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arxiv: 2505.17209 · v2 · submitted 2025-05-22 · 💻 cs.RO · cs.AI

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

Huaiyuan Yao , Pengfei Li , Bu Jin , Yupeng Zheng , An Liu , Lisen Mu , Qing Su , Qian Zhang
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Yilun Chen Peng Li
This is my paper

Pith reviewed 2026-05-22 13:14 UTC · model grok-4.3

classification 💻 cs.RO cs.AI
keywords lifelong learningmotion planningautonomous drivinglong-tail scenarioslarge language modelsclosed-loop planningmemory augmentationnuPlan benchmark
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The pith

LiloDriver combines memory updates with LLM reasoning in a four-stage pipeline to adapt motion planning to long-tail driving scenarios without retraining.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces LiloDriver as a lifelong learning framework that lets autonomous vehicles handle both everyday and rare driving situations by updating a memory store and consulting a large language model for strategy refinement. Existing rule-based and data-driven planners tend to fail when confronted with unusual events because they cannot incorporate new experience on the fly. A reader who accepts the central claim would expect safer closed-loop behavior in real traffic because the system can draw on accumulated cases rather than relying on a fixed model. The architecture processes raw perception into a scene encoding, retrieves relevant past strategies from memory, and uses LLM guidance to produce the final plan, all while logging new outcomes for future use.

Core claim

The central claim is that a memory-augmented planner generation system integrated with LLM-guided reasoning enables continuous adaptation to new long-tail scenarios in closed-loop settings without any retraining, and that this approach produces superior performance on the nuPlan benchmark in both common and rare driving scenarios compared with static rule-based and learning-based planners.

What carries the argument

The four-stage architecture of perception, scene encoding, memory-based strategy refinement, and LLM-guided reasoning, supported by continuous memory updates that store and retrieve prior planning experiences.

If this is right

  • The planner maintains high performance on common scenarios while improving results on rare ones without separate retraining steps.
  • Continuous memory growth allows the system to incorporate new experiences directly from closed-loop operation.
  • The same pipeline supports human-like reasoning by letting the LLM draw on structured past cases rather than generating plans from scratch.
  • Benchmark gains on nuPlan indicate that combining memory lookup with language-model reasoning scales better than purely static or purely data-driven alternatives.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the memory store grows without bound, future work may need explicit forgetting or compression mechanisms to keep retrieval efficient.
  • The approach could transfer to other sequential decision tasks such as robotic manipulation or navigation in unstructured environments.
  • Real-world deployment would still require additional safety layers to verify LLM outputs before execution.
  • Long-term data collection from deployed vehicles could create a shared memory pool across multiple agents.

Load-bearing premise

The assumption that the combination of memory refinement and LLM reasoning will generate safe, appropriate plans for any unseen long-tail scenario encountered in closed-loop operation.

What would settle it

A controlled test in which the system is placed in a novel long-tail scenario absent from the nuPlan training distribution and from its existing memory, then observed to produce either unsafe trajectories or no valid plan within the required time.

Figures

Figures reproduced from arXiv: 2505.17209 by An Liu, Bu Jin, Huaiyuan Yao, Lisen Mu, Pengfei Li, Peng Li, Qian Zhang, Qing Su, Yilun Chen, Yupeng Zheng.

Figure 1
Figure 1. Figure 1: Comparison of four planning paradigms for autonomous driving [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The overall architecture of LiloDriver comprises four core modules: (1) Environment and Perception, which integrates vectorized maps and agent histories to construct scene context; (2) Scene Encoder, which converts multi-modal perception inputs into latent embeddings for scene representation; (3) Memory and Planner Generation, which organizes clustered scene embeddings and associated few-shot planning expe… view at source ↗
Figure 3
Figure 3. Figure 3: The demonstration of LiloDriver in real-world long-tail scenarios. The first row illustrates a left-turning behavior where the vehicle smoothly [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
read the original abstract

