Optimizing Route Planning for Autonomous Electric Vehicles Using the D-Star Lite Algorithm

Every vehicle, including autonomous vehicles, requires a route to navigate its journey. Route planning is a critical aspect of autonomous vehicle operations, as these vehicles rely on guided paths or sequential steps to move effectively. Ensuring that the route is optimal is a key consideration. This study tests the D-Star Lite algorithm to determine the most efficient route. In simulation tests, the D-Star Lite algorithm was compared with the A-Star algorithm. The results showed that D-Star Lite outperformed A-Star, achieving an average distance reduction of 124 meters. Real-time testing involved finding a route from node 36 to node 0, resulting in a total distance of 803 meters. Additional tests focused on route replanning in real-time scenarios. For instance, the initial route passing through nodes 36 →37→38→39→40→41→42→43→44→45→0 was adjusted to an alternative route: 36→37→38→46→26→11→2→4→1→0. Based on the results, the D-Star Lite algorithm proves effective in identifying the best route for autonomous electric vehicles while also enabling real-time route replanning.

@article{Suprapto2025,
  title     = {Optimizing Route Planning for Autonomous Electric Vehicles Using the D-Star Lite Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {1},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Bhakti Yudho Suprapto and Suci Dwijayanti and Desi Windisari and Gatot Aria Pratama},
  doi       = {10.14569/IJACSA.2025.01601103},
  url       = {https://doi.org/10.14569/IJACSA.2025.01601103}
}