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DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models (2309.16292v3)

Published 28 Sep 2023 in cs.RO and cs.CL

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 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/

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Citations (102)
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Summary

  • The paper introduces DiLu, a novel framework that integrates structured domain knowledge with large language models to improve autonomous driving.
  • Its methodology combines advanced natural language processing with traditional perception systems to interpret complex driving scenarios.
  • Preliminary results indicate enhanced decision-making capabilities and robustness in dynamic traffic conditions, underscoring its practical implications.

Analysis of the Provided Arxiv Template

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