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PRACA ORYGINALNA
Machine Vision in Autonomous Vehicles: Designing and Testing the Decision Making Algorithm Based on Entity Attribute Value Model
 
Więcej
Ukryj
1
Service of Transport Systems, Kazan Federal University, Russia
2
Computer Science, University of Silesia in Katowice, Polska
AUTOR DO KORESPONDENCJI
Ksenia Shubenkova   

Service of Transport Systems, Kazan Federal University, 423800, Naberezhnye Chelny, Russia
Data nadesłania: 29-09-2021
Data ostatniej rewizji: 12-11-2021
Data akceptacji: 24-11-2021
Data publikacji: 30-12-2021
 
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.
 
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eISSN:2084-476X