Paper Citation Record · LEDGER

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving

As of 11 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2501.03535.

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

pith.paper-citation-record.v1
2501.03535 v2

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:56:08.149743Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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 exact1
verified fuzzy21
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 45dce700-8811-4ef1-9108-ebb3f39606f8 · outbound

This paper cites An overview of sensors in autonomous vehicles.Procedia Com- puter Science, 198:736–741, 2022.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving An overview of sensors in autonomous vehicles.Procedia Com- puter Science, 198:736–741, 2022

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.643940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c5afa47c-93ef-4b2b-90dd-f8557d23303a · outbound

This paper cites Object detection in 20 years: A survey.Proceed- ings of the IEEE, 111(3):257–276, 2023.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Object detection in 20 years: A survey.Proceed- ings of the IEEE, 111(3):257–276, 2023

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.629957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 307a3d05-490d-4ce0-94e0-ae4e96f7b83b · outbound

This paper cites Review the state-of-the-art technologies of seman- tic segmentation based on deep learning.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Review the state-of-the-art technologies of seman- tic segmentation based on deep learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.617589Z

Source-reported events for the cited work

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Observation 5c95f688-34c6-479e-82ca-460c64a7adb9 · outbound

This paper cites Deep learning for computer vision: A brief review.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Deep learning for computer vision: A brief review

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.605090Z

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Observation a28fa83e-1622-4476-a4b7-4a740068b57c · outbound

This paper cites A survey on multimodal large language models for autonomous driving.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving A survey on multimodal large language models for autonomous driving

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.593397Z

Source-reported events for the cited work

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Observation 94c91b18-094b-4c5e-b2ab-4500e1cc10cf · outbound

This paper cites DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:08.037340Z

Source-reported events for the cited work

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Observation fcbc033d-f42a-4a03-861d-86dfa5020e10 · outbound

This paper cites Language Models are Few-Shot Learners.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Language Models are Few-Shot Learners

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:08.042075Z

Source-reported events for the cited work

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Observation 31e0290c-ecb0-4c23-adb7-464a006725d6 · outbound

This paper cites Deep learning in computer vision: A critical review of emerg- ing techniques and application scenarios.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Deep learning in computer vision: A critical review of emerg- ing techniques and application scenarios

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.581822Z

Source-reported events for the cited work

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Observation dc4b89ae-84d0-4cca-bc00-88debde56f3e · outbound

This paper cites Vehicle- to-everything (v2x) in the autonomous vehicles domain–a technical review of communication, sensor, and ai technolo- gies for road user safety.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Vehicle- to-everything (v2x) in the autonomous vehicles domain–a technical review of communication, sensor, and ai technolo- gies for road user safety

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:08.051399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4008681b-cff8-4cc5-83aa-066d9a062634 · outbound

This paper cites Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.561697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f79871d5-9408-4149-a2b9-dad09175872e · outbound

This paper cites Towards Optimizing the Costs of LLM Usage.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Towards Optimizing the Costs of LLM Usage

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:08.059353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d1d2484-196f-4529-af3f-23ae3700306d · outbound

This paper cites Collabora- tive perception for autonomous driving: Current status and future trend.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Collabora- tive perception for autonomous driving: Current status and future trend

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.549575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 40c8871b-126a-4ef3-af48-eda57a778d1a · outbound

This paper cites Collaborative perception in autonomous driv- ing: Methods, datasets, and challenges.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Collaborative perception in autonomous driv- ing: Methods, datasets, and challenges

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.536706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a89f86b2-2177-4510-9b8a-7156d922eef3 · outbound

This paper cites How does c-v2x help autonomous driving to avoid acci- dents? Sensors, 22(2):686, 2022.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving How does c-v2x help autonomous driving to avoid acci- dents? Sensors, 22(2):686, 2022

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.525031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ccba179f-514e-44ab-9f32-df1a8597c44c · outbound

This paper cites Au- tonomous driving under v2x environment: state-of-the-art survey and challenges.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Au- tonomous driving under v2x environment: state-of-the-art survey and challenges

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.512758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 80581271-e5e2-4905-a87f-fa5ec01e650b · outbound

This paper cites Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:08.080175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d1a778f-1f75-492d-9b05-dfcfd09a7b63 · outbound

