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Paper Citation Record · LEDGER

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges

As of 23 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:1907.09454.

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

pith.paper-citation-record.v1
1907.09454 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-24T20:31:24.162095Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

37 of 37 outbound references displayed

  • verified exact5
  • verified fuzzy32
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 129af78c-188a-4db4-81ed-998daee882cb · outbound

This paper cites Fog computing and its role in the internet of things.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Fog computing and its role in the internet of things

Reference 1

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raw_fallback, observed 2026-05-24T20:36:23.254634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:a81be1c09760cad740793f4d9a72c340eb0a296b45a87612658d319b274fe38c

Observation b1bb7ff3-654e-4210-9550-03d2fb9191a1 · outbound

This paper cites Shaping the digital twin for design and production engineering.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Shaping the digital twin for design and production engineering

Reference 2

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raw_fallback, observed 2026-05-24T20:36:23.261243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:2293534d35fb9e6f087f5a92f4557ef6a476eda746a4fb1c47b82837e3a1263c

Observation f20afe9b-0a14-4c7e-b6c9-9a8eb4633681 · outbound

This paper cites Fog com- puting: Principles, architectures, and applications.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Fog com- puting: Principles, architectures, and applications

Reference 3

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raw_fallback, observed 2026-05-24T20:36:23.243823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:ba7549322c2e87d6b4270ac58d972d4ae77f3319bd647c4129abf234942c7cd1

Observation 806bf538-7997-4ccf-928d-ed775456ebe9 · outbound

This paper cites A survey of fog computing: concepts, applications and issues.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges A survey of fog computing: concepts, applications and issues

Reference 4

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raw_fallback, observed 2026-05-24T20:36:23.250797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:1671895fc3a988a3d75296883940d133010269cd2d2492889474aa328c4716b7

Observation 6b24288d-e333-443a-af5c-27ebf710923e · outbound

This paper cites Edge computing: Vision and challenges.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Edge computing: Vision and challenges

Reference 5

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raw_fallback, observed 2026-05-24T20:36:23.257969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:4549ed46f051b5ed884d9d0cf969e0d5d2f5136aaab83b7ec62e727c30e25b30

Observation c3c1a93f-630c-4e41-9963-4ae7c3bac3fb · outbound

This paper cites Fog Computing: Focusing on Mobile Users at the Edge.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Fog Computing: Focusing on Mobile Users at the Edge

Reference 6

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verified exact
local_arxiv, observed 2026-05-24T20:34:53.185859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:17a6686f1eceeb902e3c6388853d24a5d113cf84dc0c084e3eeacbd188c8eca8

Observation 678c2ef1-0f85-4caa-bb29-2f279acfdbad · outbound

This paper cites Fog computing architecture to enable con- sumer centric internet of things services.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Fog computing architecture to enable con- sumer centric internet of things services

Reference 7

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raw_fallback, observed 2026-05-24T20:36:23.247865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:3ec2d4f80e85f552e5ca55d7d9b45456e4bfa80cb16d45862324c051c76ad0b0

Observation 3246e60e-55a4-4e48-b9be-d9f3f10fa139 · outbound

This paper cites Mobile edge comput- ingâĂŤa key technology towards 5g.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Mobile edge comput- ingâĂŤa key technology towards 5g

Reference 8

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raw_fallback, observed 2026-05-24T20:36:23.232617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:2f8b957d16e9dea4b2c01ceb968a8288336f6e01ad5f0b629915ccd5329b3117

Observation 5434198f-b35c-4aab-97ce-d0a67a0b1653 · outbound

This paper cites Multi-access edge computing: The driver behind the wheel of 5g-connected cars.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Multi-access edge computing: The driver behind the wheel of 5g-connected cars

Reference 9

Resolution
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raw_fallback, observed 2026-05-24T20:36:23.236146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:8a180f66bf3d30e1673cfc6d81e546e06b978a9ffec67459abc0eba2f2b8f547

