LLM-Powered Synthetic Environments for Self-Driving Scenarios


  • Oluwanifemi Adebayo Moses Adekanye Bowen University




Synthetic Environments, Large Language Models (LLMs), Self-Driving Scenarios


This paper outlines a proposal exploring the potential use of Large Language Models (LLMs), particularly GPT-4, in crafting realistic synthetic environments for self-driving scenarios. The envisioned approach involves dynamic scene generation within game engines, leveraging LLMs to introduce challenging elements for autonomous vehicles. The proposed evaluation process outlines assessments such as realistic testing, safety metrics, and user interaction, aiming to set the stage for potential improvements in self-driving system performance. The paper aims to contribute to the AI field by discussing how LLMs could be utilized to create valuable testing grounds for autonomous vehicles, potentially fostering the development of more robust self-driving technology. The envisioned impact is the eventual enhancement of road safety and the possible acceleration of the adoption of autonomous vehicles, paving the way for a future with safer and more efficient transportation.




How to Cite

Adekanye, O. A. M. (2024). LLM-Powered Synthetic Environments for Self-Driving Scenarios. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23721-23723. https://doi.org/10.1609/aaai.v38i21.30540