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<p>The analysis of vehicle data is becoming increasingly important in mobility research and vehicle development. Within the collaborative project SofDCar, an innovative perspective on this data was investigated: the consideration of vehicle journeys as processes and their generation from sensor data using process mining. To represent journeys as processes, a&#xa0;transformation from continuous sensor data into discrete process events is necessary. This work bridges this gap by developing a&#xa0;systematic pipeline for transforming vehicle data into process models. The pipeline encompasses the collection of internal vehicle and external data sources, recognition of driving activities, and the generation of process models through process mining methods. The prototypical implementation demonstrates the applicability of the approach using real vehicle data. The evaluation shows that individual journeys can be modeled as process instances and similar journeys as processes with variants. The resulting process models provide new insights into driving patterns and their variability, which can contribute to improving driver assistance systems and driving behavior.</p>

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Process Mining mit Fahrzeugdaten: Von Sensordaten zu Prozessmodellen

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月24日

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  • Fabian Rybinski,
  • Gunther Schiefer

摘要

The analysis of vehicle data is becoming increasingly important in mobility research and vehicle development. Within the collaborative project SofDCar, an innovative perspective on this data was investigated: the consideration of vehicle journeys as processes and their generation from sensor data using process mining. To represent journeys as processes, a transformation from continuous sensor data into discrete process events is necessary. This work bridges this gap by developing a systematic pipeline for transforming vehicle data into process models. The pipeline encompasses the collection of internal vehicle and external data sources, recognition of driving activities, and the generation of process models through process mining methods. The prototypical implementation demonstrates the applicability of the approach using real vehicle data. The evaluation shows that individual journeys can be modeled as process instances and similar journeys as processes with variants. The resulting process models provide new insights into driving patterns and their variability, which can contribute to improving driver assistance systems and driving behavior.