## 1 Introduction  
Autonomous or self-driving vehicles are attracting attention as a solution for transportation due to their various advantages such as improved safety, increased traffic efficiency, decrease in pollution, and equitable access to mobility. The coverage of autonomous driving has been extended from the motorway to urban roads to spread these advantages. In this process, uncontrolled intersections are one of the most dangerous driving situations because there is no traffic signal to control the traffic participants. In particular, the accident rate and fatality rate at uncontrolled intersections are higher than in other driving environments [1, 2]. Several studies tried to figure out the important factors related to the geometric and traffic-related elements that affect the accident rates of intersections [3, 4]. Based on the achievements of accident analysis in intersections, situation awareness, decision making, and motion planning become important issues to pass an uncontrolled intersection safely and comfortably. Various algorithms have been proposed for autonomous driving at uncontrolled intersections. Various approaches have been used for decision making and motion planning for uncontrolled intersections. In many previous studies, decision making at intersections has been designed based on pre-defined policies. The Markov Decision Process (MDP) has been utilized as a policy-based motion planning algorithm. In particular, the partially observable Markov decision process was introduced to overcome the limitations of unmeasurable variables from the sensors and communication [5, 6, 7, 8]. In these studies, the uncertainty of the surrounding vehicles was considered at the maneuver level, which affects the driving policy decisions of the autonomous vehicles. The Bayesian network was also utilized for probabilistic decision making at intersections. A time window for observation was considered to improve the performance of the Bayesian network [9]. The rule-based approach was applied to select the optimal rule from the pre-defined rule sets [10]. The MDP and the deep Q-network algorithm were established to obtain the optimal driving policy based on traffic image analysis using convolution neural networks [11]. In addition to the decision-making problem, there have been studies focused on determining the driving path and acceleration command for the autonomous vehicle. The combined approach of Gaussian processes regression and a rapidly exploring random tree was used to generate a desired local path to guide the autonomous vehicle through the intersection [12]. The robust Model Predictive Control (MPC) was used for optimal velocity planning under the assumption that all target information is available [13]. The loosely coupled low-complexity MPC was formulated for automated yield maneuvers [14]. The linear MPC with target vehicle motion prediction and driving mode decision was employed to achieve human-like behavior at uncontrolled intersections [15]. In addition, the cooperative scheduling method of all intersection-approaching vehicles was proposed under the assumption of a connected autonomous vehicle [16; 17; 18; 19]. In particular, virtual platooning was introduced, in which vehicles traveling on each branch are transferred to the same road [17]. In order to improve the performance of the decision-making and motion-planning algorithm, various studies have been conducted on traffic modeling and predictions on surrounding vehicles at intersections. The prediction can be classified as maneuver-level [5; 6; 7; 8; 19; 20] and trajectory-level prediction [9; 21] based on the output of the predictor. A game theory was used to model the vehicle interactions in unsignalized intersections [20]. The map-based prediction was used to provide the lane-level prediction results [9]. A learning-based approach, such as the support vector machine and the long short-term memory-based recurrent neural network [21; 22], was also utilized to predict the future behavior of surrounding vehicles.