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Paper Citation Record · LEDGER

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles

As of 16 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2505.07855.

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

pith.paper-citation-record.v1
2505.07855 v2

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:19:11.416120Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

25 of 25 outbound references displayed

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  • verified fuzzy23
  • unresolved2
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4effe293-1a2f-425f-9226-2869f5fa401c · outbound

This paper cites Fail-safe motion planning of autonomous vehicles,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Fail-safe motion planning of autonomous vehicles,

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation f788491a-0cfc-4990-9b1d-898ad7cfe58b · outbound

This paper cites A dynamic motion planning framework for autonomous driving in urban environments,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles A dynamic motion planning framework for autonomous driving in urban environments,

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 12d0ccd3-d9a2-4b3f-881e-5c6fe05168c0 · outbound

This paper cites Occupancy Grids: A Stochastic Spatial Representation for Active Robot Perception.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Occupancy Grids: A Stochastic Spatial Representation for Active Robot Perception

Reference 3

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 56c554d6-b490-4fde-bc98-22842072c15f · outbound

This paper cites From probabilistic occupancy grids to versatile collision avoidance using predictive collision detection,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles From probabilistic occupancy grids to versatile collision avoidance using predictive collision detection,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation e7b0a4b3-9b83-44e2-9c7e-2f7350a4a751 · outbound

This paper cites Bayesian occupancy grid mapping via an exact inverse sensor model,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Bayesian occupancy grid mapping via an exact inverse sensor model,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 048b4f49-a3b7-44e3-b06a-f0e3eaa5ff03 · outbound

This paper cites Bayesian learning of occupancy grids,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Bayesian learning of occupancy grids,

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 69315dca-6757-4ad0-af92-1e4ea03fffae · outbound

This paper cites Transitional grid maps: Joint modeling of static and dynamic oc- cupancy,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Transitional grid maps: Joint modeling of static and dynamic oc- cupancy,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation f6b95f65-d327-4543-acc8-d17235381bda · outbound

This paper cites A random finite set approach for dynamic occu- pancy grid maps with real-time application,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles A random finite set approach for dynamic occu- pancy grid maps with real-time application,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 6daba1ba-03f2-4da4-9f07-7ab2d7d015be · outbound

This paper cites A hybrid rule-based and data-driven approach to driver modeling through particle filtering,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles A hybrid rule-based and data-driven approach to driver modeling through particle filtering,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 84fad2f3-25db-4aef-b9c5-c742c17000df · outbound

This paper cites A multi- task recurrent neural network for end-to-end dynamic occupancy grid mapping,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles A multi- task recurrent neural network for end-to-end dynamic occupancy grid mapping,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 5a2560b7-e3db-4541-9aee-b8bf1af6780e · outbound

This paper cites Dynamic occupancy grid prediction for urban autonomous driving: A deep learning approach with fully automatic labeling,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Dynamic occupancy grid prediction for urban autonomous driving: A deep learning approach with fully automatic labeling,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 9a6fff73-0c63-4064-893e-db01bd744065 · outbound

This paper cites Traffic scene prediction via deep learning: Introduction of multi-channel occupancy grid map as a scene representation,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Traffic scene prediction via deep learning: Introduction of multi-channel occupancy grid map as a scene representation,

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation bbdc87c0-bf08-43af-8883-868527b9742a · outbound

This paper cites Mixnet: Physics constrained deep neural motion prediction for autonomous racing,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Mixnet: Physics constrained deep neural motion prediction for autonomous racing,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation ac52bb2d-e02f-4991-ad1e-e9287a2fd0dc · outbound

This paper cites Enhance planning with physics-informed safety controller for end-to-end autonomous driving,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Enhance planning with physics-informed safety controller for end-to-end autonomous driving,

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 79da3c7e-68a3-4fe7-987f-f13052cb34ca · outbound

This paper cites Robust environment perception based on occupancy grid maps for autonomous vehicle,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Robust environment perception based on occupancy grid maps for autonomous vehicle,

Reference 15

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raw_fallback, observed 2026-08-15T23:19:11.584860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 85db1ca0-469a-409a-9288-daafb3863f74 · outbound

This paper cites Modern map inference methods for accurate and fast occupancy grid mapping on higher order factor graphs,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Modern map inference methods for accurate and fast occupancy grid mapping on higher order factor graphs,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation ddca3609-3e76-45ad-a000-ac05953de5c2 · outbound

This paper cites Semantic scene completion from a single depth image,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Semantic scene completion from a single depth image,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation c22e925a-f90d-4770-a4eb-55d0afaa498e · outbound

This paper cites Ofmpnet: Deep end-to-end model for oc- cupancy and flow prediction in urban environment,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Ofmpnet: Deep end-to-end model for oc- cupancy and flow prediction in urban environment,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 1b8416ad-9521-462b-903b-d708957f3846 · outbound

This paper cites Physics- informed trajectory prediction for autonomous driving under missing observation,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Physics- informed trajectory prediction for autonomous driving under missing observation,

Reference 19

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raw_fallback, observed 2026-08-15T23:19:11.539659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation f85bbb8c-3d65-402e-acd9-61d3ce7f9c88 · outbound

This paper cites Occupancy prediction-guided neural planner for autonomous driving,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Occupancy prediction-guided neural planner for autonomous driving,

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 6b01e0db-0d66-43b1-a7b8-bee6abfa84e1 · outbound

This paper cites Occupancy flow fields for motion forecasting in autonomous driving,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Occupancy flow fields for motion forecasting in autonomous driving,

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 538cb0cd-3b29-4914-9d73-711ddc51571f · outbound

This paper cites Road traffic safety assessment in self-driving vehicles based on time-to- collision with motion orientation,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Road traffic safety assessment in self-driving vehicles based on time-to- collision with motion orientation,

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 67e2f3d7-71a4-4465-a9a4-5d394038d856 · outbound

This paper cites A literature review of performance metrics of automated driving systems for on-road vehicles,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles A literature review of performance metrics of automated driving systems for on-road vehicles,

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation b65e0549-04eb-455d-ab3c-b5cff1eedd66 · outbound

This paper cites Jerk-minimized autonomous driving strategy with deep reinforcement learning.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Jerk-minimized autonomous driving strategy with deep reinforcement learning

Reference 24

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raw_fallback, observed 2026-08-15T23:19:11.477053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 5cc4da3a-57a1-49c7-a2e9-533db04f9412 · outbound

This paper cites Reassess- ing desired time headway as a measure of car-following capability: Definition, quantification, and associated factors,.

A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehicles Reassess- ing desired time headway as a measure of car-following capability: Definition, quantification, and associated factors,

Reference 25

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raw_fallback, observed 2026-08-15T23:19:11.463810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Pith citing papers

No inbound Pith citation observations are available.