Recent advances in autonomous driving research towards motion planners that are robust, safe, and adaptive. However, existing rule-based and data-driven planners lack adaptability to long-tail scenarios, while knowledge-driven methods offer strong reasoning but face challenges in representation, control, and real-world evaluation. To address these challenges, we present LiloDriver, a lifelong learning framework for closed-loop motion planning in long-tail autonomous driving scenarios. By integrating large language models (LLMs) with a memory-augmented planner generation system, LiloDriver continuously adapts to new scenarios without retraining. It features a four-stage architecture including perception, scene encoding, memory-based strategy refinement, and LLM-guided reasoning. Evaluated on the nuPlan benchmark, LiloDriver achieves superior performance in both common and rare driving scenarios, outperforming static rule-based and learning-based planners. Our results highlight the effectiveness of combining structured memory and LLM reasoning to enable scalable, human-like motion planning in real-world autonomous driving. Our code is available at https://github.com/Hyan-Yao/LiloDriver.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper presents LiloDriver, a lifelong learning framework for closed-loop motion planning in long-tail autonomous driving scenarios. It integrates LLMs with a memory-augmented planner generation system using a four-stage architecture (perception, scene encoding, memory-based strategy refinement, and LLM-guided reasoning) that continuously updates memory to adapt without retraining. The framework is evaluated on the nuPlan benchmark and claims superior performance over static rule-based and learning-based planners in both common and rare scenarios.

Significance. If the empirical claims hold with proper quantitative support, the work could advance adaptive planning for long-tail cases by combining structured memory with LLM reasoning, offering a path toward scalable, human-like closed-loop systems. The public code release at https://github.com/Hyan-Yao/LiloDriver is a clear strength for reproducibility. However, the absence of detailed metrics and safeguards in the core claims limits immediate significance.

major comments (2)
  1. [§4] §4 (Experiments and Results): the abstract and results summary assert superior performance on nuPlan but provide no quantitative metrics (e.g., success rate, collision rate, or comfort scores), ablation studies, or details on long-tail scenario selection and closed-loop rollout protocol, leaving the central empirical claim without visible supporting evidence.
  2. [§3.3] §3.3 (Memory Update Mechanism): the description of continuous memory updates lacks any explicit conflict resolution, forgetting mitigation, or verification step to ensure retrieved memories improve rather than degrade performance on partially overlapping scenarios; this directly undermines the reliability of lifelong adaptation without retraining in closed-loop settings.
minor comments (2)
  1. The four-stage architecture diagram (likely Figure 2) would benefit from clearer labeling of data flow between memory retrieval and LLM reasoning to avoid ambiguity in the pipeline.
  2. Notation for scene encoding and strategy refinement could be formalized with explicit equations or pseudocode to improve clarity for readers unfamiliar with LLM-augmented planners.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments on our manuscript. We have reviewed the points raised and provide the following point-by-point responses. We plan to incorporate revisions to address the concerns regarding empirical evidence and the memory update mechanism.

read point-by-point responses

  1. Referee: §4 (Experiments and Results): the abstract and results summary assert superior performance on nuPlan but provide no quantitative metrics (e.g., success rate, collision rate, or comfort scores), ablation studies, or details on long-tail scenario selection and closed-loop rollout protocol, leaving the central empirical claim without visible supporting evidence.

    Authors: We agree that highlighting quantitative metrics more prominently would strengthen the presentation. The full paper includes detailed results in Section 4 with comparisons on nuPlan, but to make the claims more self-contained, we will revise the abstract to include specific performance numbers (e.g., success rates in common and rare scenarios) and add an overview table summarizing key metrics, ablations, and protocol details. This revision will be made in the next version of the manuscript. revision: yes

  2. Referee: §3.3 (Memory Update Mechanism): the description of continuous memory updates lacks any explicit conflict resolution, forgetting mitigation, or verification step to ensure retrieved memories improve rather than degrade performance on partially overlapping scenarios; this directly undermines the reliability of lifelong adaptation without retraining in closed-loop settings.

    Authors: This is a valid point. The manuscript's §3.3 describes the memory-augmented approach but does not detail mechanisms for handling conflicts or forgetting. We will expand this section to include a description of how the system uses scenario similarity for conflict resolution and incorporates closed-loop performance feedback as a verification step to mitigate potential degradation. These additions will clarify the safeguards for reliable lifelong adaptation. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical framework evaluated on external benchmark

full rationale

The paper presents LiloDriver as a four-stage empirical architecture (perception, scene encoding, memory-based strategy refinement, LLM-guided reasoning) with continuous memory updates, evaluated directly on the nuPlan benchmark for performance in common and rare scenarios. No equations, derivations, or first-principles results are claimed that reduce by construction to fitted inputs, self-definitions, or self-citation chains. Central claims rest on benchmark comparisons against rule-based and learning-based planners rather than internal consistency that loops back to the framework's own parameters or prior author work as the sole justification. The lifelong adaptation is positioned as an engineering system whose stability is assessed externally, satisfying the criteria for a self-contained result against independent benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based on the abstract alone, the central claim rests on standard assumptions in autonomous driving research such as the representativeness of simulation benchmarks and the capability of LLMs for reasoning over driving scenes; no explicit free parameters, ad-hoc axioms, or new invented entities are detailed.

