The invention discloses a backward early warning method and device based on multi-sensor fusion. The method comprises the following steps: acquiring data acquired by each sensor in real time; monitoring the working state of each sensor by using a fault detection and isolation model according to the obtained data, and isolating the data of the fault sensor according to the working state of each sensor to obtain normal data and fault information; according to the normal data and the fault information, adjusting the weight corresponding to each sensor by using a dynamic weighted data fusion model to obtain weight information and normal data corresponding to each sensor; and according to the weight information and normal data corresponding to each sensor obtained by dynamic weighting, sensing by using an environment sensing model to obtain an environment sensing result, and triggering a corresponding backward early warning function according to the environment sensing result. According to the invention, a high-performance backward early warning function can still be maintained when one or more sensors fail.

    本发明公开了一种基于多传感器融合的后向预警方法及装置,包括:获取各传感器实时采集的数据;根据获取的数据,利用故障检测与隔离模型监测各传感器的工作状态,并根据各传感器的工作状态隔离故障传感器的数据,得到正常数据和故障信息;根据所述正常数据和所述故障信息,利用动态加权数据融合模型调整各传感器对应的权重,得到各传感器对应的权重信息和正常数据;根据动态加权得到的各传感器对应的权重信息和正常数据,利用环境感知模型进行感知,得到环境感知结果,并根据所述环境感知结果触发对应的后向预警功能。本发明提出了实现了在单一或多个传感器失效时仍能保持高性能的后向预警功能。


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    Titel :

    Backward early warning method and device based on multi-sensor fusion


    Weitere Titelangaben:

    一种基于多传感器融合的后向预警方法及装置


    Erscheinungsdatum :

    19.11.2024


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G01S RADIO DIRECTION-FINDING , Funkpeilung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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