Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning
This work addresses the need for better clustering of driving data to improve autonomous vehicle decision-making, but it is incremental as it builds on existing methods.
The paper tackled the problem of grouping naturalistic driving encounters to aid self-driving car decision-making by developing an unsupervised classifier combining an auto-encoder with k-means clustering (AE-kMC), which outperformed original k-means methods on a dataset of 10,000 encounters.
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.