<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the present work, a system using data from two sensors located next to the driver and to the mass centre of the bus is proposed. Three degrees of discomfort have been used – comfortable, moderately uncomfortable and very uncomfortable. These levels are set out in the questionnaire. A survey was conducted. Respondents were selected between the ages of 14 and 65 and were divided into three age groups – adults, middle-aged and young. Accelerometer systems with MPU-6500 (TDK InvenSense Corp.) sensors are used. A correlation method (CORR) and sequentially improving estimation methods are used for feature selection, which significantly reduce the number of combinations of features obtained. Selected sensor data is entered into feature vectors. These vectors are reduced by principal component analysis. Predictive models have been created that take into account the age of passengers. The use of data from two sensors and separation of the passengers according their age, leads to an increase in the accuracy of predicting passengers discomfort level (DL) of up to 98%. These results can be used to evaluate and guide the vehicle driver in order to improve his driving style. In addition, the simplified interface does not distract the driver from the road conditions. The results obtained can lead to an improvement in the parameters of the transport process, which covers the interest of the carrier related to the efficient use of vehicles, and hence the reduction of fuel consumption and harmful emissions. However, it should be recommended that, when developing systems to ensure comfort of travel, adjustments should be made to suit the age group of passengers carried on public transport buses.</p>
</abstract>

In the present work, a system using data from two sensors located next to the driver and to the mass centre of the bus is proposed. Three degrees of discomfort have been used – comfortable, moderately uncomfortable and very uncomfortable. These levels are set out in the questionnaire. A survey was conducted. Respondents were selected between the ages of 14 and 65 and were divided into three age groups – adults, middle-aged and young. Accelerometer systems with MPU-6500 (TDK InvenSense Corp.) sensors are used. A correlation method (CORR) and sequentially improving estimation methods are used for feature selection, which significantly reduce the number of combinations of features obtained. Selected sensor data is entered into feature vectors. These vectors are reduced by principal component analysis. Predictive models have been created that take into account the age of passengers. The use of data from two sensors and separation of the passengers according their age, leads to an increase in the accuracy of predicting passengers discomfort level (DL) of up to 98%. These results can be used to evaluate and guide the vehicle driver in order to improve his driving style. In addition, the simplified interface does not distract the driver from the road conditions. The results obtained can lead to an improvement in the parameters of the transport process, which covers the interest of the carrier related to the efficient use of vehicles, and hence the reduction of fuel consumption and harmful emissions. However, it should be recommended that, when developing systems to ensure comfort of travel, adjustments should be made to suit the age group of passengers carried on public transport buses.

Driving Comfort Assistance System Considering Two Sensors Data

University of Oradea, Faculty of Energy Engineering and Industrial Management

Department of Textiles,  Leather and Industrial Management

Trakia university, Faculty of Technics and technologies

Acta Mechanica et Automatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Driving Comfort Assistance System Considering Two Sensors Data"}

In the present work, a system using data from two sensors located next to the driver and to the mass centre of the bus is proposed. Three degrees of discomfort...

Driving Comfort Assistance System Considering Two Sensors Data

Publicado en línea: 27 sept 2021

Páginas: 164 - 168

Recibido: 22 mar 2020

Aceptado: 12 jul 2021

DOI: https://doi.org/10.2478/ama-2021-0021

Palabras clave
stated preference survey, predictive model, customer satisfaction, passenger comfort, passenger service, driver assistance system

© 2021 Stanimir Karapetkov et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Driving Comfort Assistance System Considering Two Sensors Data

Publicado en línea: 27 sept 2021

Páginas: 164 - 168

Recibido: 22 mar 2020

Aceptado: 12 jul 2021

DOI: https://doi.org/10.2478/ama-2021-0021

Palabras clavestated preference survey, predictive model, customer satisfaction, passenger comfort, passenger service, driver assistance system

© 2021 Stanimir Karapetkov et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Palabras clave
stated preference survey, predictive model, customer satisfaction, passenger comfort, passenger service, driver assistance system