PRACA ORYGINALNA
Machine Vision in Autonomous Vehicles: Designing and Testing the Decision Making Algorithm Based on Entity Attribute Value Model
1
Service of Transport Systems, Kazan Federal University, Russia
2
Computer Science, University of Silesia in Katowice, Polska
Data nadesłania: 29-09-2021
Data ostatniej rewizji: 12-11-2021
Data akceptacji: 24-11-2021
Data publikacji: 30-12-2021
Autor do korespondencji
Ksenia Shubenkova
Service of Transport Systems, Kazan Federal University, 423800, Naberezhnye Chelny, Russia
Service of Transport Systems, Kazan Federal University, 423800, Naberezhnye Chelny, Russia
The Archives of Automotive Engineering – Archiwum Motoryzacji 2021;94(4):27-37
SŁOWA KLUCZOWE
DZIEDZINY
STRESZCZENIE
If we speak about the Smart City’s transport system, autonomous vehicles idea is the first thing that comes to mind. Today, it is strongly believed that the autonomous vehicles’ introduction into the traffic will increase the road safety. However, driverless cars are not the solution by itself. The road safety and, accordingly, sustainability will strongly depend on decision making algorithms inbuilt into the control module. Therefore, the goal of our research is to design and test the data mining algorithm based on Entity–Attribute–Value (EAV) model for decision making in the Intelligent System in the fully- or semi-autonomous vehicles. In this article, we describe the methodology to create 3 main modules of the designed Intelligent System: (1) an Object detection module; (2) a Data analysis module; (3) a Knowledge database built on decision rules generated with the help of our data mining algorithm. To build the Decision Table on the base of the real data, we have tested our algorithm on a simple collection of photos from a Polish two-lane road. Generated rules provide comparable classification results to the dynamic programming approach for optimization of decision rules relative to length or support. However, our decision making algorithm thanks to excluding the mistakes made on the object detection stage, works faster than existing ones with the same level of correctness.
REFERENCJE (30)
1.
Allam Z., Dhunny Z.A.: On big data, artificial intelligence and smart cities. Cities. 2019, 89, 80–91, DOI: 10.1016/j.cities.2019.01.032.
2.
Ataya A., Kim W., Elsharkawy A., Kim S.: How to Interact with a Fully Autonomous Vehicle: Naturalistic Ways for Drivers to Intervene in the Vehicle System While Performing Non-Driving Related Tasks. Sensors. 2021, 21, 2206, DOI: 10.3390/s21062206.
3.
Bibri S.E.: The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability. Sustainable Cities and Society. 2018, 38, 230–253, DOI: 10.1016/j.scs.2017.12.034.
4.
Braun T., Fung B.C.H., Iqbal F., Shah B.: Security and privacy challenges in smart cities. Sustainable Cities and Society. 2018, 39, 499–507, DOI: 10.1016/j.scs.2018.02.039.
5.
Carranza-García M., Torres-Mateo J., Lara-Benítez P., García-Gutiérrez J.: On the performance of one-stage and two-stage object detectors in autonomous vehicles using camera data. Remote Sensing. 2021, 13(1), 1–23, DOI: 10.3390/rs13010089.
6.
City Safety by Volvo Cars – outstanding crash prevention that is standard in the all-new XC90: https://www.media.volvocars.co... (accessed on 28.09.2021).
8.
Coskun S.: Autonomous overtaking in highways: A receding horizon trajectory generator with embedded safety feature. Engineering Science and Technology, an International Journal. 2021, 24(5), 1049–1058, DOI: 10.1016/j.jestch.2021.02.005.
10.
Delsing J.: Smart City Solution Engineering. Smart Cities. 2021, 4(2), 643–661, DOI: 10.3390/smartcities4020033.
12.
Fan J., Huo T., Li X.: A review of one-stage detection algorithms in autonomous driving. 4th CAA International Conference on Vehicular Control and Intelligence. 2020, 9338663, 210–214, DOI: 10.1109/CVCI51460.2020.9338663.
13.
Global status report on road safety 2015: https://www.afro.who.int/publi... (accessed on 28.09.2021).
14.
