On Systematically Exploring the State Space for Events with SFCs

Abstract: In the process of developing autonomous driving systems (AD systems), ensuring safety remains a constant and continuous priority. Scenario-based testing is a popular approach to guarantee the safety of AD systems, which however meets challenges in terms of diversity and efficiency of generation, as well as evaluating existing test case datasets in terms of their coverage of the different variations of a particular maneuver. This research aims to support approaches for diverse and efficient scenario-based testing generation through the application of space-filling curves in determining the state space of specific vehicle maneuvers. The research goal is addressed through the use of design science, in which we produce an artifact consisting of a theoretical approach that acts as an initial foundation for an algorithm that efficiently generates diverse variations of specific maneuvers. An important discovery made in this research is understanding how SFC encoding impacts the way permutations are generated, and the implications of this in terms of what is required to ensure plausible maneuvers are permuted. This research mainly provides an initial theoretical approach to generate permutations of a limited subset of lane change maneuvers based on defined constraints.