Enhancing Autonomous Driving using Multiple Large Language Models (MLLMs)

Nowadays, leveraging advanced technologies like Generative Artificial Intelligence (AI), particularly Large Language Models (LLMs) such as GPT, holds promise in revolutionizing safety measures and resource optimization. However, while these general-purpose LLMs excel in various tasks, they may lack context specificity. Domain-specific LLMs, such as those tailored for biomedicine and transportation, are emerging to address this issue. For instance, in transportation, Multimodal Large Language Models (MLLMs) show the potential to enhance autonomous driving and traffic safety decision-making. Efforts also focus on using LLMs for accident prediction and prevention, with lightweight models proposed for real-time interventions. Despite advancements, there’s still a need for versatile AI agents capable of adapting to diverse scenarios. LLMs offer a foundation for such agents, with ongoing research exploring their potential. This project aims to integrate multiple LLM-based Intelligent agents into a framework for autonomous driving, enhancing decision-making and public safety by reducing accident risks. This initiative seeks to explore the use of MLLMs and AI agents in developing a novel autonomous driving framework.

Faculty Supervisor:

Wael Jaafar

Student:

Partner:

Mediterranean Institute of Technology

Discipline:

Computer science

Sector:

Artificial Intelligence; Automotive; Energy and Utilities

University:

École de technologie supérieure

Program: