The convergence of machine learning, embedded systems, and safety-critical automotive applications has fundamentally transformed the architecture of autonomous driving systems. While deep learning models have enabled unprecedented advances in perception tasks such as lane detection and object recognition, they have simultaneously introduced new challenges related to system reliability, fault tolerance, and operational safety. This research presents a comprehensive and theoretically grounded framework that integrates fault-tolerant embedded architectures with machine learning-driven perception systems to ensure dependable autonomous vehicle operation. Drawing upon foundational principles of dependable computing and recent advancements in automotive software architecture, this study examines the interplay between hardware redundancy mechanisms, software engineering practices for machine learning, and system-level safety models. The methodology employs a conceptual synthesis of heterogeneous fault-tolerant architectures, including dual-core lockstep systems, and modern deep learning pipelines used in lane detection and end-to-end driving models. The results indicate that combining redundancy-based fault tolerance with robust software engineering practices significantly enhances system resilience against both hardware faults and algorithmic uncertainties. Furthermore, the incorporation of hazard analysis frameworks and hierarchical safety models effectively limits fault propagation across perception and control layers. The discussion explores critical challenges such as model interpretability, error correlation, and common-mode failures in machine learning systems. The study concludes by emphasizing the necessity of hybrid architectures that bridge traditional reliability engineering with modern artificial intelligence systems, offering a pathway toward safer and more dependable autonomous vehicles.