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Vision Language Models in Autonomous Driving: A Survey and Outlook

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arxiv 2310.14414 v2 pith:U6JNUKXX submitted 2023-10-22 cs.CV cs.AI

Xingcheng Zhou , Mingyu Liu , Ekim Yurtsever , Bare Luka Zagar , Walter Zimmer , Hu Cao , Alois C. Knoll This is my paper · ORCID
classification cs.CVcs.AI
keywords drivingautonomouslanguagemodelsapplicationscurrentdatasurvey
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The applications of Vision-Language Models (VLMs) in the field of Autonomous Driving (AD) have attracted widespread attention due to their outstanding performance and the ability to leverage Large Language Models (LLMs). By incorporating language data, driving systems can gain a better understanding of real-world environments, thereby enhancing driving safety and efficiency. In this work, we present a comprehensive and systematic survey of the advances in vision language models in this domain, encompassing perception and understanding, navigation and planning, decision-making and control, end-to-end autonomous driving, and data generation. We introduce the mainstream VLM tasks in AD and the commonly utilized metrics. Additionally, we review current studies and applications in various areas and summarize the existing language-enhanced autonomous driving datasets thoroughly. Lastly, we discuss the benefits and challenges of VLMs in AD and provide researchers with the current research gaps and future trends.

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Cited by 4 Pith papers

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