What can we learn from telematics car driving data: A survey
这篇综述探讨了远程信息处理汽车驾驶数据在精算科学中的应用，包括数据清洗的困难、透明度问题及隐私担忧，并介绍了利用卷积神经网络区分驾驶员以及通过热力图和时间序列改进索赔频率预测的方法。
We give a survey on the field of telematics car driving data research in actuarial science. We describe and discuss telematics car driving data, we illustrate the difficulties of telematics data cleaning, and we highlight the transparency issue of telematics car driving data resulting in associated privacy concerns. Transparency of telematics data is demonstrated by aiming at correctly allocating different car driving trips to the right drivers. This is achieved rather successfully by a convolutional neural network that manages to discriminate different car drivers by their driving styles. In a last step, we describe two approaches of using telematics data for improving claims frequency prediction, one is based on telematics heatmaps and the other one on time series of individual trips, respectively.