# Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning

arXiv: https://arxiv.org/abs/1802.10214 · HTML: https://mlsanity.com/paper/1802.10214/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [AI perspectives in Smart Cities and Communities to enable road vehicle automation and smart traffic control](https://mlsanity.com/paper/2104.03150/) (2021)

## Abstract (arXiv)

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.