Observation c5bb020e-be3a-4d89-8528-16766d8fb89e · outbound

This paper cites Digital Twins: The Convergence of Multimedia Technologies.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Digital Twins: The Convergence of Multimedia Technologies

Reference 10

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raw_fallback, observed 2026-05-24T20:36:23.240015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:bb5ac3e6173124f824f1b3de9c99468a3d40d4a61ec2a160d9d0650a1cbb49e8

Observation 3a1cf440-7ed1-4079-b4c1-ce6b990fa676 · outbound

This paper cites Mind + Machine: What is a Digital Twin?.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Mind + Machine: What is a Digital Twin?

Reference 11

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raw_fallback, observed 2026-05-24T20:36:23.228677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:29a0c4433ebf59d612133c83971ce36078c95ce41a1fb90acebac4133a5f3cfa

Observation 56551ea7-6bd3-4264-8266-70d304867c29 · outbound

This paper cites Representing Industrial Data Streams in Digital Twins Using Semantic Labeling.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Representing Industrial Data Streams in Digital Twins Using Semantic Labeling

Reference 12

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raw_fallback, observed 2026-05-24T20:36:23.213859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:59ced1315261972d137865fd0553b600d13177be877be9d038904a5cd8959ed8

Observation daf36a60-76d1-4af5-b63c-73643a6f8afd · outbound

This paper cites Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems

Reference 13

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raw_fallback, observed 2026-05-24T20:36:23.210070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:fbcb0f2a058976e686d37c636f6beb1b75a13e23e49e3c18f5aa5666641825b0

Observation e25f2b1b-cbdb-4594-99ff-94971e9e9d59 · outbound

This paper cites Digital twin-driven product design, manufacturing and service with big data.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Digital twin-driven product design, manufacturing and service with big data

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-05-24T20:36:23.196748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:c8a563f8cce4617d4f28ad47aa4e91f74c05e40b2fc9ca4c445b54ec032cc372

Observation 32e1295a-c5b7-4a33-95fa-efa202e1035f · outbound

This paper cites Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems

Reference 15

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raw_fallback, observed 2026-05-24T20:36:23.201841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:f640d090672e396f99ba51dcfd81c75b31be2557a14817eb1e040c9ebd0d1f51

Observation 9e91105e-2a46-4440-967f-8e2fbc50a378 · outbound

This paper cites The digital twin paradigm for future nasa and us air force vehicles.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges The digital twin paradigm for future nasa and us air force vehicles

Reference 16

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raw_fallback, observed 2026-05-24T20:36:23.188587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:4d1d0f1ab7c0081615f33c3ea0f929ae35df45fdd7f1339ca87ae9ee03ec5240

Observation 637333d5-4d47-472b-b9fa-ca6d917f8640 · outbound

This paper cites Gelernter, Mirror worlds: Or the day software puts the universe in a shoebox.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Gelernter, Mirror worlds: Or the day software puts the universe in a shoebox

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:4f8f86e7664336ec6453772b903ccef1e4fa302069a701f936a39e8be2c95477

Observation fb626234-ec8c-4748-8383-414c86c7344d · outbound

This paper cites Multimedia mashups for mirror worlds.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Multimedia mashups for mirror worlds

Reference 18

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raw_fallback, observed 2026-05-24T20:36:23.192406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:d2606741a2cdbd9db369382fbd1698f458b7691eef8d6aafa7541fbb03d1f659

Observation 477905b7-fa16-42e3-b46e-de18ff79e7be · outbound

This paper cites Vehicle-to-vehicle wireless communication protocols for enhancing highway traffic safety.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Vehicle-to-vehicle wireless communication protocols for enhancing highway traffic safety

Reference 19

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raw_fallback, observed 2026-05-24T20:36:23.205792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:7b5eb35d1f12cae4f3f311c87606f6cd9cb182b6d75b704c548d1208580a7264

