## 2 Overview
### Overview of the Proposed Method  
An advanced ACC (AACC) system is proposed based on the operation characteristic estimation and trajectory prediction to solve the limitations described in Section 1. Figure 3 shows the schematic of the proposed method. The AACC consists of two parts: a predictor and a planner. The predictor is generated according to the number of adjacent vehicles. If there are \(N\) adjacent vehicles around the ego vehicle, \(N\) units are generated, and then each unit predicts the trajectory of one adjacent vehicle. The inputs of the predictor are the data on the position and speed of the ego and the adjacent vehicles. These information can be acquired using GPS and a controller area network (CAN) bus. Laser scanners are installed to measure the position and speed of the adjacent vehicles. The predictor consists of three subparts: driving intention estimation, operation characteristic estimation, and trajectory prediction. The outputs of the predictor are the trajectories of \(N\) adjacent vehicles. First, this paper defines four driving intentions, \"keeping\", \"changing\", \"arrival\", and \"adjustment\". The proposed method handle the intention estimation as a multiclass problem. Each intention indicates a class, and it is estimated by the support vector machine (SVM). The lateral movement of the adjacent vehicles is used as a feature. Details of this method are provided in Section 3.1. The output of this part is the driving intention of each driver. Second, the operation characteristics of the adjacent drivers are estimated at each time step. Using the measured information, four operation characteristic variables are estimated: three parameters of the GM model and the reaction time. The proposed method applies the Levenberg-Marquardt algorithm to determine the real-time values of the considered variables. The details of this part are explained in Section 3.2. Third, the method applies the estimated driving intention and operation characteristic variables to perform the trajectory prediction. In general, different schemes are conducted according to different intentions. When drivers intend to keep the current lane such as keeping and adjustment, they aim to maintain the safe distance from the preceding vehicle of the same lane. However, when drivers intend to change a lane such as changing and arrival, they must consider the vehicles in the adjacent lane. The proposed method is based on this assumption. Two prediction methods are applied to each direction. For the longitudinal direction, the GM model is used to calculate acceleration based on the estimated values of operation characteristic variables. For the lateral direction, the proposed method uses the sinusoidal model based on the estimated intention. Section 3.3 describes details of the trajectory prediction method. Figure 3: Schematic of the proposed method: the green vehicle represents the ego vehicle, and the blue ones are the adjacent vehicles, which are the targets of operation characteristic estimation and trajectory prediction. The ego vehicle is equipped with measurement devices used to acquire the data on the distance and speed of the adjacent vehicles. The proposed method is implemented within the ego vehicle. Based on the outputs of the predictor, the planner calculates the acceleration of the ego vehicle to maintain the safe distance from adjacent vehicles. The planner consists of two subparts: collision risk assessment and risk minimization. The dynamic potential field method is applied to evaluate the collision risk [20]. As the risk index, the repulsive potential energy generated from the adjacent vehicle is defined. The large amount of the energy is produced when two vehicles are near each other and move rapidly. In contrast, the small repulsive potential energy is produced when the ego vehicle maintains a sufficient distance from the adjacent vehicle and adjusts its speed appropriately. The value of repulsive energy reflects the collision risk. Section 3.4 explains details of this approach.