This paper cites Set- membership estimation in shared situational awareness for automated vehicles in occluded scenarios.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Set- membership estimation in shared situational awareness for automated vehicles in occluded scenarios

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.500371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8625c8be-5531-4c69-8fc1-dc40208376af · outbound

This paper cites Overcoming occlusions: Per- ception task-oriented information sharing in connected and autonomous vehicles.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Overcoming occlusions: Per- ception task-oriented information sharing in connected and autonomous vehicles

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.488168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 92efbfdd-a5b8-403c-9e06-88914ab096cd · outbound

This paper cites Cross-domain transfer learning using attention latent features for multi-agent trajectory prediction, 2024.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Cross-domain transfer learning using attention latent features for multi-agent trajectory prediction, 2024

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.475273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 34ccc886-a0f7-4c59-b612-5124305c26bd · outbound

This paper cites Toward ensuring safety for autonomous driving perception: standardization progress, research advances, and perspectives.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Toward ensuring safety for autonomous driving perception: standardization progress, research advances, and perspectives

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.462057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 82b95a99-cf17-4de1-8cb1-e49152fcb1c5 · outbound

This paper cites Towards communication-efficient collaborative per- ception: Harnessing channel-spatial attention and knowledge distillation.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Towards communication-efficient collaborative per- ception: Harnessing channel-spatial attention and knowledge distillation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.450159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1c5d1e7b-9cf4-429b-bb58-a0e7c1bab21e · outbound

This paper cites Communication-efficient collaborative percep- tion via information filling with codebook.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Communication-efficient collaborative percep- tion via information filling with codebook

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.427860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4eb1bb74-a033-4864-a07d-147aebeafb9f · outbound

This paper cites A Survey on Large Language Model-empowered Autonomous Driving.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving A Survey on Large Language Model-empowered Autonomous Driving

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:56:08.314325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a045ff14-33b6-45d5-8a6d-b9016290236e · outbound

This paper cites Llm4drive: A survey of large language models for au- tonomous driving.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Llm4drive: A survey of large language models for au- tonomous driving

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.415062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e0800128-79b1-4bb9-8dd4-b8766defe363 · outbound

This paper cites SURDS: Benchmarking Spatial Understanding and Reasoning in Driving Scenarios with Vision Language Models.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving SURDS: Benchmarking Spatial Understanding and Reasoning in Driving Scenarios with Vision Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:08.122355Z

Source-reported events for the cited work

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Observation d0ebb813-3393-41eb-9a5f-e3c3df27a6de · outbound

This paper cites PKRD-CoT: A Unified Chain-of-thought Prompting for Multi-Modal Large Language Models in Autonomous Driving.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving PKRD-CoT: A Unified Chain-of-thought Prompting for Multi-Modal Large Language Models in Autonomous Driving

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:08.127151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9a118a1-9a1b-460f-8488-24d0dac2ec50 · outbound

This paper cites Towards Knowledge-driven Autonomous Driving.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Towards Knowledge-driven Autonomous Driving

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:08.132449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 261f40ec-a85f-4e68-9cd3-4d03470c5856 · outbound

This paper cites A survey on rag meeting llms: Towards retrieval-augmented large language models.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving A survey on rag meeting llms: Towards retrieval-augmented large language models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.400789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 05521598-6834-4c36-9503-e1178841e8bd · outbound

This paper cites Rag-driver: Gen- eralisable driving explanations with retrieval-augmented in- context learning in multi-modal large language model.arXiv preprint arXiv:2402.10828, 2024.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Rag-driver: Gen- eralisable driving explanations with retrieval-augmented in- context learning in multi-modal large language model.arXiv preprint arXiv:2402.10828, 2024

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:08.141018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0aa0ef13-9a45-4d6e-b859-59c797fbf3fd · outbound

This paper cites Rag-guided large language models for visual spatial description with adaptive hallucina- tion corrector.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Rag-guided large language models for visual spatial description with adaptive hallucina- tion corrector

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.387272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0408cc9f-d6f6-470b-9c76-a837bf563cd3 · outbound

This paper cites Visual instruction tuning.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Visual instruction tuning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:56:08.373975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 14593a33-ef72-4162-8d3e-9195ee8f908e · outbound

This paper cites an unresolved cited work.

SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving Unresolved cited work

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-10T21:56:08.104643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.