Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects

Area of Science:

• Artificial Intelligence
• Autonomous Driving Systems
• Natural Language Processing

Background:

• Large language models (LLMs) demonstrate advanced semantic understanding and reasoning.
• LLMs are increasingly integrated into autonomous driving (AD) systems, improving performance.
• Chain-of-thought (CoT) reasoning, mimicking human cognition, enhances LLM capabilities in complex tasks.

Purpose of the Study:

• To investigate the impact of CoT reasoning on autonomous driving models.
• To systematically analyze the motivations, methodologies, challenges, and future directions of CoT in AD.
• To propose novel approaches for integrating CoT with self-learning in driving systems.

Main Methods:

• Comprehensive literature review on CoT in autonomous driving.
• Systematic analysis of existing research and methodologies.
• Identification of challenges and future research avenues.
• Proposal of a CoT and self-learning integration framework.

Main Results:

• CoT reasoning significantly improves the handling of complex scenarios in autonomous driving.
• Identified key challenges include computational cost and real-world validation.
• The study provides a structured overview of CoT applications in AD.
• A repository of resources for CoT in AD is maintained.

Conclusions:

• CoT is a pivotal technique for advancing reasoning in autonomous driving.
• Combining CoT with self-learning offers a promising path for adaptive and evolving driving systems.
• Continued research is essential to overcome current limitations and unlock the full potential of CoT in AD.