Adaptive Fault-Tolerant Control and Probabilistic Risk Prediction for Autonomous Electric Vehicles Using Data-Driven Learning and Intelligent Sensing
Keywords:
autonomous electric vehicles, fault-tolerant control, probabilistic risk prediction, data-driven learning, intelligent sensing, system resilience, safety governanceAbstract
The increasing deployment of autonomous electric vehicles introduces a complex interplay between machine perception, drive-by-wire actuation, battery energy management, and safety-critical control. This paper examines the system-level design of adaptive fault-tolerant control and probabilistic risk prediction architectures that integrate data-driven learning with intelligent sensing. Rather than focusing on any single algorithm, the paper analyzes how sensing modalities, learning pipelines, control adaptation, and risk governance can be structurally combined to maintain graceful degradation under actuator, sensor, and environmental uncertainty. The discussion emphasizes architectural trade-offs among computational load, sampling frequency, energy consumption, explainability, and certification feasibility. It further addresses governance and policy challenges such as residual risk allocation, cross-domain data reuse, lifecycle validation, and fairness in risk distribution. The paper draws on cross-domain evidence from intelligent vehicle control, cellular traffic prediction, and cyber-physical system engineering to illustrate how transferable learning and probabilistic reasoning can strengthen vehicle resilience. It concludes that robust deployment requires not only more accurate prediction models but also coherent institutional frameworks that connect technical fault tolerance with regulatory oversight, infrastructure readiness, and public accountability.
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