论文标题
IMU信号的数据增强和通过半监督的驾驶行为分类
Data Augmentation of IMU Signals and Evaluation via a Semi-Supervised Classification of Driving Behavior
论文作者
论文摘要
在过去的几年中，对从数据中分类驱动程序行为的兴趣激增。这种兴趣特别与汽车保险公司特别相关，这些汽车保险公司由于隐私限制，通常只能从惯性测量单元（IMU）或类似的数据中访问数据。在本文中，我们提出了一种半监督的学习解决方案，以根据驾驶员是根据此类IMU数据而积极或正常驾驶的行程进行分类的。由于标记的IMU数据的量有限且成本高昂，因此我们利用经常性的条件生成对抗网络（RCGAN）生成更标记的数据。我们的结果表明，通过利用RCGAN生成的标记数据，与没有生成的数据分类的情况下，驱动程序的分类在79％的情况下得到了改进。
Over the past years, interest in classifying drivers' behavior from data has surged. Such interest is particularly relevant for car insurance companies who, due to privacy constraints, often only have access to data from Inertial Measurement Units (IMU) or similar. In this paper, we present a semi-supervised learning solution to classify portions of trips according to whether drivers are driving aggressively or normally based on such IMU data. Since the amount of labeled IMU data is limited and costly to generate, we utilize Recurrent Conditional Generative Adversarial Networks (RCGAN) to generate more labeled data. Our results show that, by utilizing RCGAN-generated labeled data, the classification of the drivers is improved in 79% of the cases, compared to when the drivers are classified with no generated data.
