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Paper Citation Record · LEDGER

MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 29 inbound Pith citation observations for arXiv:1910.05449.

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

pith.paper-citation-record.v1
1910.05449 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 29 of 29 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T20:07:09.361168Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T16:29:56.718409Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7498afc4-dcd6-4e4c-84bc-91ea474d999e · inbound

Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference cites this paper.

Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 13

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arxiv_id, observed 2026-05-13T04:15:54.759660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation dbc7e035-086f-43f0-b48d-f061ca7a44e9 · inbound

VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning cites this paper.

VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 3

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arxiv_id, observed 2026-05-24T03:18:49.496542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 4a62545b-6cfa-430a-a850-97b0afe59c3e · inbound

Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving cites this paper.

Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 7

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arxiv_id, observed 2026-05-15T15:24:23.933893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation d9c88809-4477-4b2e-8143-814d43366375 · inbound

Direct Preference Optimization-Enhanced Multi-Guided Diffusion Model for Traffic Scenario Generation cites this paper.

Direct Preference Optimization-Enhanced Multi-Guided Diffusion Model for Traffic Scenario Generation MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T20:07:09.361168Z digest=sha256:e0556980282c141ff6c081d4cb5642502350e773277270364c6ecfc78ac71ed7

Observation 2d9efd70-a94a-4ae1-9f7d-233839ac33a2 · inbound

ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation cites this paper.

ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 7

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arxiv_id, observed 2026-05-17T08:10:39.336650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-17T08:10:39.240168Z digest=sha256:3651e17042b487a2e33008fe4de83350e26e9dd77a3bad6c4757d84924233da1

Observation e8881259-4ea7-4196-834e-29a720aa3a1f · inbound

HAMF: A Hybrid Attention-Mamba Framework for Joint Scene Context Understanding and Future Motion Representation Learning cites this paper.

HAMF: A Hybrid Attention-Mamba Framework for Joint Scene Context Understanding and Future Motion Representation Learning MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 14

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no resolver link, observed 2026-08-07T15:16:11.155330Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:16:11.155330Z digest=sha256:dacbc62f6e4e56ffc7e3a72cde156ab7c1936dbee22ed8cc3f20f2924c9cafc1

Observation 100a528b-3082-4f39-aa15-c53caa46a017 · inbound

CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving cites this paper.

CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 4

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no resolver link, observed 2026-08-07T13:47:09.742266Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:47:09.742266Z digest=sha256:1fafbdd4b36578a93de9f5f28965d49a9b4e2238abd181cdfdf3c4aa642fd712

Observation 98a226a5-c546-4b57-973a-f71d66953428 · inbound

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation cites this paper.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 2019

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source=pdf_text observed=2026-08-07T12:45:48.781737Z digest=sha256:525026e1f8353837fcda2fa696c05818a94f40fe3f4241be1172ed6b6e1a5904

Observation 7d81486f-8bef-4fb7-a4f5-f0287779a421 · inbound

ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving cites this paper.

ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 7

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arxiv_id, observed 2026-05-15T07:36:24.379941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-15T07:36:24.319361Z digest=sha256:c7898da5f1d64bd6182b674ed5af439f8f571132e5bbd80d4ad247f6e1ef0b82

Observation 957dc1fc-df82-483d-9507-51814199cd0c · inbound

RoCA: Robust Cross-Domain End-to-End Autonomous Driving cites this paper.

RoCA: Robust Cross-Domain End-to-End Autonomous Driving MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 4

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source=arxiv_source observed=2026-08-07T04:40:57.278839Z digest=sha256:87d51db458e0cb0c59921b7e1b2f06274a39ec257e91fa02bed7fb5552f569e7

Observation 09da6045-488f-4dcb-ab9f-b449363a2107 · inbound

Goal-conditioned Hierarchical Reinforcement Learning for Sample-efficient and Safe Autonomous Driving at Intersections cites this paper.

Goal-conditioned Hierarchical Reinforcement Learning for Sample-efficient and Safe Autonomous Driving at Intersections MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 2019

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source=pdf_text observed=2026-08-06T23:49:13.253143Z digest=sha256:4e1f645aaa2272aeda5bfc0d1f066962cbaf57705eb7af57ef343752f1a6a08b

Observation afff6e9d-37b8-4f8c-a3bf-0eac919a196c · inbound

SRefiner: Soft-Braid Attention for Multi-Agent Trajectory Refinement cites this paper.

