Safety and Reliability of Autonomous Systems

Dear Colleagues,

In the realm of autonomous systems, mathematics serves as the bedrock upon which safety and reliability are meticulously constructed. This Special Issue delves into the mathematical frameworks that underpin the robust design, analysis, and validation of autonomous systems, ensuring that they operate within safety and reliability boundaries.

This Issue commences with an exploration of probabilistic models, which are instrumental in quantifying uncertainty and risk in autonomous decision making. Bayesian networks and Markov decision processes are explained, as they provide a probabilistic lens through which to view the complex interactions within autonomous systems.

Moving forward, this Issue examines the role of optimization techniques, such as linear and nonlinear programming, in the efficient allocation of resources and the determination of optimal control strategies, mathematical tools which are critical for balancing performance with safety constraints.

The importance of robust statistics is also highlighted, as it enables the detection and mitigation of outliers and anomalies which could compromise system integrity. Techniques such as robust regression and outlier detection algorithms are discussed in the context of maintaining data fidelity and system robustness.

Furthermore, this Issue explores the application of graph theory and combinatorial mathematics in the design of efficient communication networks and the mapping of complex environments. These mathematical constructs are essential for the navigation and coordination of autonomous agents.

Lastly, this Issue touches upon the emerging field of machine learning, where mathematical models are used to learn from data and improve system performance over time. The interplay between traditional mathematical methods and the evolving artificial intelligence landscape is a focal point, underscoring the dynamic nature of autonomous system reliability.

Dr. Sharareh Taghipour
Guest Editor

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• autonomous systems
• reliability modelling
• safety engineering
• quality assurance
• mathematical modelling
• probabilistic model
• nonlinear programming