Abstract
ABSTRACT With the rapid progress of information technologies, cars have been made increasingly intelligent. This allows cars to act as cognitive agents, i.e., to acquire knowledge and understanding of the driving habits and behavioral characteristics of drivers (i.e., driving behavioral fingerprint) through experience. Such knowledge can be then reused to facilitate the interaction between a car and its driver, and to develop better and safer car controls. In this paper, we propose a novel approach to extract the driver’s driving behavioral fingerprints based on our conceptual framework Experience-Oriented Intelligent Things (EOIT). EOIT is a learning system that has the potential to enable Internet of Cognitive Things (IoCT) where knowledge can be extracted from experience, stored, evolved, shared, and reused aiming for cognition and thus intelligent functionality of things. By catching driving data, this approach helps cars to collect the driver’s pedal and steering operations and store them as experience; eventually, it uses obtained experience for the driver’s driving behavioral fingerprint extraction. The initial experimental implementation is presented in the paper to demonstrate our idea, and the test results show that it outperforms the Deep Learning approaches (i.e., deep fully connected neural networks and recurrent neural networks/Long Short-Term Memory networks).
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- Accepted or Published Version
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- Copyright (2020 Taylor & Francis Group, LLC)
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
CYBERNETICS AND SYSTEMS
no. 51,
pages 103 - 114,
ISSN: 0196-9722 - Language:
- English
- Publication year:
- 2020
- Bibliographic description:
- Zhang H., Li F., Wang J., Zhou Y., Sanin C., Szczerbicki E.: Experience-Based Cognition for Driving Behavioral Fingerprint Extraction// CYBERNETICS AND SYSTEMS -Vol. 51,iss. 2 (2020), s.103-114
- DOI:
- Digital Object Identifier (open in new tab) 10.1080/01969722.2019.1705547
- Verified by:
- Gdańsk University of Technology
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