Application of Multimodal Large Language Models in Autonomous Driving
Abstract
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.引用本文复制引用
Md Robiul Islam.Application of Multimodal Large Language Models in Autonomous Driving[EB/OL].(2024-12-20)[2026-04-01].https://arxiv.org/abs/2412.16410.学科分类
自动化技术、自动化技术设备/计算技术、计算机技术
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