Dynamic Vehicle Stability Analysis Using IoT Sensors and Bayesian Network Approach

A new way to improve vehicle stability analysis is to combine IoT sensors with sophisticated analytical methods like Bayesian networks. It investigates how to use Bayesian networks to handle data collected in real-time by Internet of Things (IoT) sensors mounted on vehicles. A Bayesian network model is built using data from sensors that measure things like acceleration, yaw rate, wheel speed, and road conditions to calculate the probability of a vehicle's stability during dynamic maneuvers. One important part of this project is building a framework for Bayesian networks that can make decisions and infer things in real time using sensor data. Evaluate the model's capacity to forecast possible stability problems through simulation and practical testing, including understeer or oversteer. It tests the IoT sensor network in various settings to see how well it scales and adapts. The findings demonstrate that the proposed method is practical and effective in enhancing vehicle performance and safety via proactive stability control. IoT sensor integration with Bayesian networks provides a powerful approach to real-time data analysis and monitoring, which helps develop smart transport systems and vehicle safety technology.