## 1 Introduction
Traditional learning methods like DBN and HMM are frequently used since they are simple, fast, and do not require lots of data to become trained [13]. For instance, [PERSON] et al. uses DBN, combining probabilistically uncertain observations on the vehicle's behavior and local characteristic information to estimate driver maneuvers [14]. In addition, [PERSON] et al. proposed a prediction framework based on HMM using a database of real driving data such as speed, acceleration, and yaw rate [15]. As a discriminative approach, SVM is used as a learning framework for binary classification. [PERSON] et al. present a comparison and validation of performances of SVM, HMM, and other traditional approaches consisting of TTI-, RDP-, and SDR-based algorithms [16]. In recent years, along with the advancement of information and communication technologies, V2V communication is often applied to the prediction protocol. [PERSON] et al. introduce a decentralized intersection protocol for mixed traffic environments where all automated vehicles use V2V communications [17]. Since this information-driven control research area is active, not only V2V but also vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), technologies are arousing interest. As stated above, the IMM algorithm is a method used to determine the expected target trajectory in selecting among filter models derived from the behavior of targets. To select the best hypothesis, the IMM consists of four main steps: interaction, filtering, update, and combination. At the interaction step, the initial values of each model for the filtering step are calculated by receiving signals from sensors. These initial values are transferred to the filtering step. In the filtering step, each model independently conducts the calculation and deduces the state prediction. Typically, a Kalman filter (KF) is applied at this step. Then, the probabilities of models are updated at the update step, and by combining the probabilities and sending them to the interaction step again at the combination step, one cycle of the IMM is finished. Since the IMM was introduced in the late 1980s, it is generally known and has been implemented on a wide range of conditions. [PERSON] et al. used IMM to estimate vehicle state differing road friction [18]. [PERSON] et al. illustrate that the noble IMM estimator works accurately with the external disturbances and inputs of steering [19]. As the IMM is verified in plenty of conditions, the IMM algorithms are also often modified. [PERSON] et al. presents an adaptive IMM which utilizes the models for an adaptive turn rate in order to track a target for maneuvering [20]. In addition, the IMMs are classified depending on how they use multi-model fusion criteria as scalar weight IMM (SIMM), diagonal-matrix-weight interacting multiple model, and matrix weight interacting multiple model [21; 22]. These new approaches weigh differently when mixing beginning model values with states and corresponding covariances at each step of the IMM algorithm. [PERSON] et al. also introduced Ho filtering into the distributed interacting multiple model (DIMM) algorithm instead of KF, for target tracking of maneuvering during which the measurement noise is statistically unknown [23]. [PERSON] et al. propose a new algorithm suitable for multi-object tracking using multi-data fusion by applying centralized KF to a typical IMM. Since it is reasonable and easily adaptable, the IMM is applied in a variety of fields which require tracking of the maneuvering target. The IMM filter is often applied in aircraft tracking problems, such as in the study by [PERSON] et al. [24]. [PERSON] et al. used IMM for 3D human motion tracking [25]. For application to autonomous vehicles, an IMM filter that integrates market commercial GPS and vehicle local sensors is implemented to develop a vehicle localization algorithm [26].