Motion Control Bayesian Neural Visual Odometry Intelligent Routing For Traffic Management In Wireless Network

Machine Learning has massive influence in the automotive industry whichleads to evolution of intelligent routing for traffic management in wireless network via autonomous vehicles(AVs). The evolution of numerous automotive platforms for efficient traffic management has been the current trend.A method called, Bayesian Neural Visual Odometry and Polynomial Regression (BNVO-PR) with vehicle to vehicle communication for traffic management ensuring intelligent routing for autonomous driving is proposed. This method has three sections, namely, perception, localization and prediction.Spatio-Temporal Motion Control-based Perception is applied to the raw Udacity Self Driving Car dataset provided as input to obtain robust point of interest object detection with which intelligent routing can be ensured. Second, the obtained point of interest results are subjected to Bayesian Neural Visual Odometry-based Localization, ensuring computationally efficient object recognition for significant traffic management. Finally, with the recognized objects obtained, Polynomial Regression-based Autonomous Vehicle Prediction is designed to interpret actuate kinematic maneuvers in AVs, ensuringintelligent routing for efficient traffic management in wireless network concurrently. There is a reduction inrouting overhead and performance evaluation like precision, routing accuracy and routing time during autonomous driving using our network when compared with the existingmethods, leads to higher routing accuracy during inference, achieving accurate autonomous driving.