IMU信号的数据增强和通过驾驶行为的半监督分类的评估
Data Augmentation of IMU Signals and Evaluation via a Semi-Supervised Classification of Driving Behavior
摘要：
在过去的几年里，从数据中对驾驶员行为进行分类的兴趣激增。这种兴趣对于汽车保险公司来说尤其重要，由于隐私限制，汽车保险公司通常只能访问来自惯性测量单元（IMU）或类似设备的数据。在本文中，我们提出了一种半监督学习解决方案，根据驾驶员是否积极驾驶或基于此类IMU数据正常驾驶来对行程部分进行分类。由于标记IMU数据的数量有限且生成成本高，因此我们利用递归条件生成对抗网络（RCGAN）来生成更多的标记数据。我们的研究结果表明，通过利用RCGAN生成的标记数据，驾驶员的分类在79%的情况下得到了改善，与没有生成数据的驾驶员进行分类时相比。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
