Operational Hypervisor: Anomaly Detection and Handling for Automated Electric Vehicles

Abstract

The transition toward fully automated electric vehicles (AEVs) demands robust safety mechanisms capable of addressing unforeseen internal critical situations without reliance on human drivers. Conventional diagnostic systems remain constrained to predefined failure modes and cannot capture all safety-critical anomalies. This paper introduces an operational hypervisor framework, an integrated anomaly detection and fault-tolerant control advisor, designed to enhance unforeseen internal-system critical situations in AEVs. The proposed approach combines data-driven anomaly detection, including isolation forests, correlation analysis, and explainable AI (XAI) to capture anomalous patterns across heterogeneous AEV subsystems. Beyond anomaly detection, the operational hypervisor functions as a fault-tolerant advisory layer, attributing anomalies to their probable sources, quantifying their potential impact, and recommending context-aware corrective actions to ensure safe operation. To ensure robustness, the framework employs multistage data preprocessing, improving sensitivity to both shortterm and persistent anomalies. Designed for real-time execution, the hypervisor can be seamlessly integrated into the control architecture of AEVs, allowing interaction with decision-making and vehicle supervisory layers. Experimental evaluation with real occurrences of safety-critical AEVs anomalies verifies the system’s capability to extend detection beyond conventional diagnostic limits and provide interpretable feedback to controllers. By coupling explainable anomaly detection with anomaly handling, operational hypervisor advances the state of operational safety management in AEVs