eISSN: 2791-173X editor@grpublishing.org
All Journals
Submit your article

Global Multidisciplinary Journal

Open Access Peer Review International
Open Access

A Unified Fault-Tolerant and Machine Learning-Driven Architecture for Autonomous Driving Systems: Integrating Dependability, Perception, And Embedded Reliability

DOI
PDF
Dr. Sofia Laurent 1 ,
4 Department of Computer Science and Engineering, École Polytechnique, France

Abstract

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.

 

Keywords

Autonomous driving, fault tolerance, machine learning systems, embedded systems

References

📄 Aly, M. (2008). Real time detection of lane markers in urban streets. IEEE Intelligent Vehicles Symposium, 7–12.
📄 Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., & Zimmermann, T. (2019). Software engineering for machine learning: A case study. Proceedings of the International Conference on Software Engineering, 291–300.
📄 Arp, D., Spreitzenbarth, M., Gascon, H., & Rieck, K. (2014). Drebin: Effective and explainable detection of android malware in your pocket.
📄 Autumn Model. Steering models repository.
📄 Behere, S., & Törngren, M. (2015). A functional architecture for autonomous driving. Automotive Software Architecture Workshop, 3–10.
📄 Behrendt, K., & Witt, J. (2017). Deep learning lane marker segmentation from automatically generated labels. IEEE/RSJ International Conference on Intelligent Robots and Systems, 777–782.
📄 Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J., & Zieba, K. (2016). End to end learning for self-driving cars.
📄 Avizienis, A., et al. (2004). Basic concepts and taxonomy of dependable and secure computing. IEEE Transactions on Dependable and Secure Computing.
📄 Al-Kuwaiti, M., Kyriakopoulos, N., & Hussein, S. Network dependability, fault-tolerance, reliability, security.
📄 Ozer, E., Venu, B., Iturbe, X., Das, S., Lyberis, S., Biggs, J., Harrod, P., & Penton, J. Error correlation prediction.
📄 Dubrova, E. (2013). Fault-tolerant design.
📄 Kaufman, L. M., Bhide, S., & Johnson, B. W. Modeling of common-mode failures in digital embedded systems.
📄 Yiu, J. Design of SoC for high reliability systems with embedded processors.
📄 Kottke, T., & Steininger, A. A reconfigurable generic dual-core architecture.
📄 Mitra, S., et al. (2000). Common-mode failures in redundant VLSI systems: a survey. IEEE Transactions on Reliability.
📄 Rodrigues, C., et al. (2019). Towards a heterogeneous fault-tolerance architecture based on ARM and RISC-V processors. IECON.
📄 Papadopoulos, Y., & McDermid, J. A. (1999). Hierarchically performed hazard origin and propagation studies.
📄 Sangiovanni-Vincentelli, A., & Di Natale, M. (2007). Embedded system design for automotive applications. IEEE Computer.
📄 HIS Members and Partners. (2007). Specification Requirements Interchange Format (RIF).
📄 Abdul Salam Abdul Karim. (2023). Fault-Tolerant Dual-Core Lockstep Architecture for Automotive Zonal Controllers Using NXP S32G Processors. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 877–885. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7749

How to Cite

Dr. Sofia Laurent. (2025). A Unified Fault-Tolerant and Machine Learning-Driven Architecture for Autonomous Driving Systems: Integrating Dependability, Perception, And Embedded Reliability. Global Multidisciplinary Journal, 4(12), 153-158. https://www.grpublishing.org/journals/index.php/gmj/article/view/377

Most read articles by the same author(s)

1 2 3 4 5 6 7 8 9 10 > >> 

Similar Articles

61-70 of 103

You may also start an advanced similarity search for this article.