A Novel Driver Performance Model Based on Machine Learning

被引：19

作者：

Aksjonov, Andrei ^{[1
]}

Nedoma, Pavel ^{[1
]}

Vodovozov, Valery ^{[2
]}

Petlenkov, Eduard ^{[2
]}

Herrmann, Martin ^{[3
]}

机构：

[1] SKODA Auto As, Mlada Boleslav, Czech Republic

[2] Tallinn Univ Technol, Tallinn, Estonia

[3] IPG Automot GmbH, Karlsruhe, Germany

来源：

IFAC PAPERSONLINE | 2018年 / 51卷 / 09期

基金：

欧盟地平线“2020”;

关键词：

Neural networks; Neural fuzzy modelling and control; Machine learning for environmental applications; Vehicle dynamic systems; Human factors in vehicular system; Learning and adaptation in autonomous vehicles; Safety; DISTRACTION;

D O I：

10.1016/j.ifacol.2018.07.044

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Models of road vehicle driver behaviour are widely used in several disciplines, like driver distraction and autonomous driving. In this paper, a novel driver performance model, which is unique for every driver, is introduced. The driver is modelled with machine learning algorithms, namely artificial neural network and adaptive neuro-fuzzy inference system. Every model is trained and validated with the data collected during the real-time driver-in-the-loop experiment on a vehicle simulator for each driver separately. In total, 18 participants contributed to the experiment. Although the prediction accuracy of the models depends on the algorithm specifications, the artificial neural network was slightly more accurate in driver performance prediction comparing to the adaptive neuro-fuzzy inference system. The driver models may be used in detection of driver distraction induced by in-vehicle information system. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：267 / 272

页数：6

共 21 条

[1]

Aksjonov A., 2017, P 26 INT C INF COMM, P23

[2]

Alpaydin E., 2004, Introduction to Machine Learning

[3]

[Anonymous], 2016, DEEP LEARNING

[4] THE EFFECTS OF MOBILE TELEPHONING ON DRIVING PERFORMANCE [J].