Observation 3231a0fb-babc-447c-a241-19b56b442c0a · outbound

This paper cites Driving behaviors: Models and challenges for non-lane based mixed traffic.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Driving behaviors: Models and challenges for non-lane based mixed traffic

Reference 20

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raw_fallback, observed 2026-05-24T20:36:23.217266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:36e4d17e44f4e956f3e6f9269ad4f2c147748c4f2728dd7ffc48304d200a0b4e

Observation a2649e28-0061-4650-9440-e66967b840c9 · outbound

This paper cites An approach to design of feedback systems with time delay.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges An approach to design of feedback systems with time delay

Reference 21

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raw_fallback, observed 2026-05-24T20:36:23.175599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:9a4eb49a8c49a2d13d0451e7eb19b376c1cbcc666d5906f49d8d89a73d99fa35

Observation 430c8546-f6df-453a-b8c9-5fb5779d6d47 · outbound

This paper cites Implementing fault-tolerant services using the state machine approach: A tutorial.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Implementing fault-tolerant services using the state machine approach: A tutorial

Reference 22

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raw_fallback, observed 2026-05-24T20:36:23.171669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:c1b460edd5bcc7ea15558867597504cb97cd7837b054ed8efaac48bfcb514244

Observation 47d024ac-8714-41ed-b740-09842f008c28 · outbound

This paper cites Convolutional social pooling for vehicle trajectory prediction.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Convolutional social pooling for vehicle trajectory prediction

Reference 23

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raw_fallback, observed 2026-05-24T20:36:23.180696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:d73016034027c556b4dae54593cfa91a97cc5be8b84b98c0239b05eebbeb7646

Observation 2cbe07d4-4077-44b8-8ecc-6b48e4b7bbe3 · outbound

This paper cites The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems

Reference 24

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raw_fallback, observed 2026-05-24T20:36:23.220829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:2301157a08c7adca56acaf2bd8eb8e122e4bf4bd695cc9ced9a54948386b126f

Observation baca3339-c57a-45b4-9576-8f158522794e · outbound

This paper cites Xgboost: A scalable tree boosting system.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Xgboost: A scalable tree boosting system

Reference 25

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raw_fallback, observed 2026-05-24T20:36:23.225097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:fe26379dfd59a479cee64a1d8ffecc917dcd2284f313d78b97a17e6422bd4cba

Observation bd665729-c696-4b66-83d9-7a2eb2246f22 · outbound

This paper cites PI-Edge: A Low-Power Edge Computing System for Real-Time Autonomous Driving Services.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges PI-Edge: A Low-Power Edge Computing System for Real-Time Autonomous Driving Services

Reference 26

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local_arxiv, observed 2026-05-24T20:34:53.175450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:258b06214f8154a3420dc3e9297c66adb0c79e5a4763982bb92e8e7e2f5fc306

Observation 2ab1ebee-b807-48fb-8eb6-cbe4e49aac7d · outbound

This paper cites Design of distributed cyber– physical systems for connected and automated vehicles with implementing method- ologies.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Design of distributed cyber– physical systems for connected and automated vehicles with implementing method- ologies

Reference 27

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raw_fallback, observed 2026-05-24T20:36:23.156054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:799349ebf2f1750703c5e9fc6207939ad02205d4b1441d7936f68d6f3f31c620

Observation fd5c53b1-8e3b-4b1d-a765-b2d685568edd · outbound

This paper cites Infrastructure enabled autonomy: A distributed intelligence architecture for autonomous vehicles.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Infrastructure enabled autonomy: A distributed intelligence architecture for autonomous vehicles

Reference 28

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raw_fallback, observed 2026-05-24T20:36:23.168217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:34537cf2b00eb44a833a06486b0860f10bb2d454ba0bdce71ad026db325a826a

