Automated event detection has emerged as one of the fundamental practices to monitor the behavior of technical systems by means of sensor data. In the automotive industry, these methods are in high demand for tracing events in time series data. For assessing the active vehicle safety systems, a diverse range of driving scenarios is conducted. These scenarios involve the recording of the vehicle's behavior using external sensors, enabling the evaluation of operational performance. In such setting, automated detection methods not only accelerate but also standardize and objectify the evaluation by avoiding subjective, human-based appraisals in the data inspection. This work proposes a novel event detection method that allows to identify frequency-based events in time series data. To this aim, the time series data is mapped to representations in the time-frequency domain, known as scalograms. After filtering scalograms to enhance relevant parts of the signal, an object detection model is trained to detect the desired event objects in the scalograms. For the analysis of unseen time series data, events can be detected in their scalograms with the trained object detection model and are thereafter mapped back to the time series data to mark the corresponding time interval. The algorithm, evaluated on unseen datasets, achieves a precision rate of 0.97 in event detection, providing sharp time interval boundaries whose accurate indication by human visual inspection is challenging. Incorporating this method into the vehicle development process enhances the accuracy and reliability of event detection, which holds major importance for rapid testing analysis.

翻译：自动事件检测已成为通过传感器数据监控技术系统行为的基础实践之一。在汽车工业中，这类方法在时间序列数据的事件追踪方面需求旺盛。为评估车辆主动安全系统，需开展多样化的驾驶场景测试。这些场景通过外部传感器记录车辆行为，从而评估运行性能。在此背景下，自动化检测方法不仅能加速评估过程，还能通过避免数据检查中基于人工的主观判断，实现评估的标准化与客观化。本文提出一种新颖的事件检测方法，能够识别时间序列数据中的频率事件。为此，将时间序列数据映射至时频域表示（即尺度图）。经滤波处理以增强信号相关部分后，训练目标检测模型识别尺度图中的目标事件对象。对于未知时间序列数据，可通过训练后的目标检测模型在尺度图中检测事件，再将其映射回原始时间序列以标记对应时间区间。该算法在未知数据集上评估后，事件检测精确率达到0.97，且能提供人类目视检查难以精确标注的清晰时间区间边界。将该方法集成至车辆开发流程中，可提升事件检测的准确性与可靠性，这对快速测试分析具有关键意义。