系统梳理知识融合在自动驾驶预测与规划中的应用方法。
Knowledge Integration Strategies in Autonomous Vehicle Prediction and Planning: A Comprehensive Survey
- 按知识表示与融合方式分类，涵盖符号到神经符号混合架构
- 强调可解释AI与形式化验证在安全系统中的关键作用
- 适合关注自动驾驶系统可信性与知识融合技术的研究者
本综述系统分析了知识驱动方法在自动驾驶系统中轨迹预测与规划的应用。从领域知识、交通规则到常识推理，全面考察了多种知识表示与融合策略。文章将方法按知识表达与集成方式分为纯符号与混合神经符号架构，并探讨逻辑编程、基础模型、强化学习等新兴技术在知识融合中的进展。研究揭示了当前趋势：可解释人工智能的重要性提升、形式化验证在安全关键系统中的角色增强，以及传统知识表示与现代机器学习结合的混合方法潜力。本文还识别出该领域的关键挑战、发展机遇与未来研究方向。
原文摘要 · Abstract (English)
This comprehensive survey examines the integration of knowledge-based approaches in autonomous driving systems, specifically focusing on trajectory prediction and planning. We extensively analyze various methodologies for incorporating domain knowledge, traffic rules, and commonsense reasoning into autonomous driving systems. The survey categorizes and analyzes approaches based on their knowledge representation and integration methods, ranging from purely symbolic to hybrid neuro-symbolic architectures. We examine recent developments in logic programming, foundation models for knowledge representation, reinforcement learning frameworks, and other emerging technologies incorporating domain knowledge. This work systematically reviews recent approaches, identifying key challenges, opportunities, and future research directions in knowledge-enhanced autonomous driving systems. Our analysis reveals emerging trends in the field, including the increasing importance of interpretable AI, the role of formal verification in safety-critical systems, and the potential of hybrid approaches that combine traditional knowledge representation with modern machine learning techniques.
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