Message Priority Classification Framework for Autonomous Vehicles in Supply Chain Management

In modern supply chain management, efficient communication and timely decision-making are essential to ensuring smooth operations and avoiding descriptions. This is ensured by efficient communication and timely decision-making. Prioritizing essential messages with these systems becomes essential as autonomous technologies become more integrated. This work presents a message priority classification framework formulated to refine communication processes within autonomous vehicles (AVs). This plays a growing role in supply chain logistics. Our framework uses machine learning (ML) models such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LR) which are implemented to classify message priorities in real-time, assuring efficient and uninterrupted communication. The experimental outcomes show that the RF model outperformed other models and achieved the highest accuracy of 0.9714. Further, the proposed framework enhances the modularity of AV communication systems and supports efficiency in decision making in complex and variable supply chains. This study emphasizes the significance of ML in optimizing communication leading to more dependable and efficient supply chain operations for AVs.