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Application of Multimodal Large Language Models in Autonomous Driving

Application of Multimodal Large Language Models in Autonomous Driving

来源:Arxiv_logoArxiv
英文摘要

In this era of technological advancements, several cutting-edge techniques are being implemented to enhance Autonomous Driving (AD) systems, focusing on improving safety, efficiency, and adaptability in complex driving environments. However, AD still faces some problems including performance limitations. To address this problem, we conducted an in-depth study on implementing the Multi-modal Large Language Model. We constructed a Virtual Question Answering (VQA) dataset to fine-tune the model and address problems with the poor performance of MLLM on AD. We then break down the AD decision-making process by scene understanding, prediction, and decision-making. Chain of Thought has been used to make the decision more perfectly. Our experiments and detailed analysis of Autonomous Driving give an idea of how important MLLM is for AD.

Md Robiul Islam

自动化技术、自动化技术设备计算技术、计算机技术

Md Robiul Islam.Application of Multimodal Large Language Models in Autonomous Driving[EB/OL].(2024-12-20)[2025-08-18].https://arxiv.org/abs/2412.16410.点此复制

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