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Paper Citation Record · LEDGER

DRIFT: Drift and Aggregation for Motion Planning

As of 8 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2607.14507.

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

pith.paper-citation-record.v1
2607.14507 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T01:56:05.733028Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

29 of 29 outbound references displayed

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External citation measurements

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Outbound references

Observation b9166c5e-226b-4bfb-b808-240344165375 · outbound

This paper cites Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline,.

DRIFT: Drift and Aggregation for Motion Planning Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline,

Reference 1

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Observation 0ba36c4f-0b05-4722-9eec-bfcd1e9d0022 · outbound

This paper cites Planning-oriented autonomous driving,.

DRIFT: Drift and Aggregation for Motion Planning Planning-oriented autonomous driving,

Reference 2

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Observation c8d2a3b0-0f0c-4268-a96f-f20b45e8688b · outbound

This paper cites Parting with misconceptions about learning-based vehicle motion planning,.

DRIFT: Drift and Aggregation for Motion Planning Parting with misconceptions about learning-based vehicle motion planning,

Reference 3

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source=pdf_text observed=2026-08-02T01:56:05.620156Z digest=sha256:6eb9822749e95de74122e5a0508e2f8edaee80f16dbef5145de917279ebfb058

Observation 8fd4e0dd-3e72-4cd8-b278-1a84cdd51905 · outbound

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

DRIFT: Drift and Aggregation for Motion Planning End-to-end autonomous driving: Challenges and frontiers,

Reference 4

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Observation 31ee5bc2-074c-49f0-a901-0b97ec574034 · outbound

This paper cites Trajectory prediction for autonomous driving: Progress, limitations, and future directions,.

DRIFT: Drift and Aggregation for Motion Planning Trajectory prediction for autonomous driving: Progress, limitations, and future directions,

Reference 5

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source=pdf_text observed=2026-08-02T01:56:05.629605Z digest=sha256:9c50a070393a0d71ba911f1f30b42dfb6c768c9c2d806d3027a16b2229bec327

Observation 9c2bbd49-44c3-4cb3-8210-c3f4a678e4ba · outbound

This paper cites Detra: A unified model for object detection and trajectory forecast- ing,.

DRIFT: Drift and Aggregation for Motion Planning Detra: A unified model for object detection and trajectory forecast- ing,

Reference 6

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Observation 7bec6502-6183-42ca-8252-d64aa4e4f03c · outbound

This paper cites Diffusiondrive: Truncated diffusion model for end-to-end autonomous driving,.

DRIFT: Drift and Aggregation for Motion Planning Diffusiondrive: Truncated diffusion model for end-to-end autonomous driving,

Reference 7

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Observation de17cab1-7500-45f8-961e-c854ca1de7fd · outbound

This paper cites Diffusion-based planning for autonomous driv- ing with flexible guidance,.

DRIFT: Drift and Aggregation for Motion Planning Diffusion-based planning for autonomous driv- ing with flexible guidance,

Reference 8

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source=pdf_text observed=2026-08-02T01:56:05.643807Z digest=sha256:6698d86cfd48187ac412436ac9cbd287099da2f7be678d2319c8088d42ae0956

Observation 0b578c19-3b3f-4fbc-ac63-f02d8abdee1a · outbound

This paper cites GenAD: Generalized Predictive Model for Autonomous Driving.

DRIFT: Drift and Aggregation for Motion Planning GenAD: Generalized Predictive Model for Autonomous Driving

Reference 9

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Observation e747f785-126b-4119-ba51-b6a2563c8579 · outbound

This paper cites Meanfuser: Fast one-step multi-modal trajectory generation and adaptive reconstruction via meanflow for end-to-end autonomous driving,.

DRIFT: Drift and Aggregation for Motion Planning Meanfuser: Fast one-step multi-modal trajectory generation and adaptive reconstruction via meanflow for end-to-end autonomous driving,

Reference 10

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source=pdf_text observed=2026-08-02T01:56:05.653290Z digest=sha256:449c21f50053a723dcf201eaf73967df01f32e6c73ca62167fff89a09409d043

Observation a792d65e-9b05-4f59-8206-316e22634284 · outbound

This paper cites Generalized Trajectory Scoring for End-to-end Multimodal Planning.

DRIFT: Drift and Aggregation for Motion Planning Generalized Trajectory Scoring for End-to-end Multimodal Planning

Reference 11

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Observation 28024476-3310-40de-837c-56a64f8bf670 · outbound

This paper cites Grade: Guiding realistic autonomous driving with adaptive trajectory evolution,.

DRIFT: Drift and Aggregation for Motion Planning Grade: Guiding realistic autonomous driving with adaptive trajectory evolution,

Reference 12

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Observation b982296e-fd29-425a-a2ee-de28502da9d2 · outbound

This paper cites Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving.

DRIFT: Drift and Aggregation for Motion Planning Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving

Reference 13

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Observation 6ef14108-6f55-4197-9b25-7eff47a3e973 · outbound

This paper cites Driveadapter: Breaking the coupling barrier of perception and planning in end-to-end autonomous driving,.

