Supervised dynamic probabilistic risk assessment: Review and comparison of methods

With the adoption of autonomous systems in higher levels of autonomy, large-scale, complex and dynamic sys-tems are becoming commonplace. Ensuring safe operation of safety-critical autonomous systems is paramount, typically approached through risk assessment. Two challenges associated with using traditional risk assessment methods for complex systems are that these systems are dynamic (i.e., their state changes over time) and interactions between subsystems and components may lead to unpredictable behaviors and impact on the surrounding environment and other systems in the close vicinity. Dynamic probabilistic risk assessment (DPRA) methods are possible solutions to these challenges, where the dynamic and uncertain nature of the systems is considered. The methods, however, usually face combinatorial explosion related to hazards and scenarios, which make their practical application prohibitive; in the DPRA literature, this problem is known as the state explosion problem. In this paper, we present a literature review on methods for DPRA, with focus on the existing solutions to the state explosion problem. Specifically, we analyze and compare these solutions in terms of computational time complexity, traceability and state-space coverage. Finally, we discuss the comparisons and propose potential paths to improved solutions for the state explosion problem based on the knowledge gained in the study.