Paper Citation Record · LEDGER

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario

As of 14 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2507.15587.

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

pith.paper-citation-record.v1
2507.15587 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:32:56.612478Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

32 of 32 outbound references displayed

verified exact3
verified fuzzy22
unresolved7
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5c95d6a1-8d64-4403-97fa-ff8b791b78ba · outbound

This paper cites Edge computing for autonomous driving: Opportunities and challenges,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Edge computing for autonomous driving: Opportunities and challenges,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:56.458867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cc421c2-78d1-4fc2-8185-9e58532fadcc · outbound

This paper cites A survey on safety-critical driving scenario generation—a methodological perspec- tive,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario A survey on safety-critical driving scenario generation—a methodological perspec- tive,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:56.463770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb9beb9b-5796-410e-81fd-5d4a378d249e · outbound

This paper cites Intelligent driving in- telligence test for autonomous vehicles with naturalistic and adversarial environment,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Intelligent driving in- telligence test for autonomous vehicles with naturalistic and adversarial environment,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:57.149144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 96c22709-2fed-4e65-9bb1-8b78f1b8f746 · outbound

This paper cites Cmts: A conditional multiple trajectory synthesizer for generating safety-critical driving scenarios,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Cmts: A conditional multiple trajectory synthesizer for generating safety-critical driving scenarios,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:57.125689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 158b8e79-0b78-4797-b946-6b017816d709 · outbound

This paper cites Generating critical test scenarios for auto- mated vehicles with evolutionary algorithms,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Generating critical test scenarios for auto- mated vehicles with evolutionary algorithms,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:57.102110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 84715c10-9b50-450c-9d24-2636400fcfbc · outbound

This paper cites Diversifying latent flows for safety-critical scenarios generation,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Diversifying latent flows for safety-critical scenarios generation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:57.086388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fd27eac4-a4cb-4ae5-a753-fcfa84e5129d · outbound

This paper cites Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:32:56.781278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d815c146-e50b-42a9-a8d1-ca9ca6a837e5 · outbound

This paper cites Risk-aware attention-based td3 approach for vehicle decision-making in dynamic traffic,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Risk-aware attention-based td3 approach for vehicle decision-making in dynamic traffic,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:57.071429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation de75af90-e4e6-4939-96f6-eb500add4816 · outbound

This paper cites Risk- aware vehicle trajectory prediction under safety-critical scenarios,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Risk- aware vehicle trajectory prediction under safety-critical scenarios,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:57.055445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f169796d-0f1a-438e-a667-6e5288ccd8cd · outbound

This paper cites Safety risk evaluation based autonomous vehicle decision-making approach for cut- in emergency scenario,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Safety risk evaluation based autonomous vehicle decision-making approach for cut- in emergency scenario,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:57.037595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3ca4dfd2-0ace-467b-9304-c797058dbb2c · outbound

This paper cites A decision- making strategy for vehicle autonomous braking in emergency via deep reinforcement learning,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario A decision- making strategy for vehicle autonomous braking in emergency via deep reinforcement learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:57.021880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3ffb447e-0232-4ee5-9fc5-ab398525bb5e · outbound

This paper cites Human decision-making in high-risk driving scenarios: A cognitive modeling perspective,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Human decision-making in high-risk driving scenarios: A cognitive modeling perspective,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:57.006468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fa0d5273-a500-4942-9055-1d6144e55d95 · outbound

This paper cites Decision-making for complex scenario using safe reinforcement learning,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Decision-making for complex scenario using safe reinforcement learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:56.990995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 94735276-2f5f-4e07-844d-9617012722da · outbound

This paper cites Dynamic Residual Safe Reinforcement Learning for Multi-Agent Safety-Critical Scenarios Decision-Making.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Dynamic Residual Safe Reinforcement Learning for Multi-Agent Safety-Critical Scenarios Decision-Making

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:32:56.759604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3a4d6488-ecce-43e2-a28c-e06ebd458b95 · outbound

This paper cites Markov decision processes,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Markov decision processes,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:56.975537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 682c9375-484e-4e09-83f1-4b9e231c0120 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Proximal Policy Optimization Algorithms

Reference 17

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Source-reported events for the cited work

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Observation 7eb1a9ef-1245-4663-b7bd-122a2451201e · outbound

