Paper Citation Record · LEDGER

BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2503.03074.

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

pith.paper-citation-record.v1
2503.03074 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:24:07.342331Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T12:26:56.137634Z

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 640592ba-b6e2-41a7-ba45-66ce4bd1018b · inbound

Generative AI for Autonomous Driving: A Review cites this paper.

Generative AI for Autonomous Driving: A Review BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving

Reference 256

Resolution: unresolved; no resolver link, observed 2026-08-07T15:24:07.342331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfaf150f-7bbd-48cc-a930-5b5a60c4a71a · inbound

STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving cites this paper.

STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T06:02:13.136091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71e9668f-8843-447a-8649-32d10fbadc64 · inbound

AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning cites this paper.

AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:46:44.251267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5fe883e0-9b70-4631-9edc-4e92f1dbec6a · inbound

A Survey on Vision-Language-Action Models for Autonomous Driving cites this paper.

A Survey on Vision-Language-Action Models for Autonomous Driving BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving

Reference 134

Resolution: unresolved; no resolver link, observed 2026-08-06T21:31:04.794090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f051b78a-4fa9-4985-9c9a-12b45f5dbaf0 · inbound

BEVLM: Distilling Semantic Knowledge from LLMs into Bird's-Eye View Representations cites this paper.

BEVLM: Distilling Semantic Knowledge from LLMs into Bird's-Eye View Representations BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving

Reference 62

Resolution: unresolved; no resolver link, observed 2026-07-15T13:44:18.418529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92c9afd8-4140-403a-89c6-e2eaeea3f6ea · inbound

ICR-Drive: Instruction Counterfactual Robustness for End-to-End Language-Driven Autonomous Driving cites this paper.

ICR-Drive: Instruction Counterfactual Robustness for End-to-End Language-Driven Autonomous Driving BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:35:51.656361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c1923c3b-8d65-4345-9906-66c8105deeae · inbound

LMGenDrive: Bridging Multimodal Understanding and Generative World Modeling for End-to-End Driving cites this paper.

LMGenDrive: Bridging Multimodal Understanding and Generative World Modeling for End-to-End Driving BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving

Reference 56

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T06:31:01.212778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5f788de2-b803-40f7-aa0a-077ccb3c3221 · inbound

DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving cites this paper.

DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving

Reference 112

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:31:26.579840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 13a2ad9e-4a0f-4491-b670-a132e3d8a3e0 · inbound

MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems cites this paper.

MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:51:27.456047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d05975d6-1704-4aa3-9306-3e02a8fb4183 · inbound

What's Hidden Matters: Identifying Planning-Critical Occluded Agents using Vision-Language Models cites this paper.

What's Hidden Matters: Identifying Planning-Critical Occluded Agents using Vision-Language Models BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:26:56.139147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d78f862e-e468-49b8-a8a9-7aec7fb333e9 · inbound

TGRIP: A Text-Guided Approach to Vehicle Instance Prediction in Autonomous Driving cites this paper.

TGRIP: A Text-Guided Approach to Vehicle Instance Prediction in Autonomous Driving BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving

Reference 38

Resolution: unresolved; no resolver link, observed 2026-07-11T13:09:16.091741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.