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Paper Citation Record · LEDGER

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment

As of 18 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2509.22550.

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

pith.paper-citation-record.v1
2509.22550 v4

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:53:36.858537Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

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

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

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Outbound references

Observation b8e90b84-50de-47c5-9432-b9d102872a39 · outbound

This paper cites A survey of autonomous driving: Common practices and emerging technologies,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment A survey of autonomous driving: Common practices and emerging technologies,

Reference 1

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Observation aabc7c65-9daa-4013-b5f3-7e7e80f9dcea · outbound

This paper cites Toward human-vehicle col- laboration: Review and perspectives on human-centered collaborative automated driving,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Toward human-vehicle col- laboration: Review and perspectives on human-centered collaborative automated driving,

Reference 2

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Observation 24f008ae-2d65-4b81-8458-e5bbbc5da305 · outbound

This paper cites Milestones in autonomous driving and intelligent vehicles: Survey of surveys,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Milestones in autonomous driving and intelligent vehicles: Survey of surveys,

Reference 3

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Observation 6be47954-f56a-442e-9fac-c37b1036a996 · outbound

This paper cites Planning and decision- making for autonomous vehicles,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Planning and decision- making for autonomous vehicles,

Reference 4

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Observation b987b3c2-bddf-4969-bc83-3e84c77a6300 · outbound

This paper cites A model for the structure of lane-changing decisions,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment A model for the structure of lane-changing decisions,

Reference 5

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Observation fe550261-0bec-471b-83d8-cc823cddb966 · outbound

This paper cites Recent developments and research needs in modeling lane changing,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Recent developments and research needs in modeling lane changing,

Reference 6

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Observation d9bdd9c8-eeb7-4be3-98b5-7afd79ce3a6f · outbound

This paper cites Review of lane-changing maneuvers of connected and automated vehicles: models, algorithms and traffic impact analyses,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Review of lane-changing maneuvers of connected and automated vehicles: models, algorithms and traffic impact analyses,

Reference 7

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Observation 345d1b5f-6eb0-45ec-9036-b3d61cb2ec34 · outbound

This paper cites Inverse reinforcement learning based: Segmented lane-change trajectory planning with consideration of interactive driving intention,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Inverse reinforcement learning based: Segmented lane-change trajectory planning with consideration of interactive driving intention,

Reference 8

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Observation 5c71dab8-444f-496d-b4a7-a58ea6267416 · outbound

This paper cites Probabilistic prediction of inter- active driving behavior via hierarchical inverse reinforcement learning,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Probabilistic prediction of inter- active driving behavior via hierarchical inverse reinforcement learning,

Reference 9

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Observation 1fa26e5a-4bdb-448d-84b7-a0817cde04e9 · outbound

This paper cites A cooperation- aware lane change method for automated vehicles,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment A cooperation- aware lane change method for automated vehicles,

Reference 10

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Observation 1761bb72-f3b4-49a3-bf5c-6fbe1d84c45f · outbound

This paper cites Factors affecting lane change crashes,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Factors affecting lane change crashes,

Reference 11

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Observation c76cb82e-f6a4-46ee-8933-dce6aa7ba6a9 · outbound

This paper cites Learning driving styles for autonomous vehicles from demonstration,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Learning driving styles for autonomous vehicles from demonstration,

Reference 12

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Observation c9e47152-3787-4c37-87ca-c9aef8b27736 · outbound

This paper cites General lane-changing model mobil for car-following models,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment General lane-changing model mobil for car-following models,

Reference 13

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Observation 30d0050e-7f75-419a-9ef6-91c34139d2e4 · outbound

This paper cites An intelli- gent lane-changing behavior prediction and decision-making strategy for an autonomous vehicle,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment An intelli- gent lane-changing behavior prediction and decision-making strategy for an autonomous vehicle,

Reference 14

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Observation 358ff1a5-bdea-4aef-897e-379d3eb6edb2 · outbound

This paper cites Lane change decision- making through deep reinforcement learning with rule-based con- straints,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Lane change decision- making through deep reinforcement learning with rule-based con- straints,

Reference 15

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source=pdf_text observed=2026-08-04T14:53:33.884736Z digest=sha256:29cd5fe3494e8c416b007fb9a144a9afc6c15967eafde149d3a978c4da723083

Observation 6d516ed6-9c30-4d4c-a723-a59c08fc8cd5 · outbound

This paper cites Rule-based safety-critical control design using control barrier functions with application to au- tonomous lane change,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Rule-based safety-critical control design using control barrier functions with application to au- tonomous lane change,

Reference 16

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Observation 390c6a1b-426b-4dd7-bac9-ff070c4d960d · outbound

This paper cites Using support vector machines for lane-change detection,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Using support vector machines for lane-change detection,

Reference 17

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Observation 981c1633-b974-4d94-94b2-4884a3069671 · outbound

