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

Dewei Yi*, Jinya Su, Liang Hu, Cunjia Liu, Mohammed A. Quddus, Mehrdad Dianati, Wen-Hua Chen

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

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.
Original languageEnglish
Number of pages18
JournalIEEE Transactions on Intelligent Vehicles
Early online date19 Dec 2019
DOIs
Publication statusE-pub ahead of print - 19 Dec 2019

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Keywords

  • Intelligent vehicles
  • driver behavior analysis
  • personalization
  • Advanced Driver Assistance Systems

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