DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models
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/
- Publication:
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arXiv e-prints
- Pub Date:
- September 2023
- DOI:
- 10.48550/arXiv.2309.16292
- arXiv:
- arXiv:2309.16292
- Bibcode:
- 2023arXiv230916292W
- Keywords:
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- Computer Science - Robotics;
- Computer Science - Computation and Language
- E-Print:
- Published as a conference paper at ICLR 2024