SRefiner: Soft-Braid Attention for Multi-Agent Trajectory Refinement MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 5

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source=pdf_text observed=2026-08-06T19:56:40.441830Z digest=sha256:54fec09a13a968382e4b0643ac82548a048ec9c2cabbee2ed771e4c107718387

Observation 17254ada-b175-411d-9e47-4e8ad90e3fc5 · inbound

ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture cites this paper.

ILNet: Trajectory Prediction with Inverse Learning Attention for Enhancing Intention Capture MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 5

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source=pdf_text observed=2026-08-06T19:07:15.397231Z digest=sha256:f235cfd73837499cf5a3446b974d274276f9681f954402425193a95353b56c75

Observation 9e635c8f-2ac5-4687-8221-b84e63ad6944 · inbound

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics cites this paper.

Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 3

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:59:54.485669Z digest=sha256:039a3d0ecdd79dc969023f6832a24e0100e6c9f61dc97b7b7429df9d0eec6b44

Observation 2b7ec655-202e-4893-a459-82997c26c408 · inbound

PhysVarMix: Physics-Informed Variational Mixture Model for Multi-Modal Trajectory Prediction cites this paper.

PhysVarMix: Physics-Informed Variational Mixture Model for Multi-Modal Trajectory Prediction MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 5

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source=pdf_text observed=2026-08-06T14:13:03.903985Z digest=sha256:9f5948fcee52e4acfd22bee6d47e16a14b98a7d67b88f533701853f0240d8358

Observation 5fcff8f1-6666-4cd1-9274-c4dd65ae98e5 · inbound

A Spatio-temporal Continuous Network for Stochastic 3D Human Motion Prediction cites this paper.

A Spatio-temporal Continuous Network for Stochastic 3D Human Motion Prediction MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 33

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no resolver link, observed 2026-08-06T05:33:57.854132Z

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source=arxiv_source observed=2026-08-06T05:33:57.854132Z digest=sha256:23157774b8298a53fe2e95b7ab667d685be5520fb6523225ddecb0ba72a79d67

Observation e93793cd-fb8c-40d8-a923-b1b292b09415 · inbound

Adaptive Output Steps: FlexiSteps Network for Dynamic Trajectory Prediction cites this paper.

Adaptive Output Steps: FlexiSteps Network for Dynamic Trajectory Prediction MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 1

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no resolver link, observed 2026-08-05T16:50:01.098425Z

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source=pdf_text observed=2026-08-05T16:50:01.098425Z digest=sha256:d196cb0bdc5e0526c327308ca4270e03526e265c0a453339b8685682102b658c

Observation 37fbfe0a-46f7-4054-af8c-daf5d69c605d · inbound

Goal-Oriented Reactive Simulation for Closed-Loop Trajectory Prediction cites this paper.

Goal-Oriented Reactive Simulation for Closed-Loop Trajectory Prediction MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 5

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arxiv_id, observed 2026-05-15T00:58:26.223278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-15T00:55:01.446865Z digest=sha256:4de2f9733b385ea9640a1da8e8756d3f0654d0919b0e84a5fc1a54453848e233

Observation 6a0aa7d5-adf8-44c5-ba8b-b4c98f0bb929 · inbound

SAIL: Scene-aware Adaptive Iterative Learning for Long-Tail Trajectory Prediction in Autonomous Vehicles cites this paper.

SAIL: Scene-aware Adaptive Iterative Learning for Long-Tail Trajectory Prediction in Autonomous Vehicles MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 72

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verified exact
arxiv_id, observed 2026-05-10T22:50:50.869193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T19:31:42.519478Z digest=sha256:ce67e902b33a5d1559c8906ba6edb3993dffb4c4fc377cc6ea8891c09927aa3d

Observation a5700cec-b0f7-4848-9b26-9c3b4f9f5137 · inbound

EdgeVTP: Exploration of Latency-efficient Trajectory Prediction for Edge-based Embedded Vision Applications cites this paper.

EdgeVTP: Exploration of Latency-efficient Trajectory Prediction for Edge-based Embedded Vision Applications MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 10

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arxiv_id, observed 2026-05-10T09:18:32.375802Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T07:53:01.735375Z digest=sha256:6b5b6ed7854e67795020f806c96f408112e0baee92fa61bc3af48c30e0870798

Observation cdec73a1-90fa-46fe-a9ca-8e6bc0769e66 · inbound

Chatting about Conditional Trajectory Prediction cites this paper.

