DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models

TL;DR

DiLu introduces a knowledge-driven autonomous driving framework using large language models that enhances decision-making, generalization, and real-world applicability by integrating reasoning, reflection, and experience accumulation.

Contribution

This work is the first to leverage knowledge-driven capabilities of LLMs for autonomous vehicle decision-making, integrating reasoning and reflection modules.

Findings

DiLu outperforms reinforcement learning methods in generalization.

It can directly learn from real-world datasets.

Demonstrates potential for practical deployment.

Abstract

Recent advancements in autonomous driving have relied on data-driven approaches, which are widely adopted but face challenges including dataset bias, overfitting, and uninterpretability. Drawing inspiration from the knowledge-driven nature of human driving, we explore the question of how to instill similar capabilities into autonomous driving systems and summarize a paradigm that integrates an interactive environment, a driver agent, as well as a memory component to address this question. Leveraging large language models (LLMs) with emergent abilities, we propose the DiLu framework, which combines a Reasoning and a Reflection module to enable the system to perform decision-making based on common-sense knowledge and evolve continuously. Extensive experiments prove DiLu's capability to accumulate experience and demonstrate a significant advantage in generalization ability over reinforcement learning-based methods. Moreover, DiLu is able to directly acquire experiences from real-world datasets which highlights its potential to be deployed on practical autonomous driving systems. To the best of our knowledge, we are the first to leverage knowledge-driven capability in decision-making for autonomous vehicles. Through the proposed DiLu framework, LLM is strengthened to apply knowledge and to reason causally in the autonomous driving domain. Project page: https://pjlab-adg.github.io/DiLu/