Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Reinforcement Learning from Human Feedback for Lane Changing of Autonomous Vehicles in Mixed Traffic
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2408.04447.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T06:01:58.366332Z
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 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 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 2026-08-10T06:31:04.303077+00:00.