## 2 Related Work  
Traditional path planning algorithms are represented by the graph search-based method, sampling-based method, and genetic-based method. The representative algorithms of each are A*[2] and D*[3] for the graph search-based method, RRT*[4; 5] for the sampling-based method, and PSO[6; 7] for the genetic-based method. Moreover, recently, APF[8] using the gravitational field principle has been studied.  
From these traditional methods, various studies for path planning have been performed. However, most common path planning studies are simulations based on theories, and not real environments, or in real-world environments, mainly focusing on highway situations. To consider nearby obstacles and constraints, path planning algorithms with mathematical equations or costs are studied. Therefore, based on methods that can extract mathematical meanings, such as RRT*, APF, and PSO, various path planning algorithms have been studied[5; 9].  
Moreover, to apply the path planning algorithms to real-world autonomous vehicles, the generated path needs to be smooth. Real-world vehicles are larger and faster than a small-sized mobile robot. Therefore, if the generated path is bumpy, it is not good for the stability performance of behavior control. Therefore, research has been done in order to smooth the generated path. The represent methods are shown in [10]. Reference [10] compares curve fitting methods, namely the Akima splines method, three-order polynomial, cubic Bezier curve, and the 3 th degree Bezier curve.  
In addition, there are various approaches to path planning studies. In particular, the method of generating candidate paths and selecting the best one among the candidate paths is the most common one. This method is introduced in [11; 12; 13; 14; 15]. Reference [11] is a paper that proposes path planning in a closed-path autonomous race-driving environment. The paper proposes setting up reference nodes in the driving area, generating multiple candidate paths by connecting the nodes using a cubic polynomial, and selecting the optimal paths. Since the generated paths are the result of node connections, they requires more nodes if a user wants to cover a broad region, which leads to lower real-time performance. The authors of [12] applies the Frenet frame method to predict candidate paths. Among the candidate paths, the one nearest to the reference path where obstacles do not affect the path is selected. Reference [13] applies sampling-based methods that generate multiple random path points. The paper constraints the region where the RRT*'s random points are generated using [PERSON]'s vehicle model. The authors of [14] apply the cubic spline method to connect a route from the start point to the arrival candidate points; a similar method was also applied in [15]. On the other hand, the proposed urban-based path planning algorithm searches for just one proper path without using a candidate path, by using A* and APF. This approach is similar to that in [16]. However, the work in [16] incorporates behavior control and path generation through a model of predictive control [17], making it difficult to consider additional peripheral states when autonomous driving was done.