Application of Multimodal Large Language Models in Autonomous Driving
This work addresses safety and efficiency problems for autonomous driving systems, but it appears incremental as it applies existing methods to a specific domain.
The paper tackled performance limitations in autonomous driving by implementing a multimodal large language model, using a virtual question answering dataset for fine-tuning and chain of thought for decision-making, resulting in improved scene understanding and decision processes.
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.