Simulate complex scenarios and optimize algorithm performance.
Realistic AI-driven driving scenarios for autonomous vehicle testing and development.
Data Collection
Gather a comprehensive dataset of real-world driving scenarios, including rare and extreme conditions (e.g., heavy rain, snow, accidents, pedestrian crossings).
Model Fine-Tuning
Fine-tune GPT-4 on the simulation dataset to optimize its ability to generate realistic and dynamic driving scenarios, including traffic patterns, environmental changes, and unexpected events.
System Development
Develop an AI-powered simulation platform that integrates the fine-tuned model to create and test complex driving scenarios for autonomous vehicle algorithms.
System Development
Develop an AI-powered simulation platform that integrates the fine-tuned model to create and test complex driving scenarios for autonomous vehicle algorithms.
Expected Outcomes
This research aims to demonstrate that fine-tuning GPT-4 can significantly enhance the realism and diversity of simulated driving scenarios, thereby improving the robustness and performance of autonomous driving algorithms. The outcomes will contribute to a deeper understanding of how advanced AI models can be adapted for simulation-based testing and optimization. Additionally, the study will highlight the societal impact of AI in advancing autonomous vehicle safety, reducing accidents, and accelerating the development of reliable self-driving technologies.