Design of an Automobile Status Monitoring Platform Based on Multi-Sensor Data Fusion

With the swift progression of the automotive industry, real-time monitoring of vehicular status has become increasingly paramount. Existing monitoring methodologies are, however, constrained by their inherent limitations. This paper presents an automobile status monitoring platform rooted in multi-sensor data fusion. Data processing and analysis are executed through a synergistic approach, employing the LSTM model combined with a rule engine. The research discerns that this methodology proficiently extracts pertinent information from abundant sensor data, monitoring the automobile's operational state and potential issues both real-time and with precision. This investigation proffers robust backing for vehicular health management and fault prognosis, demonstrating significant practical relevance.