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Experience-Based Cognition for Driving Behavioral Fingerprint Extraction

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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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Wersja publikacji
Accepted albo Published Version
Licencja
Copyright (2020 Taylor & Francis Group, LLC)

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
CYBERNETICS AND SYSTEMS nr 51, strony 103 - 114,
ISSN: 0196-9722
Język:
angielski
Rok wydania:
2020
Opis bibliograficzny:
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:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1080/01969722.2019.1705547
Weryfikacja:
Politechnika Gdańska

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