Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning

Understand

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.