CoT-Drive combines large language models with chain-of-thought prompting to enhance motion forecasting for safer autonomous driving systems, while remaining efficient enough for edge devices.

• Leverages LLMs' advanced scene understanding for more accurate predictions
• Implements teacher-student knowledge distillation to transfer capabilities to lightweight models
• Ensures real-time performance on edge devices without sacrificing accuracy
• Addresses critical safety requirements for autonomous driving applications

This research represents a significant engineering breakthrough by successfully integrating sophisticated language model reasoning into time-sensitive autonomous driving systems, potentially improving both safety and performance in real-world applications.

Original Paper: CoT-Drive: Efficient Motion Forecasting for Autonomous Driving with LLMs and Chain-of-Thought Prompting