@inproceedings{b91e63970929476db3f30eb99f16d3cd,
title = "Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning",
abstract = "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.",
keywords = "Driving encounter classification, auto-encoder, unsupervised learning",
author = "Sisi Li and Wenshuo Wang and Zhaobin Mo and DIng Zhao",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Intelligent Vehicles Symposium, IV 2018 ; Conference date: 26-09-2018 Through 30-09-2018",
year = "2018",
month = oct,
day = "18",
doi = "10.1109/IVS.2018.8500529",
language = "English",
series = "IEEE Intelligent Vehicles Symposium, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1354--1359",
booktitle = "2018 IEEE Intelligent Vehicles Symposium, IV 2018",
address = "United States",
}