This paper cites High-Dimensional Continuous Control Using Generalized Advantage Estimation.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario High-Dimensional Continuous Control Using Generalized Advantage Estimation

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e90e86a6-703b-43a5-92e5-cbbcbb8ccf56 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Semi-Supervised Classification with Graph Convolutional Networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:56.545014Z

Source-reported events for the cited work

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Observation 2ef68a73-05f4-4064-ab42-54a426e0dff8 · outbound

This paper cites Benchmarking Batch Deep Reinforcement Learning Algorithms.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Benchmarking Batch Deep Reinforcement Learning Algorithms

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7626f6a2-e9fa-43fc-b6d6-27d621edc16d · outbound

This paper cites Microscopic traffic simulation using sumo,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Microscopic traffic simulation using sumo,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:56.555238Z

Source-reported events for the cited work

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Observation d8b80ae1-641c-4317-8e4a-ca5c9b5dccb4 · outbound

This paper cites Dueling network architectures for deep reinforcement learning,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Dueling network architectures for deep reinforcement learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:56.949460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9a5b9976-ba17-4a07-85f9-56bf89e30ac4 · outbound

This paper cites Deep sparse rectifier neural networks,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Deep sparse rectifier neural networks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:56.934848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2fba25d2-c1b3-4f96-99eb-80b6d9ff0611 · outbound

This paper cites Mv-stghat: Multi-view spatial-temporal graph hybrid attention network for decision- making of connected and autonomous vehicles,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Mv-stghat: Multi-view spatial-temporal graph hybrid attention network for decision- making of connected and autonomous vehicles,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:56.919863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4fceb9bf-c6da-4d8c-8d22-a7710625f272 · outbound

This paper cites Multi-vehicles decision-making in interactive highway exit: A graph reinforcement learning approach,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Multi-vehicles decision-making in interactive highway exit: A graph reinforcement learning approach,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:56.905019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 84c8e0f1-119d-4812-80be-fd5612e0db21 · outbound

This paper cites Rate gqn: A deviations-reduced decision-making strategy for connected and auto- mated vehicles in mixed autonomy,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Rate gqn: A deviations-reduced decision-making strategy for connected and auto- mated vehicles in mixed autonomy,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:56.890272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1948fd57-b4f5-4e4f-bb7e-2ffab3daa665 · outbound

This paper cites Ethical alignment decision making for connected autonomous vehicle in traffic dilemmas via reinforcement learning from human feedback,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Ethical alignment decision making for connected autonomous vehicle in traffic dilemmas via reinforcement learning from human feedback,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:56.874645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5af85ab7-a0c4-49b9-9ec0-fea88d4849d1 · outbound

This paper cites A human feedback- driven decision-making method based on multi-modal deep reinforce- ment learning in ethical dilemma traffic scenarios,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario A human feedback- driven decision-making method based on multi-modal deep reinforce- ment learning in ethical dilemma traffic scenarios,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:56.859207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7e2fff93-3b53-4bd6-961a-325b9e0cb183 · outbound

This paper cites Adversarial evaluation of autonomous vehicles in lane-change scenarios,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Adversarial evaluation of autonomous vehicles in lane-change scenarios,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:56.843252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 05f7f8e0-d8c7-4bca-8be5-fd9548647126 · outbound

This paper cites Multilevel Graph Reinforcement Learning for Consistent Cognitive Decision-making in Heterogeneous Mixed Autonomy.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Multilevel Graph Reinforcement Learning for Consistent Cognitive Decision-making in Heterogeneous Mixed Autonomy

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:32:56.662402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 29bb91e1-8863-4591-957b-28e65d7ce925 · outbound

This paper cites Efficient genera- tion of safety-critical scenarios combining dynamic and static scenario parameters,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Efficient genera- tion of safety-critical scenarios combining dynamic and static scenario parameters,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:56.827059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 434f807b-aabf-4e77-b387-09c75fe97640 · outbound

This paper cites Defining time-to-collision thresholds by the type of lead vehicle in non-lane-based traffic environments,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Defining time-to-collision thresholds by the type of lead vehicle in non-lane-based traffic environments,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:56.812152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 106258f1-972f-4b1d-8a70-10cab8cc7f44 · outbound

This paper cites Characterizing warfare in red teaming,.

Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Characterizing warfare in red teaming,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:56.797131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.