observation 886808d4-d67f-4114-a30d-46f06299f229 · outbound

This paper cites DINOv3.

Class-Incremental Motion Forecasting DINOv3

Reference 39

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Observation b59988b1-6002-47a2-b692-29ba560d9f25 · outbound

This paper cites Sparsedrive: End-to-end autonomous driving via sparse scene representation.

Class-Incremental Motion Forecasting Sparsedrive: End-to-end autonomous driving via sparse scene representation

Reference 40

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source=pdf_text observed=2026-07-15T00:16:35.376647Z digest=sha256:c10c4dcd992697877c776b6bd6c097701c940630c64e187672917b92eee54dec

Observation 17e91677-7f59-4c2c-ad1d-e82b2ee4c969 · outbound

This paper cites Covio: Online continual learning for visual-inertial odometry.

Class-Incremental Motion Forecasting Covio: Online continual learning for visual-inertial odometry

Reference 41

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source=pdf_text observed=2026-07-15T00:16:35.376647Z digest=sha256:1ddde170f568c004837c8d1fee956cd921b218c6a6dafa990d3a43b5d2a73970

Observation f78c23f7-d84e-413b-b162-e2ccd7f4a49d · outbound

This paper cites Codeps: Online continual learning for depth estimation and panoptic segmentation.

Class-Incremental Motion Forecasting Codeps: Online continual learning for depth estimation and panoptic segmentation

Reference 42

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source=pdf_text observed=2026-07-15T00:16:35.376647Z digest=sha256:d27cb9362bca6a16583c5dbfb2e4d19405855ad8d8430c66dae6d190e850d2b7

Observation 17d6b14f-a8a8-4676-b59f-74a49a679d33 · outbound

This paper cites Parkdiffusion: Heterogeneous multi- agent multi-modal trajectory prediction for automated parking using diffusion models.

Class-Incremental Motion Forecasting Parkdiffusion: Heterogeneous multi- agent multi-modal trajectory prediction for automated parking using diffusion models

Reference 43

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source=pdf_text observed=2026-07-15T00:16:35.376647Z digest=sha256:9a301b8f5b6194411ec2eeec47b80b17fa8424cf1bd348aa56cb2c71ac2be458

Observation b31b7f6c-2cd6-4338-96dd-2f847c5b6374 · outbound

This paper cites Parkdiffusion++: Ego intention conditioned joint multi-agent trajectory prediction for automated parking using diffusion models.arXiv preprint arXiv:2602.20923, 2026.

Class-Incremental Motion Forecasting Parkdiffusion++: Ego intention conditioned joint multi-agent trajectory prediction for automated parking using diffusion models.arXiv preprint arXiv:2602.20923, 2026

Reference 44

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source=pdf_text observed=2026-07-15T00:16:35.376647Z digest=sha256:dfc2fb3cffed434c4bb3a421c7370b206a04a3f1e5fb5e15c2d05f080b1f9bd7

Observation e00efccb-cb14-4459-87ee-02eb1490700c · outbound

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

Class-Incremental Motion Forecasting Argoverse 2: Next generation datasets for self-driving perception and forecasting

Reference 45

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source=pdf_text observed=2026-07-15T00:16:35.376647Z digest=sha256:f28b2e0d498620ef964177259d28057e504f32e6ffb9e79fead2ea6c0e0789ce

Observation 6d18d01d-cd57-4425-9991-efd542a9cdb3 · outbound

This paper cites Motion trajectory prediction based on a cnn-lstm sequential model.Science China Information Sciences, 63(11):212207, 2020.

Class-Incremental Motion Forecasting Motion trajectory prediction based on a cnn-lstm sequential model.Science China Information Sciences, 63(11):212207, 2020

Reference 46

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source=pdf_text observed=2026-07-15T00:16:35.376647Z digest=sha256:f3742d172e8393ad748cb5d25b18abd1fd841128890847d1417d8a1e7a27f68e

Observation 7184476d-41d2-4a1e-ac81-86244289b9be · outbound

This paper cites Towards motion forecasting with real-world perception inputs: Are end-to-end approaches competitive? In Int.

Class-Incremental Motion Forecasting Towards motion forecasting with real-world perception inputs: Are end-to-end approaches competitive? In Int

Reference 47

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source=pdf_text observed=2026-07-15T00:16:35.376647Z digest=sha256:16c8b9c696361e59d50e9904f109739b99f16eaa710e6c89acfcc85e547a7e3f

Observation cca0b00e-0e75-465b-a64e-d00adf45cccc · outbound

This paper cites Multi-view correlation distillation for incremental object detection.

Class-Incremental Motion Forecasting Multi-view correlation distillation for incremental object detection

Reference 48

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source=pdf_text observed=2026-07-15T00:16:35.376647Z digest=sha256:6000e022c04d4e8e5d7112b36bbcdc8538916c0f0d01e48ddddd0fa2135a75f8

Observation 7746fbf4-d200-4d1f-b412-425e35ee4372 · outbound

This paper cites Vehicle motion prediction at intersections based on the turning intention and prior trajectories model.

Class-Incremental Motion Forecasting Vehicle motion prediction at intersections based on the turning intention and prior trajectories model

Reference 49

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source=pdf_text observed=2026-07-15T00:16:35.376647Z digest=sha256:e3a0622a6367fc593c94df96a3a605a380018198b9013821bae1d4b6aeee0f52

Observation c9564b7f-74f6-4bc5-991a-b21ea1904027 · outbound

This paper cites BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving.

Class-Incremental Motion Forecasting BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving

Reference 50

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source=pdf_text observed=2026-07-15T00:16:35.376647Z digest=sha256:e0b8b6430e5c58e5dfb9199d7baad411b3e1a1fbe6123c0a9a6e77d891847700

Observation d0da6e99-76bf-49ee-8a9b-1199b936c859 · outbound

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

Class-Incremental Motion Forecasting Hivt: Hierarchical vector transformer for multi- agent motion prediction

Reference 51

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source=pdf_text observed=2026-07-15T00:16:35.376647Z digest=sha256:fbadfc88e06dfe7ec345b5f618c582efd64d86e54879b7ded96d73a9940b0c05

Observation fa17dcb0-c14a-44ac-8870-6622c6e49da3 · outbound

This paper cites A recurrent neural network solution for predicting driver intention at unsignalized intersections.IEEE Robotics and Automation Letters, 3(3):1759–1764, 2018.

Class-Incremental Motion Forecasting A recurrent neural network solution for predicting driver intention at unsignalized intersections.IEEE Robotics and Automation Letters, 3(3):1759–1764, 2018

Reference 52

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source=pdf_text observed=2026-07-15T00:16:35.376647Z digest=sha256:05a2164d4af19077c1456871f7826fb536445afd73aae212593e7fef24efb03a

Pith citing papers

No inbound Pith citation observations are available.