Optimizing Autonomous Cargo Transport with Quantum Algorithms and Satellite Image Fusion - Prabhu, Bennet; Muthukumar, B. (2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI), 2024)
Railways are popular for moving goods because they are fast and can carry a lot. Due to technological development, researchers are now thinking about using self- driving systems instead of regular trains to make transporting goods more efficient and safer. Current train control systems sometimes make slow decisions which can be unsafe. This research introduces a new way to make these decisions faster and better for self-driving trains. We propose an algorithm is called the Near-field scene Quantum optimization (NFQO) algorithm. This uses live satellite images to help the train understand its surroundings. Additionally, a Hexagonal Grid (HG) tool, it helps the train pick the best route quickly. The main advantage of developing quantum algorithm is used to make decisions super-fast and accurate. When combined NFQO and HG with satellite images, improves reliable and effective self-driving cargo trains transportation. We've tested NFQO, and it's better than other available tools. This research, which combines quantum technology, satellite images, and self-driving systems, points to exciting future developments in train transportation.