Application of Multimodal Large Language Models in Autonomous Driving

• URL: http://arxiv.org/abs/2412.16410v1
• Date: Sat, 21 Dec 2024 00:09:52 GMT
• Title: Application of Multimodal Large Language Models in Autonomous Driving
• Authors: Md Robiul Islam,
• Abstract summary: We conduct in-depth study on implementing the Multi-modal Large Language Model. We address problems with the poor performance of MLLM on Autonomous Driving. We then break down the AD decision-making process by scene understanding, prediction, and decision-making.
• Score: 1.8181868280594944
• License:
• 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.

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