## 1 Introduction  
Automated Driving Systems (ADSs) have the potential to reduce the number of accidents, lower emissions, transport the physically-impaired, and reduce the driver's workload [1]. Based on the Society of Automotive Engineers (SAE), ADS describes the variety of commonly used terms in intelligent transportation field, e.g.,: autonomous, self-driving, driverless, etc. [2]. Most ADSs break down several tasks of automated driving and, by utilizing an array of sensors and algorithms, they can be solved accordingly. Path planning is one of the essential requirements for all ADSs. The path planning stage of a vehicle requires it to obey traffic rules within road networks while a robot has no specific requirements to follow [3]. The scenario of path planning involves planning in known and unknown scenarios. Further enhancement involves planning for static and dynamic obstacles. Various perception sensing devices, for example, camera or Light Detection and Ranging (LiDAR) sensors have been used to scan the surroundings. Then, the data are processed by Simultaneous Localization and Mapping (SLAM) algorithm to divide the data into free space and fixed obstacles [4]. The result is known as a static map. The static map provides a piece of very essential information for the path planning algorithm as it needs to generate a traversable path of the vehicle based on its position with respect to the space within the static map. One of the drawbacks, however, as the area gets larger, is that the computational requirement tends to get higher. The transformation of one type of space to another type is one of the techniques to reduce computational complexity. The transformation method is typically used by machine learning algorithms. For example, in Support Vector Machine, the field is transformed using the kernel trick, which increases the dimension of the field [5]. In data science, the data are augmented to increase data variation [6]. It is found that, when augmented data are used to test the non-augmented model, the classification error is more prominent despite the same batch of images being used. There are many techniques that has been adopted to solve the path planning problem. The blind search technique, i.e., Breath First Search (BFS) [7] and Depth First Search (DFS) [8], traverses every single state available until the feasible solution is found. They are typically used to solve maze problems. BFS and DFS are simple to be implemented, but they require a lot of computational time if the given area is huge. Alternatively, there are other more efficient methods and they can be divided into four basic algorithms: graph-search based planners, sampling-based planners, interpolating curve planners, and numerical optimization approaches [3]. Graph-based planners, for example Djikstra Algorithm and A\({}^{\star}\), utilized the graph searching algorithm to search for a solution by traversing between grid or states. The sampling-based planners sample the configuration space and return solution when connectivity between them exists. The commonly used sampling-based planner's algorithm includes the Probabilistic Roadmap Map (PRM) and Rapidly Exploring Random Tree (RRT) [9]. The interpolating curve planners add a new set of waypoints given a range of references point. This technique typically increases the path continuity, which increases comfort and allows smoother motion. Polynomial, Bezier, and Splines are some of the well-known interpolation algorithms. Finally, numerical optimization approaches uses an algorithm like Particle Swarm Optimization (PSO) [10] or Genetic Algorithm (GA) to optimize a given objective function subjected to any constrained variable, for example, vehicle kinematic constraints or any environmental constraints. Each of these algorithms can be used independently and can be combined based on the application. Among the path planning algorithms, the graph search-based planner often deals with an occupancy grid or any approximation of a discrete cell-grid space.