RESEARCH PAPER
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
Submission date: 2021-09-29
Final revision date: 2021-11-12
Acceptance date: 2021-11-24
Publication date: 2021-12-30
Corresponding author
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
KEYWORDS
TOPICS
ABSTRACT
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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