Knowledge of drivers’ mobility patterns is useful for enabling context-aware intelligent vehicle functionality, such as route suggestions, cabin preconditioning, and power management for electric vehicles. Such patterns are often described in terms of the Points of Interest (PoIs) visited by an individual. However, existing PoI extraction methods are general purpose and typically rely on detecting periods of low mobility, meaning that when they are applied to vehicle data, they often extract a large number of false PoIs (for example, incorrectly extracting PoIs due to stopping in traffic), reducing their usefulness. To reduce the number of false PoIs that are extracted, we propose using features derived from vehicle signals, such as the selected gear and status of doors, to classify candidate PoIs and filter out those that are irrelevant. In this paper, we (i) present Activity-based Vehicle PoI Extraction (AVPE), a wrapper method around existing PoI extraction methods, that utilizes a postclustering classification stage to filter out false PoIs, (ii) evaluate the benefits of AVPE compared to three state-of-the-art general purpose PoI extraction algorithms, and (iii) demonstrate the effectiveness of AVPE when applied to real-world driving data.

中文翻译：

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对车辆活动进行分类以改进兴趣点提取

了解驾驶员的移动模式对于启用上下文感知智能车辆功能非常有用，例如电动汽车的路线建议、机舱预处理和电源管理。此类模式通常根据个人访问的兴趣点 (PoI) 进行描述。然而，现有的 PoI 提取方法是通用的，通常依赖于检测低流动性时期，这意味着当它们应用于车辆数据时，它们往往会提取大量虚假的 POI（例如，由于交通堵塞而错误地提取了 POI )，减少它们的用处。为了减少提取的错误 POI 的数量，我们建议使用从车辆信号中提取的特征，例如所选档位和门的状态，对候选 POI 进行分类并过滤掉不相关的那些。