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Implicit personalization in driving assistance: State-of-the-art and open issues

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journal contribution
posted on 13.01.2020, 09:56 by Dewei Yi, Jinya Su, Liang Hu, Cunjia Liu, Mohammed Quddus, Mehrdad Dianati, Wen-Hua Chen

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

Funding

U.K. Engineering and Physical Sciences Research Council (EPSRC) Autonomous and Intelligent Systems programme under the grant number EP/J011525/1

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering
  • Architecture, Building and Civil Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IEEE Transactions on Intelligent Vehicles

Volume

5

Issue

3

Pages

397 - 413

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

18/11/2019

Publication date

2019-12-19

Copyright date

2020

ISSN

2379-8858

eISSN

2379-8904

Language

en

Depositor

Dr Cunjia Liu Deposit date: 10 January 2020

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