## 6 Conclusions  
In order to predict the vehicle's right-turn trajectory during the intersection turning without depending on V2V or V2I communication technology, this paper presented a turning trajectory prediction method using the Triclothoidal curve. The Triclothoidal curve consists of three clothoidal curves sequentially connecting without discontinuities in the  
Figure 14: The box plot of the absolute prediction errors in each target intersection. Seventy-five percent of absolute errors in all cases were less than 1.2 m, and especially in intersection ID 2-3, the 75% of prediction errors were less than 0.4 m. In addition, 50% of absolute errors in all cases were less than 0.5 m.  
curvature. In this paper, we proposed the curve generation method and how to apply the Trelclothoidal curve to the intersection trajectory prediction method. By using the Trelclothoidal curve, the proposed prediction method could predict the turning trajectory by simply providing the steering angle, terminal point direction, and terminal point position. The driving data acquisition experiments conducted at actual urban intersections evaluated the effectiveness of the proposed method. A parameter estimation equation derived the geometrical correlation between the terminal point position of the Trelclothoidal curve and the shape and size of the urban intersection. In this paper, the data acquisition experiments were conducted with a manual driving car. In the next step, we will evaluate the effectiveness of the driver assistance by implementing the trajectory prediction to the autonomous or ADAS functions for intersections such as a collision avoidance system or a steering assistance system. This paper has contributed to the research on ADAS for intersection turning in the following ways:  
* A geometrical trajectory prediction method using the Trelclothoidal curve enabled the generation of a smooth and continuous turning trajectory with a simple, fast, and real-time capable low calculation cost;
* The proposed definitions of the terminal point of the prediction trajectory adapted to the variable sizes, lanes, and shapes of the intersections. The terminal point was calculated by giving only three geometrical parameters: intersection crossing angle and road widths of the entering road and exiting road;
* The driving data acquisition experiments revealed that the trajectory prediction method using the Trelclothoidal curve was superior to the conventional curve generation methods, such as the circular arc, Biarc curve, and clothoidal curve, and was effective in real urban intersections;
* We expect to broaden the application of the proposed Trelclothoidal curve generation method beyond the intersection right-turn to other situations such as left-turn or overtaking, because the curve can be generated by giving the steering angle, terminal point direction, and terminal point position. * As a further evolution of the proposed trajectory prediction method, it is also necessary to consider the adoption of communication technologies. Especially in autonomous driving technology, Vehicle-to-Everything (V2X) communication, including X2V, is a key technology. V2V and V2I communications are especially essential for intersection safety. As an example of V2X technology, 5G New Radio (NR) is expected to realize the reliable and large-capacity V2X communication via a cellular network [35]. Using this 5G NR technology, we expect the proposed method to realize the following functions: First, to provide an accurately predicted trajectory to other traffic participants enabling the mitigation of collision risk with oncoming objects in the invisible area; Second, to obtain the position, heading, steering angle, and target direction of other vehicles through communication, enabling the prediction of the trajectory of the surrounding objects; Third, to obtain the other vehicles' turning trajectory data, which can be utilized as a priori information to improve the accuracy of trajectory prediction. Conceptualization, Y.F., S.A. and P.R.