Analysis of Car Drivers’ Behaviour and Driving Style
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU144378" target="_blank" >RIV/00216305:26220/22:PU144378 - isvavai.cz</a>
Výsledek na webu
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DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Analysis of Car Drivers’ Behaviour and Driving Style
Popis výsledku v původním jazyce
Driving security remains one of the important issues. Nowadays, various assistance systems are implemented, such as the systems for analysis of control of a car by its driver. To understand the performance of the driver’s control, a program was created to obtain valuable data and relevant characteristics. To obtain the data, we used an internally designed, laboratory-made vehicle driving simulator developed by D. Michalík [2]. Driver data were obtained using a proprietary vehicle driving simulator, and these were evaluated in the MATLAB environment via integral criteria and other calculated parameters, such as reaction delay. Features thus obtained were used as a training set for the machine learning, using LDA and QDA methods (linear and quadratic discriminant analysis). These methods reveal information concerning the importance of features for the task of driver’s identity prediction based solely on the driving actions.
Název v anglickém jazyce
Analysis of Car Drivers’ Behaviour and Driving Style
Popis výsledku anglicky
Driving security remains one of the important issues. Nowadays, various assistance systems are implemented, such as the systems for analysis of control of a car by its driver. To understand the performance of the driver’s control, a program was created to obtain valuable data and relevant characteristics. To obtain the data, we used an internally designed, laboratory-made vehicle driving simulator developed by D. Michalík [2]. Driver data were obtained using a proprietary vehicle driving simulator, and these were evaluated in the MATLAB environment via integral criteria and other calculated parameters, such as reaction delay. Features thus obtained were used as a training set for the machine learning, using LDA and QDA methods (linear and quadratic discriminant analysis). These methods reveal information concerning the importance of features for the task of driver’s identity prediction based solely on the driving actions.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
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OECD FORD obor
20205 - Automation and control systems
Návaznosti výsledku
Projekt
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Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů