Paper Citation Record · LEDGER

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning

As of 18 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2501.14992.

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

pith.paper-citation-record.v1
2501.14992 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:48:49.451638Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T06:00:50.589688Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T06:00:50.794346Z

Reference resolution

39 of 39 outbound references displayed

verified exact0
verified fuzzy29
unresolved10
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6c36f36f-e6a6-45f2-8f06-0828867163f2 · outbound

This paper cites Deep reinforcement learning for autonomous driving: A survey.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Deep reinforcement learning for autonomous driving: A survey

Reference 1

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Observation 012a6269-2b02-4b23-bd92-a6b361f32b9a · outbound

This paper cites A survey of autonomous driving: Common practices and emerging technologies.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning A survey of autonomous driving: Common practices and emerging technologies

Reference 2

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Observation ecef3ed7-6687-487a-a411-46f4feb621f2 · outbound

This paper cites Evaluating the utility of driving: Toward automated decision making under uncertainty.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Evaluating the utility of driving: Toward automated decision making under uncertainty

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:50.092344Z

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Observation 36361f9b-bf0e-4c50-b096-ff411623ffd2 · outbound

This paper cites Sketch of an ivhs systems architecture.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Sketch of an ivhs systems architecture

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:50.075991Z

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Observation a9347bb9-0900-4d4e-aba8-ae8060171d10 · outbound

This paper cites A multiple- goal reinforcement learning method for complex vehicle overtak- ing maneuvers.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning A multiple- goal reinforcement learning method for complex vehicle overtak- ing maneuvers

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:50.058823Z

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Observation 04e4a632-13f3-4c73-88ee-74cf51fc37dc · outbound

This paper cites A reinforcement learning approach to autonomous decision making of intelligent vehicles on highways.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning A reinforcement learning approach to autonomous decision making of intelligent vehicles on highways

Reference 6

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Observation 44849914-9259-4c26-82fa-c10996b29324 · outbound

This paper cites A review and analysis of lit- erature on autonomous driving.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning A review and analysis of lit- erature on autonomous driving

Reference 7

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Observation 52d97b39-787a-4222-bdd1-598705cdf50c · outbound

This paper cites An integrated model for autonomous speed and lane change decision- making based on deep reinforcement learning.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning An integrated model for autonomous speed and lane change decision- making based on deep reinforcement learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:50.011741Z

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Observation ed3546da-55ed-4c67-8a0a-6276f480c84c · outbound

This paper cites Lane change and merge maneuvers for connected and automated vehicles: A survey.IEEE Transactions on Intelligent Vehicles, 1(1):105–120, 2016.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Lane change and merge maneuvers for connected and automated vehicles: A survey.IEEE Transactions on Intelligent Vehicles, 1(1):105–120, 2016

Reference 9

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Observation b4bae2f5-f462-4a92-a80a-c1cede241f80 · outbound

This paper cites Automated lane change controller design.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Automated lane change controller design

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.974542Z

Source-reported events for the cited work

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Observation 32684c30-4594-4336-b349-d7ef0c1488a6 · outbound

This paper cites Traffic dynamics: studies in car following.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Traffic dynamics: studies in car following

Reference 11

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Observation d08fb8de-fd77-45ff-a982-360e6d164f87 · outbound

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

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning A behavioural car-following model for computer simulation

Reference 12

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Source-reported events for the cited work

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Observation b0c5240f-3d0c-4963-b246-ee5a599ebcc3 · outbound

This paper cites Congested traffic states in empirical observations and microscopic simula- tions.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Congested traffic states in empirical observations and microscopic simula- tions

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.923215Z

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Observation b9ff7b56-9f0a-471c-8fb1-2d1988c3eeed · outbound

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

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning General lane- changing model mobil for car-following models

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.906388Z

Source-reported events for the cited work

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Observation c29f9d66-4ae7-447f-ab94-ad61d206316f · outbound

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

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Driving intention recognition and lane change prediction on the highway

Reference 15

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Observation 5c5b652c-49a8-49e0-a351-1bdcf38b8710 · outbound

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

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning End to End Learning for Self-Driving Cars

Reference 16

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Observation a74100c7-79f5-45d0-a3bc-b489ac7a8da3 · outbound

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

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car

Reference 17

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Observation a8c4d7b8-e31d-416a-b0d8-e5db55030171 · outbound

This paper cites End-to-end driving via condi- tional imitation learning.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning End-to-end driving via condi- tional imitation learning

Reference 18

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Observation 88d84d90-8650-4039-b7f3-cd66d77ac532 · outbound

This paper cites Urban driving with conditional imitation learning.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Urban driving with conditional imitation learning

Reference 19

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Observation 0cafb719-1b87-4cc0-aaab-1949abc10026 · outbound

