Driving Network

Driving networks, encompassing connected and autonomous vehicles (CAVs), aim to optimize traffic flow, enhance safety, and improve passenger experience through coordinated vehicle behavior and information sharing. Current research focuses on developing efficient routing algorithms, often employing hybrid models combining informed search and heuristics, and improving the accuracy of localization and prediction models using machine learning techniques like conditional neural fields and gradient boosting machines. Addressing data quality issues and ensuring model interpretability are crucial for reliable deployment, with applications ranging from real-time traffic management to improved autonomous navigation systems.