Observation 83a5714c-dd09-4705-85d3-b4836f1458de · outbound

This paper cites SDN-Based Resource Management for Autonomous Vehicular Networks: A Multi-Access Edge Computing Approach.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges SDN-Based Resource Management for Autonomous Vehicular Networks: A Multi-Access Edge Computing Approach

Reference 29

Resolution
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local_arxiv, observed 2026-05-24T20:34:53.190352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:4867b2e7c46702d57cd9e28cb2b9b079e9888aa1c7716255d74df5da952b9590

Observation 16ebe2c9-78ec-4048-af65-e5eb98e93a75 · outbound

This paper cites A novel digital twin-centric approach for driver intention prediction and traffic congestion avoidance.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges A novel digital twin-centric approach for driver intention prediction and traffic congestion avoidance

Reference 30

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verified fuzzy
raw_fallback, observed 2026-05-24T20:36:23.160034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:279e2609bea0bf0d3cdab43441e27f1bd69d9a83649e6b1762914edb63426202

Observation f8fda749-85fc-448f-8c4d-5501e491ae8d · outbound

This paper cites Digital behavioral twins for safe connected cars.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Digital behavioral twins for safe connected cars

Reference 31

Resolution
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raw_fallback, observed 2026-05-24T20:36:23.151476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:865f38e09b8f9114c9c8b7aa06fea0d09e5c9208d36f126ed8d5312d3035228b

Observation a041f93f-a384-4559-ac6e-ae8a52ef81a0 · outbound

This paper cites An Empirical Evaluation of Deep Learning on Highway Driving.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges An Empirical Evaluation of Deep Learning on Highway Driving

Reference 32

Resolution
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local_arxiv, observed 2026-05-24T20:34:53.180475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:d82c9966e4f9d994b2711aaf72deadb1f19873e109767be8f034afac4a882ce8

Observation 0e159ba8-b694-481f-806a-7a0b6160486e · outbound

This paper cites A survey on motion prediction and risk assessment for intelligent vehicles.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges A survey on motion prediction and risk assessment for intelligent vehicles

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-05-24T20:36:23.164566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:e5aff4c672c58eb3e7c9fa78814f21ae47b9f815d8ceb8c177efa66c05978d7d

Observation 9102cea5-33fa-447c-83ed-f840470cf3df · outbound

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

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges Multi-modal trajectory prediction of surrounding vehicles with maneuver based lstms

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-05-24T20:36:23.144405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:e6b5bd5170ddca20238ac7b48c7946c79d094c8f7be05fef7392b21dc6024c96

Observation 8a2b7f0f-326f-45d3-8da6-7bd107d980eb · outbound

This paper cites How would surround vehicles move? a unified framework for maneuver classification and motion prediction.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges How would surround vehicles move? a unified framework for maneuver classification and motion prediction

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-05-24T20:36:23.148193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:debac825639cd26049033f3d0c4fba3c9f0f8d516b4999f67b702b30a98593ad

Observation 2028959f-f0b6-4a6f-b646-dc92ce19e4de · outbound

This paper cites A programming language and system for heterogeneous cloud of things.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges A programming language and system for heterogeneous cloud of things

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-05-24T20:36:23.140762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:05db4be31bc2ec7cfdd8e768353ce66cb7ab052502c675e6de1b6cd5c5699781

Observation fd8b0d23-e9ed-42ea-a154-8354af1cc45f · outbound

This paper cites A Language for Programming Edge Clouds for Next Generation IoT Applications.

A Fog Computing Framework for Autonomous Driving Assist: Architecture, Experiments, and Challenges A Language for Programming Edge Clouds for Next Generation IoT Applications

Reference 37

Resolution
verified exact
local_arxiv, observed 2026-05-24T20:34:53.170094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T20:31:24.162095Z digest=sha256:01e35e54d1a8cc2d9d84a5a8241b85339153b349d718663152dcfff902bc7972

Pith citing papers

No inbound Pith citation observations are available.