Facultad de Ingeniería, Universidad Andres Bello, Av. Antonio Varas 880, Santiago 7500971, Chile.
Institute of Applied Mathematics and Information Technologies, National Research Council (IMATI-CNR), 20133 Milano, Italy.
Sensors (Basel). 2023 Jan 28;23(3):1440. doi: 10.3390/s23031440.
In the last decade, a large amount of data from vehicle location sensors has been generated due to the massification of GPS systems to track them. This is because these sensors usually include multiple variables such as position, speed, angular position of the vehicle, etc., and, furthermore, they are also usually recorded in very short time intervals. On the other hand, routes are often generated so that they do not correspond to reality, due to artifacts such as buildings, bridges, or sensor failures and where, due to the large amount of data, visual analysis of human expert is unable to detect genuinely anomalous routes. The presence of such abnormalities can lead to faulty sensors being detected which may allow sensor replacement to reliably track the vehicle. However, given the reliability of the available sensors, there are very few examples of such anomalies, which can make it difficult to apply supervised learning techniques. In this work we propose the use of unsupervised deep neural network models based on stacked autoencoders to detect anomalous routes in vehicles within Santiago de Chile. The results show that the proposed model is capable of effectively detecting anomalous paths in real data considering validation given by an expert user, reaching a performance of 82.1% on average. As future work, we propose to incorporate the use of Long Short-Term Memory (LSTM) and attention-based networks in order to improve the detection of anomalous trajectories.
在过去的十年中，由于 GPS 系统的普及，大量的车辆位置传感器数据被生成，以便对其进行跟踪。这是因为这些传感器通常包括位置、速度、车辆的角速度等多个变量，而且它们通常也会在非常短的时间间隔内被记录下来。另一方面，由于建筑物、桥梁或传感器故障等因素，路线通常是生成的，并不对应于现实，并且由于数据量很大，人类专家的视觉分析无法检测到真正异常的路线。这些异常的存在可能会导致检测到有故障的传感器，这可能会允许更换传感器以可靠地跟踪车辆。然而，考虑到可用传感器的可靠性，这样的异常情况非常少，这使得难以应用监督学习技术。在这项工作中，我们提出使用基于堆叠自动编码器的无监督深度神经网络模型来检测圣地亚哥智利的车辆中的异常路线。结果表明，所提出的模型能够在专家用户提供的验证的情况下，有效地检测真实数据中的异常路径，平均性能达到 82.1%。作为未来的工作，我们建议在检测异常轨迹时，结合使用长短期记忆（LSTM）和基于注意力的网络。
