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  •   University of Economics & Technology Repository
  • Akademik Arşiv / Institutional Repository
  • Mühendislik Fakültesi / Faculty of Engineering
  • Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
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A Fully Unsupervised Framework for Scoring Driving Style

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Date
2018
Author
Ozgul, Ozan Firat
Cakir, Mehmet Ulas
Tan, Mehmet
Amasyali, Mehmet Fatih
Hayvacı, Harun Taha
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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.
URI
https://ieeexplore.ieee.org/document/8710574
http://hdl.handle.net/20.500.11851/2840
Collections
  • Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
  • Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering

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