Paper Citation Record · LEDGER

Reinforcement Learning from Human Feedback for Lane Changing of Autonomous Vehicles in Mixed Traffic

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2408.04447.

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

pith.paper-citation-record.v1
2408.04447 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:56:33.360309Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T09:23:10.774683Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ae6462f7-b00e-444d-b666-3ca84becff65 · inbound

Regulator-Manufacturer AI Agents Modeling: Mathematical Feedback-Driven Multi-Agent LLM Framework cites this paper.

Regulator-Manufacturer AI Agents Modeling: Mathematical Feedback-Driven Multi-Agent LLM Framework Reinforcement Learning from Human Feedback for Lane Changing of Autonomous Vehicles in Mixed Traffic

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T14:29:25.607927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ec16ec1-07f0-43f5-b787-e33f2614889c · inbound

Towards Infant Sleep-Optimized Driving: Synergizing Wearable and Vehicle Sensing in Intelligent Cruise Control cites this paper.

Towards Infant Sleep-Optimized Driving: Synergizing Wearable and Vehicle Sensing in Intelligent Cruise Control Reinforcement Learning from Human Feedback for Lane Changing of Autonomous Vehicles in Mixed Traffic

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T06:01:58.366332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e714d9b8-96d3-44c9-a19a-fea23c7ce9b6 · inbound

Sharpening the Spear: Adaptive Expert-Guided Adversarial Attack Against DRL-based Autonomous Driving Policies cites this paper.

Sharpening the Spear: Adaptive Expert-Guided Adversarial Attack Against DRL-based Autonomous Driving Policies Reinforcement Learning from Human Feedback for Lane Changing of Autonomous Vehicles in Mixed Traffic

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T18:56:33.360309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4cf43c1-dc91-4452-81b4-eb31f9d2a204 · inbound

RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning cites this paper.

RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Reinforcement Learning from Human Feedback for Lane Changing of Autonomous Vehicles in Mixed Traffic

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-20T09:23:10.776127Z

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.