# Data-Aware Path Planning for Autonomous Vehicles Using Reinforcement Learning +
## 2 Related Work  
### Path Planning  
Path planning in autonomous vehicles is a critical component that ensures that these vehicles navigate from one point to another efficiently and safely. This process is broadly categorized into global and local path planning strategies [8]. Global path planning involves calculating a predefined route from the starting point to the destination based on high-precision maps that account for available road information, such as the length of the route and its traffic density. This method, often called static or offline planning, relies on comprehensive environmental data to determine the optimal geometric route without collisions [9]. On the other hand, local path planning, also known as dynamic or online planning, adapts to real-time environmental changes. It adjusts the vehicle's trajectory in response to immediate obstacles or alterations in driving conditions, necessitating a flexible and responsive approach to path planning [9]. Path planning algorithms in autonomous vehicles rely on diverse search algorithms and are generally grouped into three categories, namely graph search algorithms, traditional algorithms, and group optimization algorithms [8]. Graph search algorithms such as [PERSON]'s algorithm are among the various algorithms used for path planning. [PERSON]'s algorithm is foundational, scanning all vertices within a distance smaller than the specified distance between the starting point and the destination. It provides a reliable but computationally intensive solution for finding routes in less complex environments [10]. The A* algorithm [11], a classic goal-directed shortest path algorithm, combines the [PERSON] algorithm with the Best First Search algorithm [12] and refines this approach by guiding the search toward the target more efficiently [8]. This method leverages the network's geometric embeddings or the graph's structural features, including the configuration of shortest path trees, to target compact areas [13]. However, its application in road networks that prioritize travel time as a metric can be limited due to its performance issues when geographic distance is used as a limit [14]. The D* (Dynamic A*) algorithm [15] represents a significant improvement in dynamic path planning, capable of adjusting the planned route in the face of obstacles, thus enhancing the efficiency of secondary path planning by reducing the exploration space and improving computational efficiency [16]. Many traditional algorithms are used for path planning, each catering to different navigational requirements and constraints. The artificial potential field (APF) algorithm creates a virtual force field around obstacles, repelling the autonomous agent away from obstacles and attracting it toward the target [17]. This method is intuitive and effective for simple environments, but it can be difficult to use in complex scenarios with local minima where the vehicle might get stuck. Simulated annealing [18] and Tabu search algorithms [19] offer sophisticated means of escaping local minima and finding globally optimal paths by intelligently exploring the search space. The rapidly-exploring random tree (RRT) algorithm [20] stands out for its adaptability and efficiency in handling non-holonomic constraints, which are particularly relevant for autonomous vehicle navigation [21]. Holonomic systems are those in which the controllable degrees of freedom are equal to the total degrees of freedom, allowing for straightforward movement in any direction from any configuration [22]. This ideal scenario is not always practical in real-world navigation, especially for vehicles. Non-holonomic systems, in contrast, have constraints that limit the vehicle's movement directions based on its current state [22]. For example, a car cannot move sideways and its turning radius imposes constraints on its path planning. The RRT algorithm excels in these environments by randomly sampling the space and incrementally building a tree of possible paths.