The malicious node attacks on Proof-of-StakeProof-of-Stake (PoS) blockchainBlockchain are at risk to the securitySecurity and the legitimacy of autonomous vehicleAutonomous vehicles systems in supply chainSupply chain management. This attack compromised the integrity of important data exchanges and various operations. In this work, we have put forth a framework for detecting rogue nodes in PoS blockchainsBlockchain that is based on Explainable AIExplainable AI (XAI) and specifically tailored for applications involving autonomous vehiclesAutonomous vehicles (AVs). This framework implies several machine learningMachine learning models, which include models such as K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Decision Tree (DT) and Naive Bayes (NB) to analyze node behavior and to spot and identify anomalies such as delay, conspiracy, and interference. KNN obtained 0.8747, the highest accuracy score. We have investigated significant properties such as block height, coin age, and transaction timestamps using XAI techniques, including SHAPSHAP and LIMELIME; it increases openness and confidence in the machine learningMachine learning model. Metrics including precision, recall, F1-score, and ROC curves help us to calculate the performancePerformance so it promises strong evidence of abnormal and malicious activity. This method provides a reliable and trustworthy way to protect our vehicle operations in supply chainsSupply chain so guaranteeing a safe and reliable blockchainBlockchain data exchanges.