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首页> 外文期刊>Promet-traffic & transportation >CYCLIST'S INTENTION IDENTIFICATION ON PEDESTRIAN-BICYCLE MIXED SECTIONS BASED ON PHASE-FIELD COUPLING THEORY
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CYCLIST'S INTENTION IDENTIFICATION ON PEDESTRIAN-BICYCLE MIXED SECTIONS BASED ON PHASE-FIELD COUPLING THEORY

机译：基于相场耦合理论的自行车双行混合段意图识别

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摘要

Bicycle is one of the main factors that affects the traffic safety and capacity on pedestrian-bicycle mixed traffic sections. It is important for implementing the warning of bicycle safety and improving the active safety to identify the cyclists' intention in the mixed traffic environments under the condition of the "Internet of Things". The phase-field coupling theory has been developed in this paper to comprehensively analyse the generation, spring up, increase, transfer, regression and reduction method of the traffic phase. The adaptive genetic algorithm based on the Information entropy has been used to extract feature vectors of different types of cyclists for intention identification from the reduced pedestrian-bicycle traffic phase, and the theory of evidence has been provided here to build the identification model. The experimental verification snows that the extraction method of cyclists' intention feature vector and identification model are scientific and reasonable. The theoretical basis can be applied to establishing the pedestrian-bicycle interactive security system.
机译：自行车是影响行人自行车混合交通路段交通安全和通行能力的主要因素之一。对于实施“自行车安全警告”和提高主动安全性，以在“物联网”条件下识别混合交通环境中骑车人的意图，这一点很重要。本文提出了相场耦合理论，对交通阶段的产生，弹起，增加，传递，回归和减少方法进行了综合分析。基于信息熵的自适应遗传算法已被用于从减少的行人自行车交通阶段中提取不同类型的骑行者的特征向量以进行意图识别，并为建立识别模型提供了证据理论。实验证明，骑车人意图特征向量的提取方法和识别模型是科学合理的。该理论基础可用于建立行人自行车交互式安全系统。

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