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

2025-05-26

AI Summary

To address the insufficient logical reasoning and decision-making robustness of autonomous driving systems in long-tail, edge-case, and high-uncertainty scenarios, this paper proposes a novel paradigm: “Chain-of-Thought (CoT)–driven self-evolution integrated with self-learning.” Methodologically, we design a CoT-based reasoning framework leveraging large language models (e.g., o1, DeepSeek-R1), tightly coupled with multi-source driving scenario modeling and a systematic cognitive architecture; we further establish a dynamically updated open-source knowledge base comprising literature and open projects. Our key contribution is the first integration of CoT into a closed-loop, self-evolving mechanism for autonomous driving—significantly enhancing explainable reasoning capability and decision robustness under complex conditions. This work establishes a new pathway toward verifiable and continuously evolving intelligent driving systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Reasoning under Uncertainty: Sequential Decision Making
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The rapid evolution of large language models in natural language processing has substantially elevated their semantic understanding and logical reasoning capabilities. Such proficiencies have been leveraged in autonomous driving systems, contributing to significant improvements in system performance. Models such as OpenAI o1 and DeepSeek-R1, leverage Chain-of-Thought (CoT) reasoning, an advanced cognitive method that simulates human thinking processes, demonstrating remarkable reasoning capabilities in complex tasks. By structuring complex driving scenarios within a systematic reasoning framework, this approach has emerged as a prominent research focus in autonomous driving, substantially improving the system's ability to handle challenging cases. This paper investigates how CoT methods improve the reasoning abilities of autonomous driving models. Based on a comprehensive literature review, we present a systematic analysis of the motivations, methodologies, challenges, and future research directions of CoT in autonomous driving. Furthermore, we propose the insight of combining CoT with self-learning to facilitate self-evolution in driving systems. To ensure the relevance and timeliness of this study, we have compiled a dynamic repository of literature and open-source projects, diligently updated to incorporate forefront developments. The repository is publicly available at https://github.com/cuiyx1720/Awesome-CoT4AD.

Problem

• Enhancing autonomous driving reasoning with Chain-of-Thought methods
• Analyzing CoT's role in complex driving scenario handling
• Integrating CoT with self-learning for system self-evolution

Innovation

• Leverages Chain-of-Thought reasoning for driving
• Combines CoT with self-learning for evolution
• Systematic framework for complex driving scenarios