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Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

Yi, Dewei and Su, Jinya and Hu, Liang and Liu, Cunjia and Mohammed, Quddus and Mehrdad, Dianati and Chen, Wen-Hua (2020) 'Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues.' IEEE Transactions on Intelligent Vehicles, 5 (3). pp. 397-413. ISSN 2379-8858

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In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With considering personal driving preferences and characteristics, these systems become more acceptable and trustworthy. This paper presents a survey of recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, gains of personalization, application prospects, and future focal points. Several existing driving datasets are summarized and open issues of personalized driving assistance are also suggested to facilitate future research. By creating an organized categorization of the field, this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the use of these techniques by researchers within the driving automation community.

Item Type: Article
Uncontrolled Keywords: driver assistance; intelligent vehicle; personalized system
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 19 Nov 2019 17:46
Last Modified: 23 Sep 2022 19:36

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