Paper Citation Record · LEDGER

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving

As of 22 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2509.04712.

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

pith.paper-citation-record.v1
2509.04712 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T06:00:50.704572Z

measured 38 of 38 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

38 of 38 outbound references displayed

verified exact2
verified fuzzy24
unresolved12
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ee52a3a2-f5b2-4319-a44f-364d299d5565 · outbound

This paper cites A survey of autonomous driving: Common practices and emerging tech- nologies,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving A survey of autonomous driving: Common practices and emerging tech- nologies,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.509243Z

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.

Observation 0cb7e546-170d-4ba8-8088-1d89d2e43943 · outbound

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

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Lane change and merge maneuvers for connected and automated vehicles: A survey,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.485506Z

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.

Observation c93f8b65-980f-45c8-a51a-d3fb682a2c58 · outbound

This paper cites Automated lane change controller design,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Automated lane change controller design,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.463003Z

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.

Observation 0f9284b2-f756-4e47-8754-778eb9f38f79 · outbound

This paper cites Traffic dynam- ics: studies in car following,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Traffic dynam- ics: studies in car following,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.434134Z

Source-reported events for the cited work

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Observation f33213f1-b4a5-466d-837b-0d1dfb435c13 · outbound

This paper cites A behavioural car-following model for computer simulation,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving A behavioural car-following model for computer simulation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.409275Z

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.

Observation 8b341eb3-75a1-4475-bca7-c7d6930636bc · outbound

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

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Congested traffic states in empirical observations and microscopic simulations,

Reference 6

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Source-reported events for the cited work

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Observation 20015de6-1c5c-4f10-aa39-1b2aa8b98703 · outbound

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

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving General lane-changing model mobil for car-following models,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T06:00:50.541460Z

Source-reported events for the cited work

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Observation 6715f95c-e45f-4db3-8ba8-878f389d535c · outbound

This paper cites Driving intention recognition and lane change prediction on the highway,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Driving intention recognition and lane change prediction on the highway,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.349165Z

Source-reported events for the cited work

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Observation c24b7b92-da87-4042-8347-13feec93eedd · outbound

This paper cites End to End Learning for Self-Driving Cars.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving End to End Learning for Self-Driving Cars

Reference 9

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Source-reported events for the cited work

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Observation 375a326f-43e9-4b16-9a36-85937ffbcd2e · outbound

This paper cites Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T06:00:50.557642Z

Source-reported events for the cited work

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Observation 55b9b7e8-12ba-4701-a5b2-05f268b3e490 · outbound

This paper cites End-to-end driving via conditional imitation learning,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving End-to-end driving via conditional imitation learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.319836Z

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.

Observation 36b2f5bc-7278-495e-8fb1-8b47a0162173 · outbound

This paper cites Urban driving with conditional imitation learning,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Urban driving with conditional imitation learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.292253Z

Source-reported events for the cited work

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Observation 55972cc3-481a-460e-a29a-3e839dd90330 · outbound

This paper cites an unresolved cited work.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Unresolved cited work

Reference 13

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Source-reported events for the cited work

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Observation d9bb2ed2-0075-4f50-a6b6-bf88234ca509 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Proximal Policy Optimization Algorithms

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T06:00:50.578999Z

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Observation 0beb63b8-ef8f-4cca-a6c2-26501b0a1cd7 · outbound

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

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Soft actor- critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T06:00:50.584294Z

Source-reported events for the cited work

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Observation c6c14dc3-1631-44ad-a571-876a55ac214f · outbound

This paper cites Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-05T06:00:50.800160Z

Source-reported events for the cited work

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Observation f30b5aa9-2645-4da6-b0cb-3c763ee0f7fa · outbound

This paper cites Exploiting hier- archy for scalable decision making in autonomous driving,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Exploiting hier- archy for scalable decision making in autonomous driving,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.241149Z

Source-reported events for the cited work

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Observation 82cfd841-8194-4b4f-a592-e118644d4a2f · outbound

This paper cites Learning hierarchical behavior and motion planning for autonomous driv- ing,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Learning hierarchical behavior and motion planning for autonomous driv- ing,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.214963Z

Source-reported events for the cited work

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Observation 442e7056-f63b-42d2-9cec-8e1fe35220f9 · outbound

This paper cites Deep hierarchical rein- forcement learning for autonomous driving with distinct behav- iors,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Deep hierarchical rein- forcement learning for autonomous driving with distinct behav- iors,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.195558Z

Source-reported events for the cited work

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Observation 9530e4af-1363-4bc5-8a88-e4d940d0f112 · outbound

This paper cites A rein- forcement learning approach to autonomous decision making of intelligent vehicles on highways,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving A rein- forcement learning approach to autonomous decision making of intelligent vehicles on highways,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.173646Z

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.

