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 journalArticlepeer-review

29 Citations (Scopus)

Abstract

In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on 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, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community.

Original languageEnglish
Article number8936867
Pages (from-to)397-413
Number of pages17
JournalIEEE Transactions on Intelligent Vehicles
Volume5
Issue number3
Early online date19 Dec 2019
DOIs
Publication statusPublished - 30 Sept 2020

Bibliographical note

This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) Autonomous and Intelligent Systems programme under the grant number EP/J011525/1 with BAE Systems as the leading industrial partner.

Keywords

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

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