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This paper presents a comprehensive hazard analysis, risk assessment, and loss evaluation for an Evasive Minimum Risk Maneuvering (EMRM) system designed for autonomous vehicles. The EMRM system is engineered to enhance collision avoidance and mitigate loss severity by drawing inspiration from professional drivers who perform aggressive maneuvers while maintaining stability for effective risk mitigation. Recent advancements in autonomous vehicle technology demonstrate a growing capability for high-performance maneuvers. This paper discusses a comprehensive safety verification process and establishes a clear safety goal to enhance testing validation. The study systematically identifies potential hazards and assesses their risks to overall safety and the protection of vulnerable road users. A novel loss evaluation approach is introduced, focusing on the impact of mitigation maneuvers on loss severity. Additionally, the proposed mitigation integrity level can be used to verify the minimum-risk maneuver feature. This paper applies a verification method to evasive maneuvering, contributing to the development of more reliable active safety features in autonomous driving systems.

The advent of autonomous driving technology has accentuated the need for comprehensive hazard analysis and risk assessment (HARA) to ensure the safety and reliability of vehicular systems. Traditional HARA processes, while meticulous, are inherently time-consuming and subject to human error, necessitating a transformative approach to fortify safety engineering. This paper presents an integrative application of generative artificial intelligence (AI) as a means to enhance HARA in autonomous driving safety analysis. Generative AI, renowned for its predictive modeling and data generation capabilities, is leveraged to automate the labor-intensive elements of HARA, thus expediting the process and augmenting the thoroughness of the safety analyses. Through empirical research, the study contrasts conventional HARA practices conducted by safety experts with those supplemented by generative AI tools. The benchmark comparisons focus on critical metrics such as analysis time, error rates, and scope of risk identification. By employing generative AI, the research demonstrates a significant upturn in efficiency, evidenced by reduced timeframes and expanded analytical coverage. The AI-augmented processes also deliver enhanced brainstorming support, stimulating creative problem-solving and identifying previously unrecognized risk factors.

Motivated by the increasing concerns over environmental challenges such as global warming and the exhaustion of fossil-fuel reserves, the renewable energy industry has become the most demanded electrical energy production source worldwide. In this context, wind energy conversion systems (WECSs) are the most dominant and fastest-growing alternative energy production technologies, playing an increasingly vital role in renewable power generation. To tackle the pitch control problem of WECSs, this thesis proposes an optimal fault-tolerant fractional-order pitch control strategy for pitch angle regulation of WT blades subjected to sensor, actuator, and system faults. To showcast the effects of faults, changes in the system parameters are considered as a result of sensor, actuator and system faults with various levels of severity.} Furthermore, taking advantage of the favourable merits of higher-order SMCs and fractional calculus, this thesis develops a fault-tolerant fractional-calculus-based higher-order sliding mode controller for optimum rotor speed tracking and power production maximization of WECSs. \textcolor{black}{The partial loss of the generator output torque is considered as an actuator failure, leading to loss of partial actuation power. Moreover, active fault-tolerant fractional-order higher-order SMC strategies are developed for rotor current regulation and speed trajectory tracking of doubly-fed induction generator (DFIG) -driven WECSs subjected to model uncertainties and rotor current sensor faults. The developed controllers are augmented with two state observers, an algebraic state estimator and a sliding mode observer, to estimate the rotor current dynamics during sensors' faults.