  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.