axioms (1)
  • domain assumption nuPlan benchmark scenarios adequately represent real-world long-tail driving conditions for closed-loop evaluation
    The performance claims depend on this benchmark being a valid proxy for the target real-world adaptability.

pith-pipeline@v0.9.0 · 5745 in / 1255 out tokens · 33608 ms · 2026-05-22T13:14:41.364347+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

34 extracted references · 34 canonical work pages · 4 internal anchors

  1. [1]

    Motion planning for autonomous driving: The state of the art and future perspectives,

    S. Teng, X. Hu, P. Deng, B. Li, Y . Li, Y . Ai, D. Yang, L. Li, Z. Xuanyuan, F. Zhu, and L. Chen, “Motion planning for autonomous driving: The state of the art and future perspectives,”IEEE Transactions on Intelligent Vehicles, vol. 8, no. 6, p. 3692–3711, Jun. 2023. [Online]. Available: http://dx.doi.org/10.1109/TIV .2023. 3274536

    work page doi:10.1109/tiv 2023

  2. [2]

    The integration of prediction and planning in deep learning automated driving systems: A review,

    S. Hagedorn, M. Hallgarten, M. Stoll, and A. Condurache, “The integration of prediction and planning in deep learning automated driving systems: A review,” 2024. [Online]. Available: https://arxiv.org/abs/2308.05731

    work page arXiv 2024

  3. [3]

    NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles

    H. Caesar, J. Kabzan, K. S. Tan, W. K. Fong, E. M. Wolff, A. H. Lang, L. Fletcher, O. Beijbom, and S. Omari, “nuplan: A closed-loop ml-based planning benchmark for autonomous vehicles,”CoRR, vol. abs/2106.11810, 2021. [Online]. Available: https://arxiv.org/abs/2106.11810

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  4. [4]

    Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes

    J.-T. Zhai, Z. Feng, J. Du, Y . Mao, J.-J. Liu, Z. Tan, Y . Zhang, X. Ye, and J. Wang, “Rethinking the open-loop evaluation of end- to-end autonomous driving in nuscenes,” 2023. [Online]. Available: https://arxiv.org/abs/2305.10430

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  5. [5]

    Llm4drive: A survey of large language models for autonomous driving.ArXiv, abs/2311.01043, 2023

    Z. Yang, X. Jia, H. Li, and J. Yan, “Llm4drive: A survey of large language models for autonomous driving,” 2024. [Online]. Available: https://arxiv.org/abs/2311.01043

    work page arXiv 2024

  6. [6]

    A survey on multimodal large language models for autonomous driving,

    C. Cui, Y . Ma, X. Cao, W. Ye, Y . Zhou, K. Liang, J. Chen, J. Lu, Z. Yang, K.-D. Liao, T. Gao, E. Li, K. Tang, Z. Cao, T. Zhou, A. Liu, X. Yan, S. Mei, J. Cao, Z. Wang, and C. Zheng, “A survey on multimodal large language models for autonomous driving,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, ...

    work page 2024

  7. [7]

    Forging vision foundation models for autonomous driving: Challenges, methodologies, and opportunities,

    X. Yan, H. Zhang, Y . Cai, J. Guo, W. Qiu, B. Gao, K. Zhou, Y . Zhao, H. Jin, J. Gao, Z. Li, L. Jiang, W. Zhang, H. Zhang, D. Dai, and B. Liu, “Forging vision foundation models for autonomous driving: Challenges, methodologies, and opportunities,”

    work page

  8. [8]

    Available: https://arxiv.org/abs/2401.08045

    [Online]. Available: https://arxiv.org/abs/2401.08045

    work page arXiv

  9. [9]

    Vision language models in autonomous driving: A survey and outlook,

    X. Zhou, M. Liu, E. Yurtsever, B. L. Zagar, W. Zimmer, H. Cao, and A. C. Knoll, “Vision language models in autonomous driving: A survey and outlook,” 2024. [Online]. Available: https: //arxiv.org/abs/2310.14414

    work page arXiv 2024

  10. [10]

    Can vehicle motion planning generalize to realistic long-tail scenarios?

