The Stationary Gaze Entropy (SGE) is used as an indicator for estimating the level of complexity or randomness of visual search, and is used in fields such as flight simulators, surgical simulation, and pattern recognition [19,20]. Through this static analysis of gaze, it will be possible to compare the difference between the novice drivers and experienced drivers in visual search because the inexperienced novice drivers cannot recognize potential hazards on the road or do not know where to look at while driving unlike the experienced drivers. However, a static analysis on gaze is not enough to identify the mobility related characteristics, such as movement, direction and speed of a driver's gaze of their visual search around their surroundings. Under such circumstances, a dynamic analysis is additionally required to process the complexity and quantity of the information manifested in the sequential pattern of visual search as well as dispersion of gaze across the area of interest (AOI) using gaze transition entropy (GTE) based on gaze transition between the AOIs [21; 22]. It estimates uncertainty in predicting the next fixation location given the current position of the eyes. This is used in research to analyze the order of visual search and pattern recognition strategies in overall stimuli [23; 24]. In particular, studies of visual search in driving have shown that experienced drivers have higher entropy because they make more gaze changes to identify potential risks which in turn increases the entropy, whereas for novice drivers, showing the lower entropy due to gaze changes between near and far spaces with simple visual search patterns [25; 26]. Since most of results that mainly analyzed the visual search process for the direction, location, and distance at which drivers gaze, this study purposed to analyze which objects (e.g., Pedestrian, traffic light, cars etc.) the drivers stare at on the road. Even if novice and experienced drivers stare at the same road scene with the same visual field of view, there may be differences in the object and order to be searched, so the study should be conducted to analyze entropy acquired based on the objects that the drivers searched [27]. Actually, driving performance is evaluated using an actual vehicle. However, in the current research, a Virtual Reality (VR) driving simulator has been used to repeat the experiment process and to ensure safety for novice drivers from any potentially dangerous situations. Typically, a driving simulator using a virtual reality environment can evaluate drivers as if they are driving in real situations. Among these driving simulators, a Head Mounted Display (HMD)-based VR driving simulator provides higher concentration, immersion, and interest than the existing 3D (Full HD, Smart TV etc.) environment [28]. The driving simulation has other advantages, such as regulating the levels of difficulty posed by the chosen path and traffic jam, enabling an assessment of drivers' reaction to a life-threatening situation, which cannot be materialized on an actual road [29]. The current study aimed to explore whether the information obtained from the driver staring at traffic-related areas to prevent collisions while driving in a VR-based intersection scenario differs according to the driving experience, and the research questions are as follows. **RQ 1** Are there any differences in time, number, and distribution of areas at which the beginner and skilled drivers stare while driving. **RQ 2** Is there any difference in the entropy obtained by the movement and transition of gaze between the areas at which the novice and skilled drivers stare while driving. Finally, this study would like to be contribution to prior studies that have researched quantitative indicators of visual search as a method for evaluating driving performance and habits. Furthermore, to be used as a pilot study to identify hazardous drivers by evaluating their visual recognition abilities of novice and elderly.