Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2401.08045.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T15:05:27.319583Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T13:21:35.729548Z
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 8d289ecb-128c-4847-b39b-9a2608a3034e · inbound
VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving cites this paper.
VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T15:05:27.319583Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eb8da58d-e4ca-4ece-a8c0-2a75c8f6250e · inbound
An Efficient Occupancy World Model via Decoupled Dynamic Flow and Image-assisted Training cites this paper.
An Efficient Occupancy World Model via Decoupled Dynamic Flow and Image-assisted Training Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T12:51:18.452172Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8d17c79f-ab75-425d-8841-33095640e46e · inbound
LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios cites this paper.
LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T13:21:35.732896Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation fae8c30c-c100-4f15-9b78-df95426c161b · inbound
Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities cites this paper.
Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T20:52:05.987906Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation de865685-4f6f-4c08-b4e6-97453d3968c5 · inbound
Training a Student Expert via Semi-Supervised Foundation Model Distillation cites this paper.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
Reference 50
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T16:53:00.108192Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.