    M. Hallgarten, J. Zapata, M. Stoll, K. Renz, and A. Zell, “Can vehicle motion planning generalize to realistic long-tail scenarios?”

    work page

  11. [11]

    Available: https://arxiv.org/abs/2404.07569

    [Online]. Available: https://arxiv.org/abs/2404.07569

    work page arXiv

  12. [12]

    Parting with misconceptions about learning-based vehicle motion planning,

    D. Dauner, M. Hallgarten, A. Geiger, and K. Chitta, “Parting with misconceptions about learning-based vehicle motion planning,” 2023. [Online]. Available: https://arxiv.org/abs/2306.07962

    work page arXiv 2023

  13. [13]

    Congested traffic states in empirical observations and microscopic simulations,

    M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,”Physical Review E, vol. 62, no. 2, p. 1805–1824, Aug. 2000. [Online]. Available: http://dx.doi.org/10.1103/PhysRevE.62.1805

    work page doi:10.1103/physreve.62.1805 2000

  14. [14]

    Lmm-enhanced safety-critical scenario generation for autonomous driving system testing from non-accident traffic videos,

    H. Tian, X. Han, Y . Zhou, G. Wu, A. Guo, M. Cheng, S. Li, J. Wei, and T. Zhang, “Lmm-enhanced safety-critical scenario generation for autonomous driving system testing from non-accident traffic videos,”

    work page

  15. [15]

    Available: https://arxiv.org/abs/2406.10857

    [Online]. Available: https://arxiv.org/abs/2406.10857

    work page arXiv

  16. [16]

    Planagent: A multi-modal large language agent for closed-loop vehicle motion planning,

    Y . Zheng, Z. Xing, Q. Zhang, B. Jin, P. Li, Y . Zheng, Z. Xia, K. Zhan, X. Lang, Y . Chen, and D. Zhao, “Planagent: A multi-modal large language agent for closed-loop vehicle motion planning,” 2024. [Online]. Available: https://arxiv.org/abs/2406.01587

    work page arXiv 2024

  17. [17]

    A survey on multimodal large language models for autonomous driving,

    C. Cui, Y . Ma, X. Cao, W. Ye, Y . Zhou, K. Liang, J. Chen, J. Lu, Z. Yang, K.-D. Liao, T. Gao, E. Li, K. Tang, Z. Cao, T. Zhou, A. Liu, X. Yan, S. Mei, J. Cao, Z. Wang, and C. Zheng, “A survey on multimodal large language models for autonomous driving,” 2023. [Online]. Available: https://arxiv.org/abs/2311.12320

    work page arXiv 2023

  18. [18]

    End-to-end autonomous driving: Challenges and frontiers,

    L. Chen, P. Wu, K. Chitta, B. Jaeger, A. Geiger, and H. Li, “End-to-end autonomous driving: Challenges and frontiers,” 2024. [Online]. Available: https://arxiv.org/abs/2306.16927

    work page arXiv 2024

  19. [19]

    Baidu Apollo EM Motion Planner

    H. Fan, F. Zhu, C. Liu, L. Zhang, L. Zhuang, D. Li, W. Zhu, J. Hu, H. Li, and Q. Kong, “Baidu apollo em motion planner,” 2018. [Online]. Available: https://arxiv.org/abs/1807.08048

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  20. [20]

    Urban driver: Learning to drive from real-world demonstrations using policy gradients,

    O. Scheel, L. Bergamini, M. Wołczyk, B. Osi ´nski, and P. Ondruska, “Urban driver: Learning to drive from real-world demonstrations using policy gradients,” 2021. [Online]. Available: https://arxiv.org/ abs/2109.13333

    work page arXiv 2021

  21. [21]

    Dtpp: Differentiable joint conditional prediction and cost evaluation for tree policy planning in autonomous driving,

    Z. Huang, P. Karkus, B. Ivanovic, Y . Chen, M. Pavone, and C. Lv, “Dtpp: Differentiable joint conditional prediction and cost evaluation for tree policy planning in autonomous driving,” 2024. [Online]. Available: https://arxiv.org/abs/2310.05885

    work page arXiv 2024

  22. [22]

    Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving,

    Z. Huang, H. Liu, and C. Lv, “Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving,” 2023. [Online]. Available: https://arxiv.org/abs/2303.05760

    work page arXiv 2023

  23. [23]

