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Title: A Fully Unsupervised Framework for Scoring Driving Style
Authors: Özgül, Ozan Fırat
Çakır, Mehmet Ulaş
Tan, Mehmet
Amasyalı, Mehmet Fatih
Hayvacı, Harun Taha
Keywords: Driving style scoring
unsupervised learning
machine learning
Issue Date: 2018
Publisher: IEEE
Source: Ozgul, O. F., Cakir, M. U., Tan, M., Amasyali, M. F., and Hayvaci, H. T. (2018, September). A Fully Unsupervised Framework for Scoring Driving Style. In 2018 International Conference on Intelligent Systems (IS) (pp. 228-234). IEEE.
Abstract: Rating driving performance is a challenging topic. It attracts professionals from a variety of domains such as automotive industry and insurance companies. In this work, we propose a fully unsupervised driver scoring framework using a minimalistic dataset which is composed of Global Positioning System (GPS) and Controller Area Network (CAN Bus) data. Based on the natural expectation that good driving patterns should depend on the road type and traffic flow intensity, our framework attempts to assign a probabilistic score in proportion to the occurrence probability of a certain driving style given the road geometry and traffic conditions. Quantization of these random variables through clustering methods and learning of a co occurrence matrix between clusters of distinct variables provide a computationally relaxed way of otherwise intractable joint probability estimations. Utilizing this approach, we report explicitly different scoring results for aggressive and nonaggressive labelled driving experiences. Besides, we provide a rigorous analysis of clustering schemes applied on trajectory, traffic flow and driving style data.
ISBN: 978-1-5386-7097-2
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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