Real World Vehicle

Real-world vehicle research focuses on bridging the gap between simulated and physical vehicle testing for autonomous driving systems and robotics. Current efforts concentrate on developing high-fidelity digital twins, employing algorithms like RRT and RRT* adapted for vehicle dynamics, and utilizing machine learning techniques such as graph neural networks to optimize vehicle routing problems. This research is crucial for ensuring the safety and reliability of autonomous vehicles, improving the efficiency of testing procedures, and advancing the understanding of the complexities inherent in transferring simulated results to real-world scenarios.