Chatting about Conditional Trajectory Prediction MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 4

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arxiv_id, observed 2026-05-11T12:01:06.298337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T04:17:58.564914Z digest=sha256:874245206356cd79f8996090f61d73c68b82397cf1d7b3c1e9e87276e0b7176d

Observation 5d3c6ab9-3f32-4d68-9734-dbd23bfa18a9 · inbound

Driving Intents Amplify Planning-Oriented Reinforcement Learning cites this paper.

Driving Intents Amplify Planning-Oriented Reinforcement Learning MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 4

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arxiv_id, observed 2026-05-14T20:52:58.747807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-14T20:50:14.602822Z digest=sha256:a5d584ec87369c17744786cf8d7773e22bcdcf9efb5390a7dc1f21a6f71d063b

Observation 9c5ca670-ed91-489c-a9d7-c722a1af30dc · inbound

Driving Intents Amplify Planning-Oriented Reinforcement Learning cites this paper.

Driving Intents Amplify Planning-Oriented Reinforcement Learning MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 4

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arxiv_id, observed 2026-05-15T04:59:45.539399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-15T04:58:07.943615Z digest=sha256:4e7e77d4556ecbbb7b792ddfcc339a407f2757e31c2fb6b8b205fb57f9124f89

Observation 55d6660e-aaa9-4edd-af21-d2e560b18813 · inbound

BitTP: The Lightweight Trajectory Prediction Model with BitLLM for Edge-Devices cites this paper.

BitTP: The Lightweight Trajectory Prediction Model with BitLLM for Edge-Devices MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 7

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arxiv_id, observed 2026-06-29T08:03:14.567690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-29T07:56:00.180707Z digest=sha256:1ef70aba90ed3d62517aa9b20a5b6df1f6a1f4b374b0244becd1b4f1dce77449

Observation 4d2f0e47-3315-4ec8-8688-b98509a06fad · inbound

NavWM: A Unified Navigation World Model for Foresight-Driven Planning cites this paper.

NavWM: A Unified Navigation World Model for Foresight-Driven Planning MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 40

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arxiv_id, observed 2026-07-04T16:29:56.720051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-26T00:42:30.237545Z digest=sha256:1541becf7ed9a1ae4cbd9e97d3b32ba32696e3456c5d7703cca7f22fd1d04626

Observation d4e2b13e-5f03-468a-ba3c-fe6f9ab62b62 · inbound

Rethinking Training & Inference for Forecasting: Linking Winner-Take-All back to GMMs cites this paper.

Rethinking Training & Inference for Forecasting: Linking Winner-Take-All back to GMMs MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 9

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arxiv_id, observed 2026-07-04T15:49:58.057027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-26T01:16:40.007718Z digest=sha256:8df9e7196bad430ef4e83ae8356d93ba7ae05fd9428b0c436adb42b40cf2740e

Observation e66280e1-5c93-4b6d-80ac-7c8d04062adc · inbound

TGRIP: A Text-Guided Approach to Vehicle Instance Prediction in Autonomous Driving cites this paper.

TGRIP: A Text-Guided Approach to Vehicle Instance Prediction in Autonomous Driving MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 2

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T13:09:16.091741Z digest=sha256:14df00dab36e1b42bfbf7b7b6c4ddd39216e20961f5209fe50b27aa79f821981

Observation f4ddec34-db7f-4613-ba6d-ac377f0cedbe · inbound

A Dynamic Scene Interaction Reasoning Framework for Scene-level Lane-Change Intention and Trajectory Prediction of Multiple Interacting Vehicles cites this paper.

A Dynamic Scene Interaction Reasoning Framework for Scene-level Lane-Change Intention and Trajectory Prediction of Multiple Interacting Vehicles MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 20

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source=arxiv_source observed=2026-07-14T16:37:01.846968Z digest=sha256:4e1c086d7cef8cd57b701037f00a1f291e43a54c4b4195a370114ef59c896dbc

Observation 291c3adc-7fb0-48be-954a-c082873c6de4 · inbound

Pivot-Centric Trajectory Prediction: Bridging Long Horizons via Dynamical Guidance cites this paper.

Pivot-Centric Trajectory Prediction: Bridging Long Horizons via Dynamical Guidance MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 2020

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source=pdf_text observed=2026-08-05T17:29:21.061148Z digest=sha256:1f10ac8e9b3a79944e867c346c9c8b489a62503f061a6e0e101afdebc8895d5e