FUZZY MULTI-CRITERIA DECISION-MAKING APPROAHCES FOR OPTIMAL SELECTION OF EMBEDDED SYSTEMS FOR AUTONOMOUS NAVIGATED LIGHT VEHICLES

Selecting the best type of embedded system for an autonomous navigated light vehicle is complicated. The complexity arises from the need to consider criteria such as navigation accuracy, processing speed, power consumption, compatibility, and reliability when building an autonomous navigated light vehicle. This study evaluates three types of embedded systems: Arduino, Raspberry Pi, and NVIDIA Jetson. Therefore, this study aims to evaluate and select the best embedded system for an autonomous navigated light vehicle. The Fuzzy Analytic Hierarchy Process (FAHP), the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS), and the Fuzzy Simple Additive Weighting (FSAW) methods were used to test and choose from between the three types of embedded systems. Each method has its advantages. The FAHP has the advantage of determining the weight of the criteria hierarchically. The F-TOPSIS and FSAW methods were used to sort and evaluate alternatives based on the set criteria. The study’s results showed that of the three methods used, Raspberry Pi is the best alternative, followed by NVIDIA Jetson and Arduino. The findings showed that Raspberry Pi excelled in navigation accuracy and processing speed, NVIDIA Jetson excelled in reliability, and Arduino excelled in power consumption and compatibility. Fuzzy MCDM is an appropriate method to use to select embedded systems for autonomous navigated light vehicles.