Paper Citation Record · LEDGER

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy

As of 20 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2506.16720.

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

pith.paper-citation-record.v1
2506.16720 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:25:06.815723Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

31 of 31 outbound references displayed

verified exact1
verified fuzzy16
unresolved14
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 433c970f-8f7a-42f3-bef9-8c803be4193e · outbound

This paper cites A crash injury model involving autonomous vehicle: Investigating of crash and disengagement reports,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy A crash injury model involving autonomous vehicle: Investigating of crash and disengagement reports,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:25:07.150966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b22efc79-f784-47ab-9bf9-0f443d472339 · outbound

This paper cites Anti-jerk on-ramp merging using deep reinforcement learning,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Anti-jerk on-ramp merging using deep reinforcement learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:25:07.140004Z

Source-reported events for the cited work

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Observation 4a769e59-d522-458e-8128-d14410edf977 · outbound

This paper cites Identify, estimate and bound the uncertainty of reinforcement learning for autonomous driving,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Identify, estimate and bound the uncertainty of reinforcement learning for autonomous driving,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:25:07.128325Z

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Observation 3b121b92-28bc-420c-b4bc-f9c2af7a6f57 · outbound

This paper cites Generalizing from a few examples: A survey on few-shot learning,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Generalizing from a few examples: A survey on few-shot learning,

Reference 4

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Observation d9514feb-b92a-4394-8fd8-216436924b43 · outbound

This paper cites Autonomous driving policy continual learning with one-shot disen- gagement case,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Autonomous driving policy continual learning with one-shot disen- gagement case,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:25:07.110274Z

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Observation 25525039-dc33-4486-9716-e552afee0509 · outbound

This paper cites Who make drivers stop? towards driver-centric risk assessment: Risk object identification via causal inference,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Who make drivers stop? towards driver-centric risk assessment: Risk object identification via causal inference,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T19:25:06.718440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e71b49c-9070-44c7-b364-27725d663eea · outbound

This paper cites Goal-oriented object importance estimation in on-road driving videos,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Goal-oriented object importance estimation in on-road driving videos,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:25:07.092384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 42548532-94a1-46fe-9dbe-374256757ab0 · outbound

This paper cites Agent-centric risk assessment: Accident anticipation and risky region localization,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Agent-centric risk assessment: Accident anticipation and risky region localization,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T19:25:06.727112Z

Source-reported events for the cited work

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Observation 6894da82-81b6-498f-83c1-bd85d83570f4 · outbound

This paper cites Uncertainty-Aware Reinforcement Learning for Collision Avoidance.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Uncertainty-Aware Reinforcement Learning for Collision Avoidance

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T19:25:06.731199Z

Source-reported events for the cited work

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Observation 704624d6-a5f0-4a33-a006-612947b5cb87 · outbound

This paper cites Confidence-aware reinforcement learning for self-driving cars,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Confidence-aware reinforcement learning for self-driving cars,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:25:07.072254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a2127f82-b70d-4b0a-8b17-653e0b4028b6 · outbound

This paper cites Uncertainty-aware model-based re- inforcement learning: Methodology and application in autonomous driving,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Uncertainty-aware model-based re- inforcement learning: Methodology and application in autonomous driving,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:25:07.059349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3b0cdd5a-8082-4559-b3a1-c6d8021e2029 · outbound

This paper cites Learning task-relevant representations for generalization via characteristic func- tions of reward sequence distributions,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Learning task-relevant representations for generalization via characteristic func- tions of reward sequence distributions,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:25:07.047772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f16a06d1-741a-4c50-b92c-8ae6b2ff8c9b · outbound

This paper cites Uncertainty-based offline variational bayesian reinforcement learning for robustness under di- verse data corruptions,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Uncertainty-based offline variational bayesian reinforcement learning for robustness under di- verse data corruptions,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:25:07.036834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 89bcb1e7-48fc-4757-bc6e-2af87473c339 · outbound

This paper cites Analysis of accident data for test scenario definition in the assess project,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Analysis of accident data for test scenario definition in the assess project,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:25:07.024446Z