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

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Integrating deep reinforcement learning with model-based path planners for automated driving

Reference 20

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Observation eb3f02ea-f652-478c-8281-bfcd13eeb3d7 · outbound

This paper cites Learning to drive in a day.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Learning to drive in a day

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.824731Z

Source-reported events for the cited work

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Observation 2d6cc76c-fdb4-42e8-95a6-066098e67a77 · outbound

This paper cites Deep hierarchical reinforcement learning for autonomous driving with distinct behaviors.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Deep hierarchical reinforcement learning for autonomous driving with distinct behaviors

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.808463Z

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Observation b0aac2ed-1cc8-4deb-b2aa-edea408407a8 · outbound

This paper cites Exploiting hierarchy for scalable decision making in autonomous driving.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Exploiting hierarchy for scalable decision making in autonomous driving

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.791941Z

Source-reported events for the cited work

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Observation 78ebff57-1472-4391-87f8-fbf360d66949 · outbound

This paper cites Hierarchical finite state machines for autonomous mobile systems.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Hierarchical finite state machines for autonomous mobile systems

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.775321Z

Source-reported events for the cited work

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Observation eed51148-1b20-41d9-9796-d8e0102b5534 · outbound

This paper cites Multi-lane cruising using hierarchical planning and reinforcement learning.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Multi-lane cruising using hierarchical planning and reinforcement learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.758354Z

Source-reported events for the cited work

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Observation 204f22f8-3fbf-479b-b17e-b44cd416cb6a · outbound

This paper cites A reinforcement learning based approach for automated lane change maneuvers.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning A reinforcement learning based approach for automated lane change maneuvers

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.740471Z

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Observation 5cdecd08-a3d4-431d-89e8-e71f7bf6904d · outbound

This paper cites Learning hierarchical behavior and motion planning for autonomous driving.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Learning hierarchical behavior and motion planning for autonomous driving

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.723355Z

Source-reported events for the cited work

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Observation ab3b37d8-a347-4283-8226-5f2d9a2b97e7 · outbound

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

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning A Hierarchical Architecture for Sequential Decision-Making in Autonomous Driving using Deep Reinforcement Learning

Reference 28

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Observation 08368169-16b8-47eb-8f47-07ca7e5963c6 · outbound

This paper cites Interpretable goal-based prediction and planning for au- tonomous driving.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Interpretable goal-based prediction and planning for au- tonomous driving

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.704143Z

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Observation 8ba6d75a-de90-47bf-b609-5a460bd7bc36 · outbound

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

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Driving decision and control for automated lane change behavior based on deep reinforcement learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.685959Z

Source-reported events for the cited work

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Observation 8a75e899-1628-491c-acad-a88fadb34958 · outbound

This paper cites Hierarchical deep reinforcement learning: Integrat- ing temporal abstraction and intrinsic motivation.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Hierarchical deep reinforcement learning: Integrat- ing temporal abstraction and intrinsic motivation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.668814Z

Source-reported events for the cited work

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Observation 4cc43d8a-a6ec-48b9-8c6a-f1697171ecaf · outbound

This paper cites Reinforcement learning: An introduction.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Reinforcement learning: An introduction

Reference 32

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Observation 6b3ccca4-4c13-48b5-a2f7-cba96ff1da6d · outbound

This paper cites Au- tonomous highway driving using deep reinforcement learning.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Au- tonomous highway driving using deep reinforcement learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.639280Z

Source-reported events for the cited work

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Observation b2a5bb65-390c-45e2-9520-55d5c5787573 · outbound

This paper cites Deep reinforcement learning with double q-learning.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Deep reinforcement learning with double q-learning

Reference 34

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This paper cites Human- level control through deep reinforcement learning.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Human- level control through deep reinforcement learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.608720Z

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Observation e842a212-ec83-4bb8-ad4c-0659e3cf1136 · outbound

This paper cites Intrinsic motivation and reinforcement learning.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Intrinsic motivation and reinforcement learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.590652Z

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Observation dd64c0e2-3b71-4359-b68f-e2428bbe9bb1 · outbound

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

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning An environment for autonomous driving decision-making

Reference 37

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Source-reported events for the cited work

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Observation b5d6e5f0-7db6-4e69-9618-2cd3c883ed20 · outbound

This paper cites Decision transformer: Reinforcement learning via sequence modeling.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Decision transformer: Reinforcement learning via sequence modeling

Reference 38

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Source-reported events for the cited work

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Observation c2b840cc-9ee5-4185-a7f2-ae660d85827e · outbound

This paper cites Offline reinforce- ment learning as one big sequence modeling problem.

Extensive Exploration in Complex Traffic Scenarios using Hierarchical Reinforcement Learning Offline reinforce- ment learning as one big sequence modeling problem

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:48:49.549900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

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

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving cites this paper.

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

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.