Summary

Large Language Models Powered Context-aware Motion Prediction presents a novel approach that enhances motion prediction tasks in autonomous driving using Large Language Models (LLMs).

• Systematic prompt engineering visualizes complex traffic environments as image and text prompts, enriching traffic context information.
• Integrating LLM-informed context with the prediction model resulted in enhanced accuracy of motion prediction.
• The proposed cost-effective deployment method shows that even minimal LLM augmentation can significantly improve performance.

Opinion: This research signifies a major leap forward in integrating AI models to extract contextual information for autonomous systems, potentially leading to safer and more efficient autonomous vehicles.