English(EN) Neuro-Memory Fuzzy Inference System for Mimicking Human-like Car Following Behavior

新AI系统利用记忆模型模仿人类驾驶行为

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员开发了一种新颖的神经记忆模糊推理系统（NeMeFIS），旨在模仿车辆中人类的跟车行为。这种分层机器学习架构区分了加速和减速，并结合了五种人类记忆：程序性记忆、工作记忆、情景记忆、语义记忆和陈述性记忆。NeMeFIS 模拟了在各种道路类型和车辆条件下驾驶的认知影响，其性能优于线性回归和 LSTM 等传统方法。系统分析表明，陈述性记忆在复杂的刹车决策中起着重要作用，而程序性记忆则控制加速，风险感知是城市驾驶的关键因素。 AI

影响这项研究通过更好地复制人类决策过程，可能带来更直观、更安全的自动驾驶系统。

排序理由该集群包含一篇详细介绍新AI系统的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Nazmul Haque, Md Asif Raihan. Md. Hadiuzzaman · 2026-10-09 04:00

用于模仿类人跟车行为的神经-模糊推理系统

arXiv:2610.11252v1 Announce Type: cross Abstract: 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 memor…

本文如何被排名

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用于模仿类人跟车行为的神经-模糊推理系统

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