Applications of large language models and multimodal large models in autonomous driving: a comprehensive review

Date published

2025-04-01

Free to read from

2025-04-22

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

MDPI

Department

Type

Article

ISSN

2504-446X

Format

Citation

Li J, Li J, Yang G, et al., (2025) Applications of large language models and multimodal large models in autonomous driving: a comprehensive review. Drones, Volume 9, Issue 4, April 2025, Article number 238

Abstract

The rapid development of large language models (LLMs) and multimodal large models (MLMs) has introduced transformative opportunities for autonomous driving systems. These advanced models provide robust support for the realization of more intelligent, safer, and efficient autonomous driving. In this paper, we present a systematic review on the integration of LLMs and MLMs in autonomous driving systems. First, we provide an overview of the evolution of LLMs and MLMs, along with a detailed analysis of the architecture of autonomous driving systems. Next, we explore the applications of LLMs and MLMs in key components such as perception, prediction, decision making, planning, multitask processing, and human–machine interaction. Additionally, this paper reviews the core technologies involved in integrating LLMs and MLMs with autonomous driving systems, including multimodal fusion, knowledge distillation, prompt engineering, and supervised fine tuning. Finally, we provide an in-depth analysis of the major challenges faced by autonomous driving systems powered by large models, offering new perspectives for future research. Compared to existing review articles, this paper not only systematically examines the specific applications of LLMs and MLMs in autonomous driving systems but also delves into the key technologies and potential challenges involved in their integration. By comprehensively organizing and analyzing the current literature, this review highlights the application potential of large models in autonomous driving and offers insights and recommendations for improving system safety and efficiency.

Description

Software Description

Software Language

Github

Keywords

46 Information and Computing Sciences, 4602 Artificial Intelligence, 40 Engineering, 46 Information and computing sciences

DOI

Rights

Attribution 4.0 International

Relationships

Relationships

Resources

Funder/s