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Driving comfort assistance system considering two sensors data

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Strony
164--168
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
  • Technical University of Sofia, Faculty and College of Sliven, Sliven 59 Burgasko Shose Blvd 59, Bulgaria
  • Technical University of Sofia, Faculty and College of Sliven, Sliven 59 Burgasko Shose Blvd 59, Bulgaria
  • University of Oradea, Faculty of Energy Engineering and Industrial Management, Department of Textiles, Leather and Industrial Management, B.St.Delavrancea Str., no. 4, 410058, Oradea, Romania
  • Trakia university, Faculty of Technics and technologies, 38 Graf Ignatiev str., 8602, Yambol, Bulgaria
Bibliografia
  • 1. HaoLiang G., XiHui M., XiaoYong Y., Kai L. (2018), Research on the influence of virtual modeling and testing-based rubber track sys-tem on vibration performance of engineering vehicles, Engineering Review, Vol. 38, No. 3, 288-295.
  • 2. Ikeda K., Endo A., Minowa R., Narita T., Kato H. (2018), Ride comfort control system considering physiological and psychological characteristics: effect of masking on vertical vibration on passengers, Actuators, Vol. 7, 42, 1-13.
  • 3. International Organization for Standardization (1978), Guide for the evaluation of human exposure to whole-body vibration, ISO 2631; ISO: Geneva, Switzerland.
  • 4. Ivanov A., Zlatev Z. (2019), Development and research of infor-mation system elements for passengers drive comfort improvement, Cybernetics and information technologies, Vol. 19, No. 4, 101-115.
  • 5. Jurkiewicz A., Kowal J., Zając K. (2017), Sky-hook control and Kalman filtering in nonlinear model of tracked vehicle suspension system, Acta mechanica et automatica, Vol. 11, No. 3, 222-228.
  • 6. Kim D., Jeong M., Bae B., Ahn C. (2019), Design of a human evaluator model for the ride comfort of vehicle on a speed bump us-ing a neural artistic style extraction, Sensors, Vol. 19, 1-13.
  • 7. Kim J., Kim Y. (2014), Time-domain analysis of passenger comfort on cruise ships under motion responses in waves, Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineer-ing for the Maritime Environment, Vol. 228, No. 4, 331-347.
  • 8. Li S., Gao Y., Meng G., Wang G., Guan L. (2019), Accelerometer-based gyroscope drift compensation approach in a dual-axial stabili-zation platform, Electronics, Vol. 8, Art. 594, 1-12.
  • 9. Long L., Quynh L., Cuong B. (2018), Study on the influence of bus suspension parameters on ride comfort, Vibroengineering Procedia, Vol. 21, 77-82.
  • 10. Mladenov M., Penchev S., Deyanov M. (2015), Complex assess-ment of food products quality using analysis of visual images, spec-trophotometric and hyperspectral characteristics, International Jour-nal of Engineering and Innovative Technology (IJEIT), Vol. 4, No. 12, 23-32.
  • 11. Rekabdar B., Mousas C. (2018), Dilated convolutional neural net-work for predicting driver’s activity, 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, Hawaii, USA, No-vember 4-7, 3245-3250.
  • 12. Sharma S., Kumar A. (2018), Ride comfort of a higher speed rail vehicle using a magnetorheological suspension system, Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-Body Dynamics, Vol. 232, No. 1, 32-48.
  • 13. Sikora M. (2018), Modeling and operational analysis of an automo-tive shock absorber with a tuned mass damper, Acta mechanica et automatic, Vol. 12, No. 3, 243-251.
  • 14. Smith D., Andrews D., Wawrow P. (2006), Development and eval-uation of the Automotive Seating Discomfort Questionnaire (ASDQ), International journal of industrial ergonomics, Vol. 36, 141-149.
  • 15. Stoichkov R. (2013), Android smartphone application for driving style recognition, Thesis, Institute for Media Technology, Department of Electrical Engineering and Information Technology, Munih, Ger-many.
  • 16. Su W-H., He H-J., Sun D-W. (2017), Non-Destructive and rapid evaluation of staple foods quality by using spectroscopic techniques: A review, Critical reviews in food science and nutrition, Vol. 57, No. 5, 1039-1051.
  • 17. Tasev G., Krastev K. (2011), Exploration of mathematical model for optimization of frequency of diagnosis of the elements of machines, Proceedings of The 11th International Conference, Reliability and statistics in transportation and communication, Latvia, 115-119.
  • 18. Tsvetkova S. (2017), Improving the quality of passenger transport in the city of Sofia by implementing intelligent transport systems, Eco-nomic and Social Alternatives, No. 4, 29-42, (in Bulgarian).
  • 19. Vrcan Ž., Siminiati D., Lovrin, N. (2011), Design proposal for a hydrostatic city bus transmission, Engineering Review, Vol. 31, No. 2, 81-89.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6b315bc1-80b2-404c-a0f1-43669f19528d
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