Learning knowledge from driving encounters could help self-driving cars make appropriate decisions when driving in complex settings with nearby vehicles engaged. This paper develops an unsupervised classifier to group naturalistic driving encounters into distinguishable clusters by combining an auto-encoder with k-means clustering (AE-kMC). The effectiveness of AE-kMC was validated using the data of 10,000 naturalistic driving encounters which were collected by the University of Michigan, Ann Arbor in the past five years. We compare our developed method with the $k$-means clustering methods and experimental results demonstrate that the AE-kMC method outperforms the original k-means clustering method.

翻译：从驾驶经历中学习知识有助于驾驶汽车在使用附近车辆的复杂环境下驾驶时作出适当决定。本文开发了一个不受监督的分类器,通过将自动编码器与K means集群(AE-kMC)相结合,将自然驾驶经历分组为可区分的集群。AE-kMC的效力是使用密歇根大学安阿博尔过去五年收集的10 000次自然驾驶经历的数据得到验证的。我们比较了我们开发的方法与美元手段集群方法和实验结果,表明AE-kMC方法优于原k means集群法。