Ensuring ISO 26262 functional safety in advanced driver assistance systems (ADAS) is increasingly complex as these platforms integrate artificial intelligence (AI) for perception, decision-making, and vehicle control. Traditional safety mechanisms are largely deterministic, but AI introduces non-determinism, creating challenges for verification, validation, and certification. Real-time vehicle telemetry, sensor outputs, and environmental inputs are processed through machine learning algorithms that forecast hardware and software faults before they escalate into hazardous conditions. These predictions are systematically integrated with ISO 26262 safety measures, enabling adaptive diagnostics, fault isolation, and rapid recovery strategies. The AI model introduces hazards such as data bias, model drift, opaque decision-making, and unsafe automation. A dedicated AI Hazard Analysis and Risk Assessment addresses data quality, validation, monitoring, explainability, and fail-safe mechanisms alongside system-level safety controls.
The proposed approach demonstrates measurable improvements, including up to 25 % higher diagnostic coverage and fault-recovery times under 30 ms, while maintaining ASIL-D compliance and adhering to FTTI, SPFM, and DC requirements. Hardware-in-the-loop (HIL) simulations validate system performance and robustness under diverse operational scenarios. Future work focuses on uncertainty quantification and explainable AI integration, enhancing traceability and safety certification readiness for intelligent ADAS controllers. By demonstrating how AI can complement functional safety principles instead of conflicting with them, this study provides OEMs and Tier-1 suppliers with a roadmap for deploying certifiable, intelligent, and resilient ADAS platforms. This framework ensures safer, more reliable AI-enhanced vehicle systems while bridging the gap between emerging AI technologies and rigorous functional safety standards. This paper presents a predictive fault management framework that enhances functional safety in ADAS controllers by combining AI-driven predictive models with ISO 26262 safety mechanisms. This work uniquely bridges deterministic ISO 26262 workflows with predictive AI fault forecasting. In this framework, the AI model is used solely as a diagnostic enhancement and is not credited as an ISO 26262 safety mechanism; all safety decisions and fault reactions remain under deterministic safety-shell control.