## 1 Introduction
This method employs twelve parameters to model the individual characteristics of a driver. [PERSON] et al. considered a driver behavior as the second order system [12]. This study estimated two parameters to represent the operation characteristics. Although the above two methods focused on estimation of the operation characteristics, they do not discuss the trajectory prediction. Note that [PERSON] et al. proposed a method to predict a trajectory using the deep learning framework [13]. Based on real experiments, it was proven that the method achieves higher prediction accuracy compared to the previously proposed ones. However, the data-driven approach has the limitation that the performance largely depends on  
Figure 1: Rear collision caused by the interrupting vehicle: if the autonomous vehicle suddenly decelerates to avoid collisions with interrupting vehicles, it may lead to a crash with the following vehicle. the chosen training dataset. If the real conditions differ significantly from that of the training dataset, the performance of the approach may deteriorate. Considering the above limitations of previous studies, we propose a novel method to anticipate maneuvers of adjacent vehicles and to prevent a collision with them. Adaptive cruise control (ACC), which adjusts the speed and distance from the preceding vehicle, is already commercialized in recent years. [PERSON] et al. proposed the intelligent driver model (IDM) for the ACC system [14]. This method calculates the acceleration of the ACC vehicle based on the preferences of host driver, which are the desired speed, the desired time gap, and the desired deceleration with respect to the preceding vehicle. However, [PERSON] et al. reported that the IDM shows unrealistic behavior when the cut-in vehicle exists [15]. [PERSON] proposed a method to consider not only the preceding vehicle but also the cut-in vehicle near an on-ramp [16]. However, this method assumes only mandatory lane changes performed near an on-ramp or off-ramp. Therefore, it is limited to handle discretionary lane changes on freeways. [PERSON] et al. proposed the ACC and cooperative ACC (CACC), which uses a vehicle-to-vehicle (V2V) communications system [17]. They achieved smooth and stable car-following behavior by applying the CACC. [PERSON] et al. proposed the smart driver model (SDM) to improve the traffic flow stability [18]. By simulation, it was demonstrated that the SDM is able to stabilize the traffic flow under cut-in condition compared to previous models. However, the authors of [17] and [18] assume that all adjacent vehicles, including the host vehicle, implement the ACC and there is no manual driving vehicle. Therefore, the effectiveness of the two methods cannot be guaranteed under the condition where human drivers and autonomous vehicles coexist. In the simulation conducted by [18], the sudden deceleration was confirmed when a cut-in occurred. As human drivers cannot respond the sudden deceleration unlike the ACC vehicles, rear collision may occur. According to the previous study, ACC can occasionally lead to a rear collision, as it only focuses on the preceding vehicle [19]. By contrast, the method proposed in the present paper predicts the trajectories of the surrounding vehicles, including not only lane-changing vehicles, but also the following ones, behind the autonomous vehicle in question. In addition, the proposed method estimates the operation characteristics of other drivers and applies these estimates to the trajectory prediction. Then, the method assesses the collision risk according to the predicted trajectories and automatically controls its speed to maintain a safe distance from both the lane-changing and the following vehicles to minimize a risk indicator. Through the dynamic characteristic potential field method, which determines the distribution of potential fields depending on the relative distance and speed [20], the risk index is evaluated in this paper.