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English(EN) An Empirical Markov Chain Car-Following (MC-CF) Model

新的马尔可夫链模型超越传统跟车方法

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的跟车模型，称为马尔可夫链跟车（MC-CF），它采用经验概率采样方法。该模型将状态转移表示为马尔可夫过程，并通过从经验分布中随机采样加速度来预测行为。在Waymo Open Motion Dataset (WOMD)上的评估表明，MC-CF变体优于传统的基于物理的模型，并与现代数据驱动的方法具有竞争力。该模型还在Naturalistic Phoenix (PHX)数据集上展示了跨领域可迁移性，并在微观环形道路模拟中显示出可扩展性，在大多数测试场景中显著减少了碰撞。 AI

排序理由 该集群包含一篇详细介绍新模型的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv cs.LG 阅读 →

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新的马尔可夫链模型超越传统跟车方法

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该集群包含一篇详细介绍新模型的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Sungyong Chung, Yanlin Zhang, Nachuan Li, Dana Monzer, Alireza Talebpour ·

    一种经验性马尔可夫链跟车（MC-CF）模型

    arXiv:2603.27909v2 Announce Type: replace-cross Abstract: Car-following behavior is fundamental to traffic flow theory, yet traditional models often fail to capture the stochasticity of naturalistic driving. This paper proposes an empirical probabilistic sampling approach to car-…

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