DRIFT: Drift and Aggregation for Motion Planning Driveadapter: Breaking the coupling barrier of perception and planning in end-to-end autonomous driving,

Reference 14

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Observation d419fa7d-f004-4374-b43f-3f4d1a57a51f · outbound

This paper cites Comdrive: Comfort-oriented end-to- end autonomous driving,.

DRIFT: Drift and Aggregation for Motion Planning Comdrive: Comfort-oriented end-to- end autonomous driving,

Reference 15

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source=pdf_text observed=2026-08-02T01:56:05.675266Z digest=sha256:4e316c64285a98dff221ddabb8e7058960fc56c6c14e70f3025703d08c720e34

Observation 1a9198ab-1f6a-44c0-b001-06dcf0a2a7ce · outbound

This paper cites Revisiting Feature Prediction for Learning Visual Representations from Video.

DRIFT: Drift and Aggregation for Motion Planning Revisiting Feature Prediction for Learning Visual Representations from Video

Reference 16

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source=pdf_text observed=2026-08-02T01:56:05.679502Z digest=sha256:22f6c53868c709961b41d5394bb4a7f5da2aaedcb97722fafcacbe8a5103c0c7

Observation 79c5c383-4499-4547-971f-7cfc61d7d2f9 · outbound

This paper cites Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking,.

DRIFT: Drift and Aggregation for Motion Planning Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking,

Reference 17

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source=pdf_text observed=2026-08-02T01:56:05.683954Z digest=sha256:decbbb834e2968009b953711f1004e4fadc32ceee56b965af8b41d51fe483052

Observation 3373d6a1-b5a4-4f26-ab64-538e21fd971e · outbound

This paper cites Transfuser: Imitation with transformer-based sensor fusion for au- tonomous driving,.

DRIFT: Drift and Aggregation for Motion Planning Transfuser: Imitation with transformer-based sensor fusion for au- tonomous driving,

Reference 18

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Observation a7d11d75-5c8e-4b71-873b-012710d90c0d · outbound

This paper cites Drivetransformer: Unified transformer for scalable end-to-end autonomous driving,.

DRIFT: Drift and Aggregation for Motion Planning Drivetransformer: Unified transformer for scalable end-to-end autonomous driving,

Reference 19

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Observation 4eb737c4-a2c9-4f25-868f-256512fdeca4 · outbound

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

DRIFT: Drift and Aggregation for Motion Planning Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving,

Reference 20

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Observation 6fbc6e54-d23a-4333-bb60-c44c861abc30 · outbound

This paper cites Diffrefiner: Coarse to fine trajectory planning via diffusion refinement with semantic interaction for end to end autonomous driving,.

DRIFT: Drift and Aggregation for Motion Planning Diffrefiner: Coarse to fine trajectory planning via diffusion refinement with semantic interaction for end to end autonomous driving,

Reference 21

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Observation 186d4a6f-3f6c-48fa-897e-13273a444cb7 · outbound

This paper cites Hipro-ad: Sparse trajectory transformer for end-to-end autonomous driving with hybrid spatiotemporal attention,.

DRIFT: Drift and Aggregation for Motion Planning Hipro-ad: Sparse trajectory transformer for end-to-end autonomous driving with hybrid spatiotemporal attention,

Reference 22

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Observation 75ff7b11-30b2-4256-b5d5-76f8ab8b2214 · outbound

This paper cites Generative Modeling via Drifting.

DRIFT: Drift and Aggregation for Motion Planning Generative Modeling via Drifting

Reference 23

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Observation c466a6eb-9113-4c80-8465-9087a6dea594 · outbound

This paper cites End-to-end driving with online trajectory evaluation via bev world model,.

DRIFT: Drift and Aggregation for Motion Planning End-to-end driving with online trajectory evaluation via bev world model,

Reference 24

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Observation 8217c191-d37b-4deb-aa89-ea62315f2304 · outbound

This paper cites World4drive: End-to-end autonomous driving via intention-aware physical latent world model,.

DRIFT: Drift and Aggregation for Motion Planning World4drive: End-to-end autonomous driving via intention-aware physical latent world model,

Reference 25

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Observation 82cf7335-ed4d-4c6f-a288-921aae27930c · outbound

This paper cites Epona: Autoregressive diffusion world model for autonomous driving,.

DRIFT: Drift and Aggregation for Motion Planning Epona: Autoregressive diffusion world model for autonomous driving,

Reference 26

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Observation eac67d4e-11db-4df4-bd7f-3f250fce4478 · outbound

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

DRIFT: Drift and Aggregation for Motion Planning Is ego status all you need for open-loop end-to-end autonomous driving?

Reference 27

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Observation 73f0eb9c-fedf-43ca-b161-1bac98d99885 · outbound

This paper cites Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation.

DRIFT: Drift and Aggregation for Motion Planning Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation

Reference 28

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Observation 197bff6b-a2cf-49b1-bba9-f1d480a5e277 · outbound

This paper cites Drivesuprim: Towards precise trajectory selection for end- to-end planning,.

DRIFT: Drift and Aggregation for Motion Planning Drivesuprim: Towards precise trajectory selection for end- to-end planning,

Reference 29

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