It has become of key interest in the insurance industry to understand and extract information from telematics car driving data. Telematics car driving data of individual car drivers can be summarised in so-called speed–acceleration heatmaps. The aim of this study is to cluster such speed–acceleration heatmaps to different categories by analysing similarities and differences in these heatmaps. Making use of local smoothness properties, we propose to process these heatmaps as RGB images. Clustering can then be achieved by involving supervised information via a transfer learning approach using the pre-trained AlexNet to extract discriminative features. The K-means algorithm is then applied on these extracted discriminative features for clustering. The experiment results in an improvement of heatmap clustering compared to classical approaches.

中文翻译：

---

通过图像处理聚类驾驶风格

了解并从远程信息处理汽车驾驶数据中提取信息已成为保险行业的主要兴趣所在。单个汽车驾驶员的远程信息处理汽车驾驶数据可以总结为所谓的速度 - 加速度热图。本研究的目的是通过分析这些热图的相似性和差异，将这些速度-加速度热图聚类到不同的类别。利用局部平滑特性，我们建议将这些热图处理为 RGB 图像。然后可以通过使用预训练的 AlexNet 提取判别特征的迁移学习方法，通过涉及监督信息来实现聚类。这ķ然后对这些提取的判别特征应用-means算法进行聚类。与经典方法相比，该实验导致热图聚类的改进。