Designing and Developing DirectorAI: An AI Assistant for Generating Vehicle Simulation Scenarios

Abstract

This thesis investigates the use and potential benefits of AI in automating the generation of vehicle simulation scenarios. Focused on enhancing the usability of Director, a scripting tool for Volvo Cars’ Product Simulator software, this project involved designing and developing DirectorAI, an AI assistant featuring a chatbot interface. Using large language models, the research explored how to create effective and reliable scenarios without domain-specific model training. Moreover, the project identified limitations that emerge when deploying general-purpose language models in complex, domain-specific environments, alongside design patterns that enable these models to function effectively as assistants. The primary research question addressed was: "What design choices or patterns enable general-purpose language models to function as effective assistants in complex, domain-specific software environments without domain-specific training?" The outcome and findings demonstrated that the success of general-purpose LLMs in domain-specific environments relies less on model modification and more on how the system is designed to supply the model with relevant information. By iteratively crafting system prompts that embed domain context, constraints, and examples, DirectorAI was able to perform effectively without the need for custom training or fine-tuning. Through prototyping and user evaluation, several key design patterns were identified that enabled the assistant to support complex workflows within the existing simulation software. This research emphasized the importance of interaction design in shaping the utility and usability of AI-assisted systems. By identifying and analyzing the design choices and patterns that facilitate the effective use of general-purpose LLMs in domainspecific environments, this thesis contributes to the understanding of how AI-assisted tools can be developed for complex simulation scenarios, offering valuable insights for future applications. Ultimately, this study demonstrated the potential of AI to significantly improve the efficiency and reliability of vehicle simulation scenarios, with implications for the automotive industry and beyond.