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

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dc.contributor.authorLi, Jing
dc.contributor.authorLi, Jingyuan
dc.contributor.authorYang, Guo
dc.contributor.authorYang, Lie
dc.contributor.authorChi, Haozhuang
dc.contributor.authorYang, Lichao
dc.date.accessioned2025-04-22T15:27:55Z
dc.date.available2025-04-22T15:27:55Z
dc.date.freetoread2025-04-22
dc.date.issued2025-04-01
dc.date.pubOnline2025-03-24
dc.description.abstractThe 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.
dc.description.journalNameDrones
dc.identifier.citationLi 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
dc.identifier.eissn2504-446X
dc.identifier.elementsID567367
dc.identifier.issn2504-446X
dc.identifier.issueNo4
dc.identifier.paperNo238
dc.identifier.urihttps://doi.org/10.3390/drones9040238
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23791
dc.identifier.volumeNo9
dc.languageEnglish
dc.language.isoen
dc.publisherMDPI
dc.publisher.urihttps://www.mdpi.com/2504-446X/9/4/238
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.subject40 Engineering
dc.subject46 Information and computing sciences
dc.titleApplications of large language models and multimodal large models in autonomous driving: a comprehensive review
dc.typeArticle
dcterms.dateAccepted2025-03-21

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