Analyzing large volumes of real-world driving data is essential for providing meaningful and reliable insights into real-world trips, scenarios, and human driving behaviors. To this end, we developed a multi-level data processing approach that adds new information, segments data, and extracts desired parameters. Leveraging a confidential but extensive dataset (over 1 million km), this approach leads to three levels of in-depth analysis: trip, scenario, and driving. The trip-level analysis explains representative properties observed in real-world trips, while the scenario-level analysis focuses on scenario conditions resulting from road events that reduce vehicle speed. The driving-level analysis identifies the cause of driving regimes for specific situations and characterizes typical human driving behaviors. Such analyses can support the design of both trip- and scenario-based tests, the modeling of human drivers, and the establishment of guidelines for connected and automated vehicles.

翻译：分析海量真实世界驾驶数据对于获取真实世界行程、场景及人类驾驶行为方面有意义且可靠的洞察至关重要。为此，我们开发了一种多层次数据处理方法，该方法能够添加新信息、分割数据并提取所需参数。利用一个保密但规模庞大的数据集（超过100万公里），该方法实现了三个层次的深度分析：行程级、场景级和驾驶级。行程级分析阐释了真实世界行程中观察到的代表性特征，而场景级分析则聚焦于由导致车辆减速的道路事件所产生的场景条件。驾驶级分析识别了特定情境下驾驶模式形成的原因，并刻画了典型的人类驾驶行为特征。此类分析可为基于行程和场景的测试设计、人类驾驶员建模以及网联自动驾驶车辆指南的制定提供支持。