Source-reported events for the cited work

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Observation 7473ded0-c5e1-4c2f-8b52-6f66458d5e2f · outbound

This paper cites A comprehensive self-driving car test,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy A comprehensive self-driving car test,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:25:07.012986Z

Source-reported events for the cited work

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Observation 4ed0539e-8085-4470-8844-eaf370866d5a · outbound

This paper cites Congested traffic states in empirical observations and microscopic simulations,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Congested traffic states in empirical observations and microscopic simulations,

Reference 16

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Source-reported events for the cited work

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Observation ec7045b2-276a-4594-a9c0-7efa33d1f237 · outbound

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

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy General lane-changing model mobil for car-following models,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T19:25:06.764312Z

Source-reported events for the cited work

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Observation 7294dab4-19f7-425c-9386-3aa42dcc1604 · outbound

This paper cites Trafficsim: Learning to simulate realistic multi-agent behaviors,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Trafficsim: Learning to simulate realistic multi-agent behaviors,

Reference 18

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Observation 571ff858-837b-478c-ad1b-bb5c90711746 · outbound

This paper cites Simnet: Learning reactive self-driving simulations from real-world observations,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Simnet: Learning reactive self-driving simulations from real-world observations,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T19:25:06.771907Z

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Observation 98f0a991-b8a7-4812-8fa4-e908bafb28ab · outbound

This paper cites Overview on deepmind and its alphago zero ai,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Overview on deepmind and its alphago zero ai,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:25:06.968006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7928db64-357f-4271-af3c-eee27ab63c1e · outbound

This paper cites Generalization in Visual Reinforcement Learning with the Reward Sequence Distribution.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Generalization in Visual Reinforcement Learning with the Reward Sequence Distribution

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:25:06.853728Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f40060c1-44cd-438e-bf8a-be6d760000a4 · outbound

This paper cites Quan- tifying generalization in reinforcement learning,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Quan- tifying generalization in reinforcement learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:25:06.956823Z

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Observation fe7f7d1e-7406-4774-9fd1-f455fdb79e15 · outbound

This paper cites Tra- jectron++: Dynamically-feasible trajectory forecasting with heteroge- neous data,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Tra- jectron++: Dynamically-feasible trajectory forecasting with heteroge- neous data,

Reference 23

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Source-reported events for the cited work

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Observation 28ebbba5-d027-40bd-85b3-d3ad299a7a31 · outbound

This paper cites On estimation of a probability density function and mode,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy On estimation of a probability density function and mode,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T19:25:06.787977Z

Source-reported events for the cited work

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Observation fb5664ee-45ce-4fdd-a5ad-580205436f95 · outbound

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DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Unresolved cited work

Reference 25

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Observation f1d1bac1-74e3-472a-90f0-556e791050d5 · outbound

This paper cites Lane change and merge maneuvers for connected and automated vehicles: A survey,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Lane change and merge maneuvers for connected and automated vehicles: A survey,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:25:06.924804Z

Source-reported events for the cited work

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Observation 7e838601-1564-42a2-8c43-12da132203b3 · outbound

This paper cites A comprehensive review of the development of adaptive cruise control systems,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy A comprehensive review of the development of adaptive cruise control systems,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:25:06.912113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c74b6642-bfa4-4c4d-9bc2-6994e3f85526 · outbound

This paper cites Application of microscopic pedestrian simulation model,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Application of microscopic pedestrian simulation model,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:25:06.900334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7393ea60-bc97-4aad-9965-43c3e4c2a5bc · outbound

This paper cites Layered costmaps for context-sensitive navigation,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Layered costmaps for context-sensitive navigation,

Reference 29

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Observation e4c67fca-aeb3-490b-ae0f-edda9035bdc6 · outbound

This paper cites Carla: An open urban driving simulator,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Carla: An open urban driving simulator,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T19:25:06.811756Z

Source-reported events for the cited work

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Observation a74d3d00-df2a-49c8-890d-c1e1fcb1e1d8 · outbound

This paper cites Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,.

DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T19:25:06.815723Z

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