A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms

被引:179
作者
Meiring, Gys Albertus Marthinus [2 ]
Myburgh, Hermanus Carel [1 ,2 ]
机构
[1] Univ Pretoria, Dept Elect Elect & Comp Engn, ZA-0028 Pretoria, South Africa
[2] Univ Pretoria, Dept Elect Elect & Comp Engn, ZA-0028 Pretoria, South Africa
关键词
driving style; driver behaviour; artificial intelligence; machine learning; driver safety; road accident; driver identification; BEHAVIOR; VEHICLE; VIOLATIONS; DRIVERS; EVENTS; ERRORS; SAFE; ROAD;
D O I
10.3390/s151229822
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.
引用
收藏
页码:30653 / 30682
页数:30
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