## 1 Introduction  
Advanced driving assistance system (ADAS) and autonomous driving system (AD) are expected to be at the center of future transportation systems as well as highly enhance traffic safety. To successfully bring autonomous vehicles (AVs) into our lives, they must be capable of managing complex urban environments, including various participants and complex traffic infrastructure. Among them, intersections are one of the most dangerous urban traffic infrastructures. It is reported that 21.5% of fatalities and even 40% of all accidents in the United States occur at intersections [1]. Although recent commercialized vehicles are equipped with ADAS functions, human drivers still need to focus on complex driving situations such as urban intersections. To deal with this, the predict-and-plan approach is typically applied; during the 'predict' step, the ego vehicle integrates numerous signals from sources such as sensors or traffic infrastructure and predicts future actions of several agents in the vicinity. In the planning step, the ego vehicle generates maneuvers based on the trajectory from the predict step. Therefore, the predict step is essential in the entire decision-making process, so several researchers have tried to develop methods to predict the intention of other traffic agents which not only have behavior patterns but also inherent uncertainty. Recently, most of cars that have been newly released have a certain level of ADAS built into the vehicle. For instance, in the most recently released GV80, Hyundai Motors group provides general ADAS functions such as forward collision-avoidance assist (FCA) and lane keeping assist (LKA) by default. The FCA offers a more diverse range of recognition options, including when a vehicle passes through an intersection or is laterally approaching. It also provides Highway Driving Assist II (HDAII), which includes machine-learning-based adaptive cruise control (ACC), automatic lane change assist when operating direction indicator lamps, and near-field vehicle recognition technology [2]. Hyundai Motors Group also announced that they have a plan to mass-produce highway driving pilots (HDPs) in the near future, which enables the vehicle to drive on its own, even if the driver leaves the steering wheel when driving on highways [3]. Practically, it is equivalent to SAE (Society of Automotive Engineer) Level 3, which is often compared to Tesla's Navigate on Autopilot (NOA). Tesla's autopilot is capable of steering, acceleration, and deceleration of the vehicle. In addition, the most recently updated feature, NOA, based on autopilot, enables the vehicle to change lanes and overtake other cars using map data [4]. As such, the vehicles on the market with most advanced ADAS are assessed to be within the SAE Level 2-3. Now, most of the conventional carmakers and vehicle technology providers are testing AD vehicles that are equivalent to SAE Level 4 within a certain area, and they have claimed that these verified AD vehicles can be used as shuttles and taxis, for example, 'Waymo One' of Waymo [5], Google's subsidiary, 'Robotax' of Tesla [6], and 'Pilot Project' by Mercedes Benz, and Bosch [7]. This trend of technological advancement once again confirms that the vehicle's responsibilities are increasing, and the perception and control technology must ensure a certain level. As stated above, since obtaining effective vehicle behavior analysis is critical for urban autonomous driving, tracking and calculating of the expected trajectory must be conducted. To better estimate the trajectory of vehicle movement, the performance of the state of estimation algorithms is one of the most significant factors. Thus, various approaches that have been theorized in literature have been tested, such as dynamic Bayesian networks (DBN), hidden Markov models (HMM), support vector machines (SVM), interacting multiple model (IMM), and vehicle-to-vehicle (V2V) communication [8; 9; 10; 11; 12].