Application of Multimodal Large Language Models in Autonomous Driving

TL;DR

This paper explores the use of multimodal large language models to improve autonomous driving systems by enhancing scene understanding, prediction, and decision-making through a specialized VQA dataset and Chain of Thought reasoning.

Contribution

It introduces a novel application of MLLMs in AD, including a new VQA dataset and a detailed analysis of decision-making processes.

Findings

MLLM improves AD decision-making accuracy

VQA dataset enhances model fine-tuning

Chain of Thought reasoning refines predictions

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.