    Hallgarten, M

    M. Hallgarten, M. Stoll and A. Zell, ”From Prediction to Planning With Goal Conditioned Lane Graph Traversals,” 2023 IEEE 26th International Conference on Intelligent Transporta- tion Systems (ITSC), Bilbao, Spain, 2023, pp. 951-958, doi: 10.1109/ITSC57777.2023.10421854

    work page doi:10.1109/itsc57777.2023.10421854 2023

  24. [24]

    Rethinking imitation-based planner for autonomous driving,

    J. Cheng, Y . Chen, X. Mei, B. Yang, B. Li, and M. Liu, “Rethinking imitation-based planner for autonomous driving,” 2023. [Online]. Available: https://arxiv.org/abs/2309.10443

    work page arXiv 2023

  25. [25]

    H. Yao, L. Da, V . Nandam, J. Turnau, Z. Liu, L. Pang, and H. Wei,CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy Traffic, pp. 409–418. [Online]. Available: https://epubs.siam.org/doi/abs/10.1137/1.9781611978520.43

    work page doi:10.1137/1.9781611978520.43

  26. [26]

    Generative ai in transportation planning: A survey,

    L. Da, T. Chen, Z. Li, S. Bachiraju, H. Yao, L. Li, Y . Dong, X. Hu, Z. Tu, D. Wang, Y . Zhao, B. Zhou, R. Pendyala, B. Stabler, Y . Yang, X. Zhou, and H. Wei, “Generative ai in transportation planning: A survey,” 2025. [Online]. Available: https://arxiv.org/abs/2503.07158

    work page arXiv 2025

  27. [27]

    Surrealdriver: Designing llm-powered generative driver agent framework based on human drivers’ driving-thinking data,

    Y . Jin, R. Yang, Z. Yi, X. Shen, H. Peng, X. Liu, J. Qin, J. Li, J. Xie, P. Gao, G. Zhou, and J. Gong, “Surrealdriver: Designing llm-powered generative driver agent framework based on human drivers’ driving-thinking data,” 2024. [Online]. Available: https://arxiv.org/abs/2309.13193

    work page arXiv 2024

  28. [28]

    Dilu: A knowledge-driven approach to autonomous driving with large language models,

    L. Wen, D. Fu, X. Li, X. Cai, T. Ma, P. Cai, M. Dou, B. Shi, L. He, and Y . Qiao, “Dilu: A knowledge-driven approach to autonomous driving with large language models,”arXiv preprint arXiv:2309.16292, 2023

    work page arXiv 2023

  29. [29]

    Languagempc: Large language models as decision makers for autonomous driving,

    H. Sha, Y . Mu, Y . Jiang, L. Chen, C. Xu, P. Luo, S. E. Li, M. Tomizuka, W. Zhan, and M. Ding, “Languagempc: Large language models as decision makers for autonomous driving,” 2023. [Online]. Available: https://arxiv.org/abs/2310.03026

    work page arXiv 2023

  30. [30]

    Llm-assist: Enhancing closed-loop planning with language-based reasoning,

    S. P. Sharan, F. Pittaluga, V . K. B. G, and M. Chandraker, “Llm-assist: Enhancing closed-loop planning with language-based reasoning,” 2023. [Online]. Available: https://arxiv.org/abs/2401.00125

    work page arXiv 2023

  31. [31]

    Lmdrive: Closed-loop end-to-end driving with large language models,

    H. Shao, Y . Hu, L. Wang, S. L. Waslander, Y . Liu, and H. Li, “Lmdrive: Closed-loop end-to-end driving with large language models,” 2023

    work page 2023

  32. [32]

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

    W. Wang, J. Xie, C. Hu, H. Zou, J. Fan, W. Tong, Y . Wen, S. Wu, H. Deng, Z. Li, H. Tian, L. Lu, X. Zhu, X. Wang, Y . Qiao, and J. Dai, “Drivemlm: Aligning multi-modal large language models with behavioral planning states for autonomous driving,” 2023. [Online]. Available: https://arxiv.org/abs/2312.09245

    work page arXiv 2023

  33. [33]

    A density-based algorithm for discovering clusters in large spatial databases with noise,

    M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” inProceedings of the Second International Conference on Knowledge Discovery and Data Mining, ser. KDD’96. AAAI Press, 1996, p. 226–231

    work page 1996

  34. [34]

    The Llama 3 Herd of Models

    Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al- Dahle, A., ... Vasic, P. (2024). The llama 3 herd of models. arXiv preprint arXiv:2407.21783

    work page internal anchor Pith review Pith/arXiv arXiv 2024