Training SimCopilot with ChatGPT and Llama: Efficient and Diverse Dialogue-Based Language Models for Autonomous Vehicle Development

The article begins by describing the limitations of traditional methods for training autonomous vehicles, which rely on simple and repetitive descriptions of object motions. The authors propose SimCopilot as a new approach that leverages the power of human-like language models to generate more diverse and natural language descriptions of object interactions.
The proposed method involves using a dialogue-based language model to rewrite the initial description of object motions into a more human-like wording style. This process helps to create a more comprehensive understanding of the interactions between objects in a road scene, while also providing a more engaging and realistic training experience for autonomous vehicles.
The authors evaluate the effectiveness of SimCopilot by conducting user studies with 60 participants who are tasked with creating natural language descriptions of object interactions in road scenes. The results show that SimCopilot outperforms traditional methods in terms of both accuracy and comprehensiveness, providing a more realistic and engaging training experience for autonomous vehicles.
Overall, the article provides a novel approach to training autonomous vehicles by leveraging the power of human-like language models to generate natural language descriptions of object interactions in road scenes. The proposed method has the potential to improve the accuracy and comprehensiveness of autonomous vehicle training, while also providing a more engaging and realistic experience for drivers.