Vehicle Level

Vehicle-level research focuses on improving the reliability, safety, and efficiency of vehicles through advanced diagnostics and predictive maintenance. Current efforts leverage machine learning, particularly ensemble methods like gradient boosting and deep learning architectures such as BiLSTMs and CNNs, to analyze sensor data, predict potential failures, and optimize maintenance schedules. These advancements enable more accurate fault diagnosis, reducing downtime and improving operational efficiency across various vehicle types, from armored vehicles to autonomous vehicles, and impacting both public and private sectors. The resulting improvements in predictive capabilities and optimized maintenance strategies contribute to safer and more cost-effective vehicle operation.