Hammi M.T., Hammi B., Bellot P., Serhrouchni A.: Bubbles of Trust: A decentralized blockchain-based authentication system for IOT. Computers and Security. 2018, 78, 126–142, DOI: 10.1016/j.cose.2018.06.004.
15.
Kalašová A., Harantová V., Čulík K.: Public transport as a part of shared economy. The Archives of Automotive Engineering – Archiwum Motoryzacji. 2019, 85(3), 49–56, DOI: 10.14669/AM.VOL85.ART4.
16.
Kustra M., Brodowicz D.: Implementing smart city concept in the strategic urban operations - the case of Warsaw. 11th International Forum of Knowledge Assets Dynamics 2016. Dresden, Germany, 2016, DOI: 10.13140/RG.2.1.4675.9925.
18.
Mora L., Deakin M.: Untangling Smart Cities: From Utopian Dreams to Innovation Systems for a Technology-Enabled Urban Sustainability. Elsevier: Amsterdam, The Netherlands; Cambridge, MA, USA, 2019.
19.
Park J., Hong E., Le H.T.: Adopting autonomous vehicles: The moderating effects of demographic variables. Journal of Retailing and Consumer Services. 2021, 63, 102687, DOI: 10.1016/j.jretconser.2021.102687.
20.
Ramírez-Moreno M.A., Keshtkar S., Padilla-Reyes D.A., Ramos-López E., García-Martínez M., Hernández-Luna M.C. et al.: Sensors for sustainable smart cities: A review. Applied Sciences-Basel. 2021, 11(17), 8198, DOI: 10.3390/app11178198.
22.
Richardson N., Doubek F., Kuhn K., Stumpf A.: Assessing Truck Drivers’ and Fleet Managers’ Opinions Towards Highly Automated Driving. Advances in Intelligent Systems and Computing. 2017, 484, 473–484, DOI: 10.1007/978-3-319-41682-3_40.
24.
Saini D., Thakur N., Jain R., Nagrath P., Jude H., Sharma N.: Object Detection in Surveillance Using Deep Learning Methods: A Comparative Analysis. Lecture Notes in Networks and Systems. 2021, 173 LNNS, 677–689, DOI: 10.1007/978-981-33-4305-4_49.
25.
Sharifi A.: A typology of smart city assessment tools and indicator sets. Sustainable Cities and Society. 2020, 53, 101936, DOI: 10.1016/j.scs.2019.101936.
26.
Transport in the European Union. Current Trends and Issues: https://ec.europa.eu/transport... (accessed on 28.09.2021).
27.
Venkadeshan R., Jegatha M.: Blockchain-Based Fog Computing Model (BFCM) for IoT Smart Cities. EAI/Springer Innovations in Communication and Computing. 2022, 978-3-030-76215-5, 77–92, DOI: 10.1007/978-3-030-76216-2_5.
28.
Zabinski K., Zielosko B.: Decision Rules Construction: Algorithm Based on EAV Model. Entropy. 2021, 23(1), 1–18, DOI: 10.3390/e23010014.
29.
Zhang S., Liu F., Li Z.: An effective driver fatigue monitoring system. International Conference on Machine Vision and Human-Machine Interface, MVHI 2010. Kaifeng, China, 2010, 279–282, DOI: 10.1109/MVHI.2010.153.
30.
Zywiołek J., Schiavone F.: Perception of the Quality of Smart City Solutions as a Sense of Residents’ Safety. Energies. 2021, 14(17), 5511, DOI: 10.3390/en14175511.
CYTOWANIA (1):
1.
Approach Draft to Evaluate the Transport System State—A Case Study Regarding the Estimation Ratio Model of Transport Supply and Demand
Ladislav Bartuska, Ondrej Stopka, Vladimir Luptak, Jaroslav Masek
Applied Sciences
Ladislav Bartuska, Ondrej Stopka, Vladimir Luptak, Jaroslav Masek
Applied Sciences
Korzystamy z plików cookie (oraz innych podobnych technologii) i przetwarzamy unikalne identyfikatory oraz dane przeglądarki – w celu umożliwienia funkcjonowania naszej strony internetowej. Więcej informacji o tym jak przetwarzamy Twoje dane osobowe znajdziesz w
