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.

翻译：近年来，自动驾驶研究致力于开发鲁棒、安全且自适应的运动规划器。然而，现有的基于规则和数据驱动的规划器缺乏对长尾场景的适应性，而知识驱动方法虽具备强大推理能力，却在表征、控制及实际部署评估中面临挑战。为应对这些问题，我们提出LiloDriver——一种面向长尾自动驾驶场景闭环运动规划的终身学习框架。该框架通过将大语言模型（LLMs）与增强记忆的规划器生成系统相结合，无需重新训练即可持续适应新场景。其架构包含感知、场景编码、基于记忆的策略优化及LLM引导推理四个阶段。在nuPlan基准测试中的评估表明，LiloDriver在常规与罕见驾驶场景中均展现出卓越性能，超越了静态规则及学习型规划器。实验结果凸显了结构化记忆与LLM推理的协同效应，为真实自动驾驶场景中可扩展、类人的运动规划提供了可行方案。我们的代码已开源至https://github.com/Hyan-Yao/LiloDriver。