الملخص
Autonomous driving decision systems must balance safety, efficiency, and social norms in complex traffic interactions. Philosophical and ethical considerations have received limited attention in existing autonomous driving decision-making approaches based on numerical optimization, sequence prediction, and large language models (LLMs). We propose Chinese Philosophical Wisdom-Guided Driving (CPW-Drive), a closed-loop retrieval-augmented generation (RAG) framework that incorporates value guidance derived from Chinese philosophy into autonomous driving decision-making. Using Chinese Confucian thought as its knowledge source, CPW-Drive consolidates LLM-extracted keywords from relevant classical texts into driving-relevant value principles through manual screening and validation. It then contextualizes these principles through scenario-specific cases to form retrievable and reusable value guidance. We further propose Physics-aware Spatial Similarity Retrieval (PSSR), which compares vehicle layouts and velocity-extrapolated states to retrieve physically relevant historical cases. On Highway-env's multilane highway-driving task, CPW-Drive achieves success rates of 93.0%, 86.0%, and 72.0% across three traffic configurations. These results outperform the strongest baseline by 8.0, 22.5, and 25.0 percentage points, respectively. Across all configurations, CPW-Drive achieves the highest collision-free step count and maintains a low lane-change frequency. The results suggest that structured value guidance can improve simulated closed-loop safety and stability while introducing efficiency and latency trade-offs.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Bi, X., Ma, X., Sun, Y., Huang, T., Liu, C., Huang, B., Yang, H., & Mo, B. (2026). Retrieval-Augmented Large Language Model Decision-Making for Autonomous Driving Guided by Chinese Philosophical Wisdom. https://omanscience.com/ar/articles/retrieval-augmented-large-language-model-decision-making-for-autonomous-driving-guided-by-chinese-philosophical-wisdom
MLA 9
Bi, Xiaojun, et al. "Retrieval-Augmented Large Language Model Decision-Making for Autonomous Driving Guided by Chinese Philosophical Wisdom." https://omanscience.com/ar/articles/retrieval-augmented-large-language-model-decision-making-for-autonomous-driving-guided-by-chinese-philosophical-wisdom.
شيكاغو (المؤلف–التاريخ)
Bi, Xiaojun, Xiaoyuan Ma, Yiwen Sun, Tianren Huang, Chaoran Liu, Bokai Huang, Hao Yang, and Baichuan Mo. 2026. "Retrieval-Augmented Large Language Model Decision-Making for Autonomous Driving Guided by Chinese Philosophical Wisdom." https://omanscience.com/ar/articles/retrieval-augmented-large-language-model-decision-making-for-autonomous-driving-guided-by-chinese-philosophical-wisdom.
هارفارد
Bi, X., Ma, X., Sun, Y., Huang, T., Liu, C., Huang, B., Yang, H. and Mo, B. (2026) 'Retrieval-Augmented Large Language Model Decision-Making for Autonomous Driving Guided by Chinese Philosophical Wisdom', Available at: https://omanscience.com/ar/articles/retrieval-augmented-large-language-model-decision-making-for-autonomous-driving-guided-by-chinese-philosophical-wisdom.
فانكوفر
Bi X, Ma X, Sun Y, Huang T, Liu C, Huang B, et al. Retrieval-Augmented Large Language Model Decision-Making for Autonomous Driving Guided by Chinese Philosophical Wisdom. https://omanscience.com/ar/articles/retrieval-augmented-large-language-model-decision-making-for-autonomous-driving-guided-by-chinese-philosophical-wisdom
IEEE
X. Bi, X. Ma, Y. Sun, T. Huang, C. Liu, B. Huang, H. Yang, and B. Mo, "Retrieval-Augmented Large Language Model Decision-Making for Autonomous Driving Guided by Chinese Philosophical Wisdom," https://omanscience.com/ar/articles/retrieval-augmented-large-language-model-decision-making-for-autonomous-driving-guided-by-chinese-philosophical-wisdom.