用于类人跟车行为模拟的神经记忆模糊推理系统
Neuro-Memory Fuzzy Inference System for Mimicking Human-like Car Following Behavior
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中文总结 AI 辅助
本研究提出整合五种人类记忆类型的NeMeFIS架构，建模跟车行为的非对称加减速，经54种模型验证，其性能优于线性回归、ANFIS、LSTM等模型，可用于CAVs的类人决策系统以提升交通安全。
中文摘要 AI 辅助
本研究提出了神经记忆模糊推理系统（NeMeFIS），这是一种分层机器学习架构，通过整合程序、工作、情景、语义和陈述性这五种人类记忆类型，非对称地建模跟车行为中的加速与减速过程。该系统通过元启发式算法将外部变量与记忆功能关联，并通过因子分析和p值分析进行验证，揭示了 arterial（干线）、collector（集散道路）和 rural highway（乡村公路）走廊上不同类型车辆的潜在认知影响。54种不同训练模型的结果强调了由驾驶员感知极限和认知负荷形成的认知阈值。训练后的NeMeFIS模型在复制现实驾驶行为方面优于传统统计模型和常规机器学习模型，包括与线性回归、ANFIS和LSTM架构的比较。模糊规则分析显示，陈述性记忆需要最多的规则，尤其是在减速期间，这表明复杂的制动决策依赖于陈述性记忆；程序记忆驱动加速，而语义和陈述性记忆引导减速；风险感知也成为关键因素，尤其在城市道路上。NeMeFIS在异构和同构数据集上均得到验证，为驾驶员认知建模提供了稳健框架，其研究结果支持心理治疗应用以及联网自动驾驶汽车（CAVs）中类人自适应决策系统的开发，以提升交通安全。
英文摘要
This study presents the Neuro-Memory Fuzzy Inference System (NeMeFIS), a hierarchical machine learning architecture that asymmetrically models acceleration and deceleration in car following behavior by integrating five human memory types procedural, working, episodic, semantic, and declarative. By linking external variables to memory functions via metaheuristics and validating them through factor and p-value analyses, NeMeFIS uncovers latent cognitive influences across Arterial, Collector, and Rural Highway corridors for different types of vehicles. Results from 54 different trained models emphasize cognitive thresholds shaped by driver perception limits and cognitive load. The trained NeMeFIS models outperform traditional statistical and conventional machine learning models in replicating realistic driving behavior, including comparisons with Linear Regression, ANFIS, and LSTM architectures. Fuzzy rule analysis reveals that declarative memory demands the highest rule, especially during deceleration, indicating complex braking decisions. Procedural memory drives acceleration, while semantic and declarative memory guide deceleration. Risk perception also emerges as a key factor, particularly on urban roads. Validated on both heterogeneous and homogeneous datasets, NeMeFIS offers a robust framework for modeling driver cognition. The findings support psychotherapeutic applications and the development of adaptive, human-like decision systems in Connected and Autonomous Vehicles (CAVs) to enhance traffic safety.
