TY - JOUR
T1 - Implicit Personalization in Driving Assistance
T2 - State-of-the-Art and Open Issues
AU - Yi, Dewei
AU - Su, Jinya
AU - Hu, Liang
AU - Liu, Cunjia
AU - Quddus, Mohammed A.
AU - Dianati, Mehrdad
AU - Chen, Wen-Hua
N1 - 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.
PY - 2020/9/30
Y1 - 2020/9/30
N2 - 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.
AB - 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.
KW - Intelligent vehicles
KW - driver behavior analysis
KW - personalization
KW - Advanced Driver Assistance Systems
UR - http://wrap.warwick.ac.uk/131258/
UR - http://www.scopus.com/inward/record.url?scp=85083767413&partnerID=8YFLogxK
U2 - 10.1109/tiv.2019.2960935
DO - 10.1109/tiv.2019.2960935
M3 - Article
VL - 5
SP - 397
EP - 413
JO - IEEE Transactions on Intelligent Vehicles
JF - IEEE Transactions on Intelligent Vehicles
SN - 2379-8904
IS - 3
M1 - 8936867
ER -