Recent advancements in artificial intelligence (AI) have revolutionized vehicular technology, enabling the development of intelligent systems that enhance road safety and driver experience. A critical challenge in this context is the accurate modeling of driver behavior, especially under risky driving conditions. Traditional approaches often lack the capacity to integrate risk assessment into behavioral modeling, limiting their applicability in real-world scenarios. To address this gap, this chapter introduces the risk-aware adaptive learning algorithm (RA2L)—an AI-enabled driver behavior modeling framework that incorporates failure mode and effects analysis (FMEA) principles. RA2L systematically quantifies risk through risk priority numbers (RPNs) derived from severity, occurrence, and detectability metrics, effectively prioritizing high-risk behaviors. We formulate the RA2L model as an optimization problem and validate it through extensive simulations using synthetic driving data. Experimental results demonstrate RA2L's superior performance in risk-aware classification, achieving high accuracy and significantly reducing false negatives in detecting dangerous driving behaviors, rendering it a promising solution for integration into advanced driver-assistance systems (ADAS) and autonomous driving platforms.