Path Planning of Autonomous Vehicle for Real World Scenario Using CARLA

A form of navigation problem called path planning can be resolved using a variety of techniques. This paper presents an overview of path planning techniques, specifically focusing on finding the shortest and most efficient path in a static environment. Self-driving autonomous vehicles can identify the safest, most practical and economically advantageous routes from source to destination using appropriate path planning and decision-making in real-world urban contexts. The proposed work first utilizes an open-source CARLA Simulator to implement path planning using the A star algorithm in its inbuilt town map. It makes use of CARLA library modules such as Waypoint API, CARLA Townmap, and PID controllers for its functionality. Secondly, the local real-world map is exported from the osm.org website and consists of local geographic data required to demonstrate the path planning of autonomous vehicle in a real-world environment. The results are demonstrated using the simulator. With several path planning algorithms present, this work utilizes A* algorithm and gives out the shortest path between start and end locations. The major advantage of using the CARLA simulator is that we can use the inbuilt Python API to convert a given exported .osm file to a .xodr file, which can be integrated into the simulator, thus allowing our algorithms to be tested in real-world scenarios.