## 1 Introduction  
Many studies have been conducted to develop autonomous navigation systems that can facilitate safe and efficient mobility of vehicles [1; 2; 3; 4; 5]. Autonomous navigation systems combine new technologies, such as modern sensors, information communication, and intelligent control, to enable a vehicle to recognize its surrounding environment, analyze risks, and achieve active safety [1; 2; 3; 4; 5]. Furthermore, the development of autonomous navigation technology could enhance the safety of driving via improvements in the range of recognition and reaction time, reduction in road accidents, alleviation of traffic congestion, and promotion of the automotive convergence industry [1; 2; 3; 4; 5]. Behavior-based navigation algorithms are commonly used for developing autonomous navigation systems [6; 7; 8; 9]. They determine the motion of a vehicle by using programmed behavior rules corresponding to the real-time information of the surroundings obtained from the mounted sensors. Moreover, these algorithms improve navigation skills, such as ensuring coping ability for unexpected situations while heading to a target and avoiding obstacles. However, to achieve more advanced navigation, multiple behavior rules generating a variety of vehicular motions corresponding to surrounding information must be included, and the problems involving combination between behavior rules should be considered [6; 7; 8; 9]. Fuzzy-logic theory mimics human recognition ability, and it is widely employed in studies on autonomous navigation because it exhibits good performance in processing information gathered from the surrounding environment [6; 7; 8; 9]. Fuzzy logic includes three stages: fuzzification, rule evaluation, and defuzzification. In the fuzzification process,all sensor data input to the fuzzy system have their own weights assigned by input membership functions. In the rule-evaluation process, the behavior rules are activated for sensor data with nonzero weights. In the defuzzification process, all the outputs from the behavior rules are combined using the defuzzification function. Because all the weights of the input signals are considered, and the behavior rules are well combined to produce an output--such as the motion of a robot. Fuzzy logic demonstrates excellent performance in systems addressing multiple environmental uncertainties, such as robot navigation algorithms [6; 7; 8; 9]. Positioning is one of the most important techniques in autonomous navigation [1; 2; 3; 4; 5]. A variety of positioning methods based on sensor techniques are currently utilized in the autonomous navigation systems of vehicles. Further, studies are underway to minimize the measurement errors that occur for each sensor and enhance the accuracy of the positioning results by utilizing the advantages of each positioning sensor [1; 2; 3; 4; 5]. Commonly used positioning sensors present advantages as well as disadvantages. In the case of the global navigation satellite system (GNSS), the position of the receiver is calculated by using satellite networks through triangulation, utilizing the time at which the satellite signal arrives at the ground-surface receiver and the position information of other satellites. However, owing to various error factors, a positioning error of approximately 7 m occurs [10; 11; 12; 13; 14; 15]. In addition, differential GNSS uses the ground master station to address satellite clock, ion/ionospheric, and orbit errors; the positioning error is approximately 2 m in this case, and obtaining an accurate measurement is still difficult [1; 2]. Some studies have combined inertial navigation systems (INS), vision, radar, geomagnetic sensors, Wi-Fi, etc., to improve accuracy. However, Wi-Fi has a different positioning accuracy depending on the interval of installation and the number of surrounding signal sources (access points, APs). In INS-GPS, owing to the sensor bias and noise accumulation over time, the accuracy and reliability of positioning decrease [10; 11; 12; 13; 14; 15].