Paper Citation Record · LEDGER

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios

As of 5 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2505.17209.

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

pith.paper-citation-record.v1
2505.17209 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-22T13:18:30.486507Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T21:21:10.946939Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact26
verified fuzzy6
unresolved0
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c7177b98-3adb-4e90-8187-19d06ae9e6b1 · outbound

This paper cites Motion planning for autonomous driving: The state of the art and future perspectives.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Motion planning for autonomous driving: The state of the art and future perspectives

Reference 1

Resolution: malformed identifier; doi_truncated, observed 2026-05-22T13:21:35.335962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8dbd5e28-4cc1-42df-af33-457085c03701 · outbound

This paper cites The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.790300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 302796d0-cbc0-4a69-927b-2e5edc19f19d · outbound

This paper cites NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-22T13:21:35.795565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 33607a74-b704-4902-8564-2401d930aec8 · outbound

This paper cites Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-22T13:21:35.683835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 541af2fc-3ef7-4951-9b9a-93fd6a5ef537 · outbound

This paper cites LLM4Drive: A Survey of Large Language Models for Autonomous Driving.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios LLM4Drive: A Survey of Large Language Models for Autonomous Driving

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.693710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c8786bf7-6458-4d3e-9cd7-c101e9d0b9fe · outbound

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

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios A survey on multimodal large language models for autonomous driving

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:21:36.408288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 66aae896-3319-4254-a81d-da3d9e082b69 · outbound

This paper cites Forging vision foundation models for autonomous driving: Challenges, methodologies, and opportunities.

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 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:21:36.405303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8d17c79f-ab75-425d-8841-33095640e46e · outbound

This paper cites Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities.

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 2026-08-05T06:32:48.257954+00:00.

Observation ad3d9577-ac9e-4da2-98fb-1adbcf6c0039 · outbound

This paper cites Vision Language Models in Autonomous Driving: A Survey and Outlook.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Vision Language Models in Autonomous Driving: A Survey and Outlook

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.679014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 32f7d0d1-550a-4ef0-97af-10960a20554c · outbound

This paper cites Can vehicle motion planning generalize to realistic long-tail scenarios?.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Can vehicle motion planning generalize to realistic long-tail scenarios?

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:21:36.418672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9574cd77-c4ac-4020-8d2b-9e8ff9b13d8e · outbound

This paper cites Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.754388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4b1c4072-b706-40d5-90bb-92d7440ef9cb · outbound

This paper cites Parting with Misconceptions about Learning-based Vehicle Motion Planning.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Parting with Misconceptions about Learning-based Vehicle Motion Planning

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.722576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c630c9f8-a05b-45f4-9f9d-4a6663e1e1c5 · outbound

This paper cites Congested Traffic States in Empirical Observations and Microscopic Simulations.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Congested Traffic States in Empirical Observations and Microscopic Simulations

Reference 13

Resolution: verified exact; doi, observed 2026-05-22T13:21:35.353770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7d2ddc36-31fc-470a-897c-3ff34fd17c91 · outbound

This paper cites Lmm-enhanced safety-critical scenario generation for autonomous driving system testing from non-accident traffic videos.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Lmm-enhanced safety-critical scenario generation for autonomous driving system testing from non-accident traffic videos

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:21:36.421939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation dd71634b-52b1-4492-8a66-7feb50d08a59 · outbound

This paper cites LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.698956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4057bfdd-edbb-4e6a-99c4-b8edee50e31d · outbound

This paper cites PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.760419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d211e8af-0f49-49fd-8ddb-ae3ad290da68 · outbound

This paper cites A Survey on Multimodal Large Language Models for Autonomous Driving.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios A Survey on Multimodal Large Language Models for Autonomous Driving

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.749047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b75f6952-43ae-4c4f-b452-8c0356f6a862 · outbound

This paper cites End-to-end Autonomous Driving: Challenges and Frontiers.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios End-to-end Autonomous Driving: Challenges and Frontiers

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.765750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0ae1b590-4318-4d5e-85ee-2ae90ecb761b · outbound

This paper cites Baidu Apollo EM Motion Planner.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Baidu Apollo EM Motion Planner

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-05-22T13:21:35.780008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 06f25d30-3656-49e1-8aa2-b42658a551f3 · outbound

This paper cites Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.770266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b7406c7e-b0f9-4d4b-a7fc-3ba5be4c2544 · outbound

This paper cites DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.785021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 171a8f90-52a6-4d8a-b089-97455dabf402 · outbound

This paper cites GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.688786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8d18178b-a159-4a2f-ab6b-256c78f66cef · outbound

This paper cites In: Proceedings of the 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), pp.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios In: Proceedings of the 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), pp

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T13:21:35.345851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cb5f4476-db52-4449-9788-504c7941b8e7 · outbound

This paper cites Rethinking Imitation-based Planner for Autonomous Driving.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Rethinking Imitation-based Planner for Autonomous Driving

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.712854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 57f260f7-b814-4fc4-8a1c-86fed0364e42 · outbound

This paper cites an unresolved cited work.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Unresolved cited work

Reference 25

Resolution: verified exact; doi, observed 2026-05-22T13:21:35.359376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d05e231e-8423-4167-9bc7-c929650e7d75 · outbound

This paper cites Generative AI in Transportation Planning: A Survey.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Generative AI in Transportation Planning: A Survey

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.744331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 07139216-c450-42c8-8564-0b39e678440c · outbound

This paper cites SurrealDriver: Designing LLM-powered Generative Driver Agent Framework based on Human Drivers' Driving-thinking Data.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios SurrealDriver: Designing LLM-powered Generative Driver Agent Framework based on Human Drivers' Driving-thinking Data

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.727248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation cafcbda4-1b02-4c16-b763-e7ced6a1934b · outbound

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

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.738804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 63ed8dae-c6e7-4a92-855c-53f6024b72eb · outbound

This paper cites LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.708162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 14f0662e-bd18-494f-bda9-780f3884f562 · outbound

This paper cites LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.674668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 79ce1738-151a-408d-8c37-80121863e480 · outbound

This paper cites Lmdrive: Closed-loop end-to-end driving with large language models.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Lmdrive: Closed-loop end-to-end driving with large language models

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:21:36.415206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation dd6e35f2-c604-424d-bd56-5212eb9bd4b1 · outbound

This paper cites Drivemlm: Aligning multi-modal large language models with behavioral planning states for au- tonomous driving.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios Drivemlm: Aligning multi-modal large language models with behavioral planning states for au- tonomous driving

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.775599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 4aba7dbe-db7e-4b22-91c5-f67ed6eb3d52 · outbound

This paper cites A density-based algorithm for discovering clusters in large spatial databases with noise.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios A density-based algorithm for discovering clusters in large spatial databases with noise

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T13:21:36.402148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8d7b3974-939a-4662-9994-fd6786dc3300 · outbound

This paper cites The Llama 3 Herd of Models.

LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios The Llama 3 Herd of Models

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-05-22T13:21:35.717348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

Observation 345b1737-a2bd-4424-8bf3-42cb83f4f381 · inbound

Deconfounded Lifelong Learning for Autonomous Driving via Dynamic Knowledge Spaces cites this paper.

Deconfounded Lifelong Learning for Autonomous Driving via Dynamic Knowledge Spaces LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios

Reference 53

Resolution: unresolved; no resolver link, observed 2026-07-14T21:21:10.946939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.