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

TL;DR

LiloDriver is a lifelong learning framework that combines large language models and memory-augmented planning to improve autonomous driving in rare and complex scenarios.

Contribution

It introduces a novel architecture integrating LLMs with memory for continuous adaptation without retraining in autonomous driving.

Findings

Outperforms static rule-based and learning-based planners on nuPlan benchmark.

Achieves superior performance in both common and rare driving scenarios.

Demonstrates effective integration of memory and LLM reasoning for scalable motion planning.

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.