Meiring Gys Albertus Marthinus, Myburgh Hermanus Carel
Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Private Bag X20, Hatfield, Pretoria 0028, South Africa.
Sensors (Basel). 2015 Dec 4;15(12):30653-82. doi: 10.3390/s151229822.
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.
本文对各种驾驶风格分析解决方案进行了研究。进行了深入调查，以确定当前驾驶员行为和驾驶风格分析系统中使用的相关机器学习和人工智能算法。因此，本综述可作为一个信息宝库，将为专家和学生提供有关驾驶员风格分析系统的当前技术水平、这些系统的应用以及应用于这些应用的基础人工智能算法的信息。调查的目的是评估利用其他驾驶员行为研究中确定的方法进行独特驾驶员识别的可能性。研究发现，如果能够降低模型复杂性，模糊逻辑推理系统、隐马尔可夫模型和支持向量机具有解决独特驾驶员识别算法的潜力。
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