Observation 9b9c98ce-ed79-45fd-a6dc-46550f4f3f75 · outbound

This paper cites Integrating deep reinforcement learning with model-based path planners for automated driving,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Integrating deep reinforcement learning with model-based path planners for automated driving,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.153412Z

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.

Observation 3a1a448e-32d0-4c06-8cad-5fbcedbb70ff · outbound

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

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Lane change decision-making through deep reinforcement learning with rule- based constraints,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.131439Z

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.

Observation 202b78e6-34ee-4f55-b85d-e941086fc858 · outbound

This paper cites Combining reinforcement learning with rule- based controllers for transparent and general decision-making in autonomous driving,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Combining reinforcement learning with rule- based controllers for transparent and general decision-making in autonomous driving,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.110195Z

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.

Observation 6948b72f-e7b5-4b3e-a656-133b06883507 · outbound

This paper cites A combined reinforcement learning and model predictive control for car- following maneuver of autonomous vehicles,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving A combined reinforcement learning and model predictive control for car- following maneuver of autonomous vehicles,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.087262Z

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.

Observation 29d92c44-477d-48f7-b5f3-3535411ff1ed · outbound

This paper cites Combining reinforcement learning with model predic- tive control for on-ramp merging,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Combining reinforcement learning with model predic- tive control for on-ramp merging,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.064224Z

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.

Observation fb03a919-7a64-4c50-8a76-44a78b66fd6c · outbound

This paper cites A Hierarchical Architecture for Sequential Decision-Making in Autonomous Driving using Deep Reinforcement Learning.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving A Hierarchical Architecture for Sequential Decision-Making in Autonomous Driving using Deep Reinforcement Learning

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-05T06:00:50.774633Z

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.

Observation 94b4fe8e-3b93-44d9-8ac6-7d5ae5304ca8 · outbound

This paper cites Driving decision and control for automated lane change behavior based on deep reinforcement learning,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Driving decision and control for automated lane change behavior based on deep reinforcement learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.043596Z

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.

Observation 2ad53c79-efa3-406a-94ca-e017a3af85d7 · outbound

This paper cites Prioritized experience- based reinforcement learning with human guidance for au- tonomous driving,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Prioritized experience- based reinforcement learning with human guidance for au- tonomous driving,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.024417Z

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.

Observation 27b91336-1d58-4653-afa1-78e13170692f · outbound

This paper cites Learning to drive in a day,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Learning to drive in a day,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:51.002152Z

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.

Observation 3b472d54-e28a-4949-b19b-de8fe36f6896 · outbound

This paper cites Efficient deep reinforcement learning with imitative expert priors for autonomous driving,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Efficient deep reinforcement learning with imitative expert priors for autonomous driving,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:50.978928Z

Source-reported events for the cited work

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Observation 6bda6f56-dce9-46bf-9fa5-8cbe3c6a03d7 · outbound

This paper cites Imitation is not enough: Robustifying imitation with reinforcement learning for challenging driving scenarios,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Imitation is not enough: Robustifying imitation with reinforcement learning for challenging driving scenarios,

Reference 31

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Observation 19369424-5080-40a3-b3f4-36d24e2bd34e · outbound

This paper cites Boosted bellman residual minimization handling expert demonstrations,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Boosted bellman residual minimization handling expert demonstrations,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:50.949614Z

Source-reported events for the cited work

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Observation 46d406f3-6250-4d75-a145-16ba275627c3 · outbound

This paper cites Deep q-learning from demonstrations,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Deep q-learning from demonstrations,

Reference 33

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Source-reported events for the cited work

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Observation e453e3c0-12a8-4260-b396-4acb29cf25ab · outbound

This paper cites Reward learning from human preferences and demonstrations in atari,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Reward learning from human preferences and demonstrations in atari,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-05T06:00:50.683143Z

Source-reported events for the cited work

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Observation 10074819-6656-4277-8120-92136c49b0d5 · outbound

This paper cites SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T06:00:50.687992Z

Source-reported events for the cited work

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Observation 55434cac-0246-4a85-b7ec-c8d4ce512d71 · outbound

This paper cites An environment for autonomous driving decision- making,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving An environment for autonomous driving decision- making,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:50.907365Z

Source-reported events for the cited work

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Observation 319d0981-1e94-4a81-a857-42912ccd75b6 · outbound

This paper cites Conservative q- learning for offline reinforcement learning,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Conservative q- learning for offline reinforcement learning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:00:50.887294Z

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.

Observation fc2ea35e-942f-4094-a434-f69b9e4cf02c · outbound

This paper cites Generative adversarial imitation learning,.

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving Generative adversarial imitation learning,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T06:00:50.704572Z

Source-reported events for the cited work

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