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Paper Citation Record · LEDGER

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS

As of 22 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2501.00242.

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

pith.paper-citation-record.v1
2501.00242 v3

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:00:13.810625Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

19 of 19 outbound references displayed

  • verified exact0
  • verified fuzzy17
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8a0244b7-239f-49c5-bea7-cf434932ecef · outbound

This paper cites Autonomous vehicles and intelligent automation: Applications, chal- lenges, and opportunities,.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS Autonomous vehicles and intelligent automation: Applications, chal- lenges, and opportunities,

Reference 1

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation c3d37912-b16b-4036-abf8-0fb9eba45cfa · outbound

This paper cites Mercedes-Benz backs redundancy for safe conditionally automated driving,.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS Mercedes-Benz backs redundancy for safe conditionally automated driving,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:00:14.152746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 6502be94-0fb3-49f3-a0bc-74cf61e90eac · outbound

This paper cites A Commute in Data: The comma2k19 Dataset.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS A Commute in Data: The comma2k19 Dataset

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-10T23:00:13.730502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 11d94bc9-8e12-4bed-a40a-2c59d6395682 · outbound

This paper cites An integrated reduced inertial sensor system - RISS / GPS for land vehicle,.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS An integrated reduced inertial sensor system - RISS / GPS for land vehicle,

Reference 4

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation a7021f73-c0d4-4e92-aaff-ecc7042b02ac · outbound

This paper cites A High-rate, Heterogeneous Data Set From The DARPA Urban Chal- lenge,.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS A High-rate, Heterogeneous Data Set From The DARPA Urban Chal- lenge,

Reference 5

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation b27f5523-f6f0-4a56-bad9-b463516bb76b · outbound

This paper cites Ford Campus vision and lidar data set,.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS Ford Campus vision and lidar data set,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:00:14.096830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation a944c1e4-eb29-4077-a618-bfc0137b2e38 · outbound

This paper cites Complex urban dataset with multi-level sensors from highly diverse urban environments,.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS Complex urban dataset with multi-level sensors from highly diverse urban environments,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:00:14.080349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 8d29397e-877d-4841-bb82-489a9ad435c3 · outbound

This paper cites A Multi-sensor Traffic Scene Dataset with Omnidirectional Video,.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS A Multi-sensor Traffic Scene Dataset with Omnidirectional Video,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:00:14.062622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T23:00:13.756334Z digest=sha256:d25ff19878646467189256066cad05849290e124f2571befa4b6a2a4837deb74

Observation d79da56b-eeeb-4a78-9cba-81a362f45b6b · outbound

This paper cites Gnss/multisensor fusion using continuous-time factor graph optimization for robust localization,.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS Gnss/multisensor fusion using continuous-time factor graph optimization for robust localization,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:00:14.045725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 1f6244e9-6602-40a7-a851-c9c7cb42d646 · outbound

This paper cites nuScenes: A Multimodal Dataset for Autonomous Driv- ing,.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS nuScenes: A Multimodal Dataset for Autonomous Driv- ing,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:00:14.025905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 2dac270b-fbda-4a60-9b1d-08645674dddf · outbound

This paper cites A2D2: Audi Autonomous Driving Dataset.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS A2D2: Audi Autonomous Driving Dataset

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-10T23:00:13.770948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 2586c86b-ac89-4627-a40a-7c5c1899be4e · outbound

This paper cites Noureldin, T.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS Noureldin, T

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:00:14.008840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 1efe7102-c834-4600-a1b4-426669ea3b64 · outbound

This paper cites GNSS/IMU/ODO/LiDAR-SLAM Integrated Navigation System Using IMU/ODO Pre-Integration,.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS GNSS/IMU/ODO/LiDAR-SLAM Integrated Navigation System Using IMU/ODO Pre-Integration,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:00:13.992513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 36989ec5-102b-426e-ab02-0465c84dc7ac · outbound

This paper cites An Enhanced Error Model for EKF-Based Tightly-Coupled Integration of GPS and Land Vehicle’s Motion Sensors,.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS An Enhanced Error Model for EKF-Based Tightly-Coupled Integration of GPS and Land Vehicle’s Motion Sensors,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:00:13.973918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T23:00:13.785479Z digest=sha256:365259533026c94a16b6f785a52cf88599436dbb03e2b537c0361d6f903b9efe

Observation 51b266a7-4dc2-42a1-8941-60613c312aec · outbound

This paper cites Stereo Vision-Based Seman- tic 3D Object and Ego-Motion Tracking for Autonomous Driving,.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS Stereo Vision-Based Seman- tic 3D Object and Ego-Motion Tracking for Autonomous Driving,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:00:13.955672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T23:00:13.790882Z digest=sha256:6dc5f80242582995ed650a0d406f4c87f74490633322f7bfb3f61de489bda553

Observation e75f30f8-c75e-416a-89c7-17eb54e0466a · outbound

This paper cites To- ward Land Vehicle Ego-Velocity Estimation Using Deep Learning and Automotive Radars,.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS To- ward Land Vehicle Ego-Velocity Estimation Using Deep Learning and Automotive Radars,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:00:13.938579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T23:00:13.796046Z digest=sha256:d88a3885c8cc72ed9140b2ae0088acc74b8c9ca586939ee7a50d659fcc841f18

Observation 89f7ff70-341d-4baf-bb24-2fc50b142edc · outbound

This paper cites Reif, Ed., Brakes, Brake Control and Driver Assis- tance Systems: Function, Regulation and Components.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS Reif, Ed., Brakes, Brake Control and Driver Assis- tance Systems: Function, Regulation and Components

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:00:13.921316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T23:00:13.800848Z digest=sha256:b708a5d892ae2d3384084223f9aaf66a2297c0a119cf0380f361ca15fddb4253

Observation cd4b03e7-091c-416b-bb19-56c77da92ce2 · outbound

This paper cites Integration of Topological Maps with GNSS and Onboard Sensors for Robust Land Vehicle Navigation,.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS Integration of Topological Maps with GNSS and Onboard Sensors for Robust Land Vehicle Navigation,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:00:13.903970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-10T23:00:13.805736Z digest=sha256:cad229dddd9bc655c36096c3e18f76a83c9c566a66fd93b2ba93f060613be048

Observation 53e407ce-999a-4cc9-ba95-1528196415c3 · outbound

This paper cites The Utilization of DNN-based Semantic Segmenta- tion for Improving Low-Cost Integrated Stereo Visual Odometry in Challenging Urban Environments,.

Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS The Utilization of DNN-based Semantic Segmenta- tion for Improving Low-Cost Integrated Stereo Visual Odometry in Challenging Urban Environments,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:00:13.886491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Pith citing papers

No inbound Pith citation observations are available.