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Paper Citation Record · LEDGER

Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement

As of 12 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 1 inbound Pith citation observation for arXiv:2412.10822.

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

pith.paper-citation-record.v1
2412.10822 v1

Coverage vector

measured 6 of 6 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:40:41.209608Z

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-17T02:38:27.178243Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T02:38:53.766511Z

Reference resolution

6 of 6 outbound references displayed

  • verified exact0
  • verified fuzzy4
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 75a085ad-5c6f-4a00-b48b-b746fb36489d · outbound

This paper cites Transportation Research Record 1999(1), 86-94.

Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement Transportation Research Record 1999(1), 86-94

Reference 2007

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:40:41.294283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:40:41.195221Z digest=sha256:a6b82865f3c9439467ee8bc1ff4710d9d8346ebdb670cd921763689a8d0a9560

Observation bc9c5f7a-6682-411c-a02f-0c0ce0fcdd58 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement Proximal Policy Optimization Algorithms

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-11T15:40:41.209608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:40:41.209608Z digest=sha256:9108b496a6c451bc13ae74fa3cd1d5b7437b4a36e8613f5225209db86ba03776

Observation 7e0b0e61-691b-4cad-8d7e-712723d65a66 · outbound

This paper cites IEEE, pp.

Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement IEEE, pp

Reference 2018

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:40:41.311112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:40:41.192098Z digest=sha256:a7fe3aad6fe444f6a01d596ef9788c632fb723eff435845564ef77ef86be0cc7

Observation 44fa7758-ec71-4f1d-a802-611039243b21 · outbound

This paper cites IEEE, pp.

Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement IEEE, pp

Reference 2019

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:40:41.274866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:40:41.205503Z digest=sha256:554938cac1881aa2f1e2e87c2fd79eab95b47dc8159f679c03493d05dc91f604

Observation eff42da2-13c9-45bb-a871-56598747269b · outbound

This paper cites IEEE, pp.

Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement IEEE, pp

Reference 2020

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:40:41.327079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T15:40:41.187959Z digest=sha256:bd482c0a9ae2da550a0b83424c86ec004646745ca23c7e3c081ace7b4d1c084b

Observation 904a7ced-b784-4e91-9685-111cab13331b · outbound

This paper cites Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization.

Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-11T15:40:41.198683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T15:40:41.198683Z digest=sha256:511ab8738bcd6dba8faec010caee91cd92324de53ffd32b49cc595274287b605

Pith citing papers

Observation f90e5d89-d3c8-484a-a82e-fece9b795eee · inbound

MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving cites this paper.

MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-17T02:38:53.769025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-17T02:38:27.178243Z digest=sha256:a44435d5f1b5c4be2456bce657795a4d12d0f107cb5367e048cc5f4d58412199