This paper cites Lane changing prediction at highway lane drops using support vector machine and artificial neural network classi- fiers,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Lane changing prediction at highway lane drops using support vector machine and artificial neural network classi- fiers,

Reference 18

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Observation 7599f2db-29d6-4648-ac47-8ba88a62a077 · outbound

This paper cites A deep learning method for lane changing situation assessment and decision making,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment A deep learning method for lane changing situation assessment and decision making,

Reference 19

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Observation ca16746a-b04a-457e-b784-82e1bf9e29dd · outbound

This paper cites A vehicle driving intention prediction method based on gated dual tower transformer model for autonomous driving,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment A vehicle driving intention prediction method based on gated dual tower transformer model for autonomous driving,

Reference 20

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Observation c1fd117b-b0da-40c5-beb1-2c9123b10f50 · outbound

This paper cites Combining planning and deep reinforcement learning in tactical decision making for autonomous driving,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Combining planning and deep reinforcement learning in tactical decision making for autonomous driving,

Reference 21

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Observation 3101b87a-3c77-494f-8c25-ae99f6c1bda3 · outbound

This paper cites Human-like autonomous car-following model with deep reinforcement learning,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Human-like autonomous car-following model with deep reinforcement learning,

Reference 22

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Observation c677de9d-c48e-4bd8-bd4a-3dab7262d484 · outbound

This paper cites A survey of inverse reinforcement learning: Challenges, methods and progress,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment A survey of inverse reinforcement learning: Challenges, methods and progress,

Reference 23

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Observation 510b1f9f-9893-4467-82c6-4868a0b718ef · outbound

This paper cites Driving behavior modeling using natu- ralistic human driving data with inverse reinforcement learning,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Driving behavior modeling using natu- ralistic human driving data with inverse reinforcement learning,

Reference 24

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Observation c47cbab0-c187-41d6-a91e-7576af796392 · outbound

This paper cites Improved car-following strategy based on merging behavior prediction of adjacent vehicle from natural- istic driving data,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Improved car-following strategy based on merging behavior prediction of adjacent vehicle from natural- istic driving data,

Reference 25

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Observation 2b884dea-fdb1-4ca7-9982-2671fb3ac083 · outbound

This paper cites Dual transformer based prediction for lane change intentions and trajectories in mixed traffic environment,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Dual transformer based prediction for lane change intentions and trajectories in mixed traffic environment,

Reference 26

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Observation d76718e0-5e6c-4ff3-a8d0-7ab6572c6758 · outbound

This paper cites Lc-rss: A lane- change responsibility-sensitive safety framework based on data-driven lane-change prediction,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Lc-rss: A lane- change responsibility-sensitive safety framework based on data-driven lane-change prediction,

Reference 27

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Observation acf77f57-9119-4bd1-8a92-8b91e92f7939 · outbound

This paper cites Socially-Aware Autonomous Driving: Inferring Yielding Intentions for Safer Interactions.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Socially-Aware Autonomous Driving: Inferring Yielding Intentions for Safer Interactions

Reference 28

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Observation fa3aafa5-ca6c-43a0-a1eb-d4ac69d46f28 · outbound

This paper cites A novel lane change decision-making model of autonomous vehicle based on support vector machine,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment A novel lane change decision-making model of autonomous vehicle based on support vector machine,

Reference 29

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Observation 3e646a19-eb92-40ef-8c26-9a04d84d93c9 · outbound

This paper cites Personalized driver/vehicle lane change models for adas,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Personalized driver/vehicle lane change models for adas,

Reference 30

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Observation 08ff5959-2027-4d12-8d2e-ee3e8ac15ae5 · outbound

This paper cites An inverse reinforcement learning approach for customizing automated lane change systems,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment An inverse reinforcement learning approach for customizing automated lane change systems,

Reference 31

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Observation d945ebea-1d42-4c94-b22b-2ba0a87badb4 · outbound

This paper cites Driving with style: Inverse reinforcement learning in general-purpose planning for automated driving,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Driving with style: Inverse reinforcement learning in general-purpose planning for automated driving,

Reference 32

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Observation 0187e3a5-5626-4f9f-8721-ee1a0b9c299c · outbound

This paper cites Personalized lane-change as- sistance system with driver behavior identification,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Personalized lane-change as- sistance system with driver behavior identification,

Reference 33

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Observation 0c5dd0a7-6d96-420a-beb8-a56808b74258 · outbound

This paper cites Human-like lane-change control strategy for connected and autonomous vehicles to improve interactions with human- driven vehicles,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Human-like lane-change control strategy for connected and autonomous vehicles to improve interactions with human- driven vehicles,

Reference 34

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Observation c0bc7fd0-0e36-4699-a08b-0b5f778a50ba · outbound

This paper cites Interstate 80 freeway dataset,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Interstate 80 freeway dataset,

