Finding the shortest path is crucial for efficient route planning in autonomous vehicles (AVs). However, traditional algorithms struggle with the
vast scale and dynamic nature of modern road networks. This paper explores various approaches for tackling this challenge. The widely used Dijkstra algorithm is efficient but limited for large graphs. A* offers improved performance but requires accurate heuristics. Parallel computing methods show promise for faster processing in large-scale scenarios. Additionally, research into generalized Dijkstra and efficient algorithms for dynamic graphs holds potential for handling unforeseen obstacles. The paper highlights the need for robust pathfinding solutions that can adapt to real-time traffic data and dynamic changes in the environment. By leveraging advancements in graph theory, algorithms, and parallel processing, we can pave the way for reliable and efficient route planning for AVs.