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IoT on-board system for driving style assessment
BetterSolutions S.A., POL.
Politechnika Gdanska, POL.
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
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2018 (English)In: Sensors, E-ISSN 1424-8220, Vol. 18, no 4, article id 1233Article in journal (Refereed) Published
Abstract [en]

The assessment of skills is essential and desirable in areas such as medicine, security, and other professions where mental, physical, and manual skills are crucial. However, often such assessments are performed by people called “experts” who may be subjective and are able to consider a limited number of factors and indicators. This article addresses the problem of the objective assessment of driving style independent of circumstances. The proposed objective assessment of driving style is based on eight indicators, which are associated with the vehicle’s speed, acceleration, jerk, engine rotational speed and driving time. These indicators are used to estimate three driving style criteria: safety, economy, and comfort. The presented solution is based on the embedded system designed according to the Internet of Things concept. The useful data are acquired from the car diagnostic port—OBD-II—and from an additional accelerometer sensor and GPS module. The proposed driving skills assessment method has been implemented and experimentally validated on a group of drivers. The obtained results prove the system’s ability to quantitatively distinguish different driving styles. The system was verified on long-route tests for analysis and could then improve the driver’s behavior behind the wheel. Moreover, the spider diagram approach that was used established a convenient visualization platform for multidimensional comparison of the result and comprehensive assessment in an intelligible manner. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

Place, publisher, year, edition, pages
MDPI AG , 2018. Vol. 18, no 4, article id 1233
Keywords [en]
Driver’s behavior, Driving style, Eco driving, Embedded system, Internet of things, Real-time vehicle tracking, Skills assessment, Behavioral research, Diagnosis, Embedded systems, Accelerometer sensor, Comprehensive assessment, Driving styles, Eco-driving, Objective assessment, Real time, Visualization platforms, Automobile safety devices
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-16152DOI: 10.3390/s18041233ISI: 000435574800305Scopus ID: 2-s2.0-85045628840OAI: oai:DiVA.org:bth-16152DiVA, id: diva2:1203686
Note

open access

Available from: 2018-05-04 Created: 2018-05-04 Last updated: 2022-04-05Bibliographically approved
In thesis
1. Detection and Classification Multi-sensor Systems: Implementation of IoT and Systematic Design Approaches
Open this publication in new window or tab >>Detection and Classification Multi-sensor Systems: Implementation of IoT and Systematic Design Approaches
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The detection and classification of features or properties, which characterize people, things or even events can be done in reliable way due to the development of new technologies such as Internet of Things (IoT), and also due to advances in Artificial Intelligence (AI) and machine learning algorithms. Interconnection of users with sensors and actuators have become everyday reality and IoT, an advanced notation of a Multi-sensor System, has become an integral part of systems for assessment of people’s habits and skills as well as the evaluation of quality of things or events' performances. The assessment approach presented in this thesis could be understood as an evaluation of multidimensional fuzzy quantities, which lack standards or references.

The main objective of this thesis is systematical design of multi-sensor systems for industrial and behavioral applications. The systematization is based on User Oriented Design (UOD), the methodology where stakeholders and future users are actively involved in all steps of the development process. An impact of the application environment on design principles is quantitatively and qualitatively analyzed. It shows different design approaches, which can be used for developing systems monitoring human activities or industrial processes.

The features identification approach applied in this thesis involves the extraction of the necessary data, which could be used for behavior classification or skills assessment. The data used for these purposes are vision or radio-based localization and orientation combined with measurement data of speed, acceleration, execution time or the remaining energy level.

Background removal, colour segmentation, Canny filtering and Hough Transform are the algorithms used in vision applications presented in the thesis. In cases of radio-based solutions the methods of angle of arrival, time difference of arrival and pedestrian dead reckoning were utilized. The applied classification and assessment methods were based on AI with algorithms such as decision trees, support vector machines and k-nearest neighborhood.

The thesis proposes a graphical methodology for visualization and assessment of multidimensional fuzzy quantities, which facilitate assessor's conceptualization of strengths and weaknesses in a person's skills or abilities. Moreover, the proposed method can be concluded as a single number or score useful for the evaluation of skills improvement during of training.

The thesis is divided into two parts. The first part, Prolegomena, shows the technical background, an overview of applied theories along with research and design methods related to systems for identification and classification of people’s habits and skills as well as assessing the quality of things or performances. Moreover, this part shows relationships among the papers constituting the second part titled Papers, which includes six reformatted papers published in peer reviewed journals. All the papers concern the design of IoT systems for industrial and behavioral applications.

 

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2020. p. 236
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2020:10
Keywords
Assessment, Behavior Recognition, Classification, Design Methodology, Detection, Indoor Localization, Internet of Things, Multi-Sensor System, Skills Assessment, Outdoor Localization, Wireless Sensor Network
National Category
Signal Processing
Research subject
Applied Signal Processing
Identifiers
urn:nbn:se:bth-20566 (URN)978-91-7295-410-6 (ISBN)
Public defence
2020-12-04, J1630, Campus Gräsvik, Karlskrona, 13:00 (English)
Opponent
Supervisors
Available from: 2020-10-21 Created: 2020-10-20 Last updated: 2020-12-14Bibliographically approved

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Kulesza, Wlodek

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