Reference 35

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source=pdf_text observed=2026-08-04T14:53:35.548818Z digest=sha256:73f6aa7f5532ce474b26e3f4e173b723059e9af169001f1e6bcb5e7d60999a58

Observation c9a3ede3-c963-4f4e-863c-de3a087a215d · outbound

This paper cites Analysing and modelling of discretionary lane change duration considering driver heterogeneity,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Analysing and modelling of discretionary lane change duration considering driver heterogeneity,

Reference 36

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Observation e12b4110-aaf1-4e11-89e9-2882fa0ec2ce · outbound

This paper cites Modeling duration of lane changes,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Modeling duration of lane changes,

Reference 37

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source=pdf_text observed=2026-08-04T14:53:35.751297Z digest=sha256:0e160f981a26c4f29c0af5f030ef45ae2a2e9bdb12d15988cedf06c959298547

Observation 4227435b-e11f-420e-ace2-424df2c7dd5a · outbound

This paper cites Maximum entropy inverse reinforcement learning.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Maximum entropy inverse reinforcement learning

Reference 38

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source=pdf_text observed=2026-08-04T14:53:35.872707Z digest=sha256:2b25c78e5664f211cdda6fc5dd6bde3e7d896d07564b484022f8562ac184d4e9

Observation d7140f51-8d4d-4285-98f7-7b42c7ae7b0b · outbound

This paper cites Driving style- aware car-following considering cut-in tendencies of adjacent vehicles with inverse reinforcement learning,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Driving style- aware car-following considering cut-in tendencies of adjacent vehicles with inverse reinforcement learning,

Reference 39

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source=pdf_text observed=2026-08-04T14:53:35.960621Z digest=sha256:926e9b64aff67d9076f8dc7f7d19d7079876d72bf23fac9369d77f7968f8b18f

Observation 9c5559f7-accd-4c8c-ad99-3ad335e18663 · outbound

This paper cites Trajectory reference generation and guidance control for autonomous vehicle lane change maneuver,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Trajectory reference generation and guidance control for autonomous vehicle lane change maneuver,

Reference 40

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source=pdf_text observed=2026-08-04T14:53:36.112612Z digest=sha256:f0b2ec0a5df8f85acaa0fd60bae8fef4f47c947465aa015329cfaf28621ae82b

Observation bb9f41eb-0029-47b7-9d58-5cfd80d65bb8 · outbound

This paper cites UDMC: Unified decision-making and control framework for urban autonomous driving with motion prediction of traffic participants,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment UDMC: Unified decision-making and control framework for urban autonomous driving with motion prediction of traffic participants,

Reference 41

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source=pdf_text observed=2026-08-04T14:53:36.244134Z digest=sha256:5b6ebad2557797be8e51fc2d35f883b7615e68b120cf6a38254aeda7e57660dc

Observation 7dff909b-bcf8-489b-90c4-c9d374cc1a61 · outbound

This paper cites Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity,

Reference 42

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source=pdf_text observed=2026-08-04T14:53:36.340880Z digest=sha256:b82a6d65aa3f34daf6062d67e09bc1fe53118a24aa596cce692e5f124c2fa34c

Observation 7e41d861-a26a-4c94-b20f-6d61729a500f · outbound

This paper cites Online prediction of lane change with a hierarchical learning- based approach,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Online prediction of lane change with a hierarchical learning- based approach,

Reference 43

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source=pdf_text observed=2026-08-04T14:53:36.489531Z digest=sha256:5f2ea96a72c88c6d64a5708e4c7edf188322c4f33da4279a08218ee042f66f9e

Observation cb786ae0-59d6-4423-a045-a56366a305a7 · outbound

This paper cites High-level decision making for automated highway driving via behavior cloning,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment High-level decision making for automated highway driving via behavior cloning,

Reference 44

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Observation 683b2910-359d-4e27-9424-9d063db4356f · outbound

This paper cites Variable weight combination model for lane-changing prediction of human-driven vehicle,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Variable weight combination model for lane-changing prediction of human-driven vehicle,

Reference 45

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source=pdf_text observed=2026-08-04T14:53:36.798622Z digest=sha256:7e770917d1feff8f0aa5c28e0e16a370dba5afd79d99a51170aa08f99fed1730

Observation 1516c91b-229e-4477-a07d-6d997cb6d928 · outbound

This paper cites Deepsignals: Predicting intent of drivers through visual signals,.

An Intention-driven Lane Change Framework Considering Heterogeneous Dynamic Cooperation in Mixed-traffic Environment Deepsignals: Predicting intent of drivers through visual signals,

Reference 46

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source=pdf_text observed=2026-08-04T14:53:36.858537Z digest=sha256:ee480ced80bdbd69bf71ddb1bfb0595c033785b613ef5c5147fd0b3b4bd2ca86

Pith citing papers

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