Human error oriented stochastic hybrid automation for human system interaction

One of the main causes of accidents in share-control systems is the human error. In order to identify human errors and improve performance in human system interaction (HSI), it is of essential significance to explore the cognition mechanism and characterize the dynamic interaction scenario. However, very few methods have yet been proposed to analyze the entire human system reliability in a quantitative way. This paper tries to confront this challenge and develops a computational HSI model from the human error perspective based on stochastic hybrid automation (SHA). Under situation awareness (SA) centered cognition architecture, the fuzzy logic and fuzzy entropy are introduced to describe the cognitive process with uncertainty. Moreover, the Human reliability analysis (HRA) is also employed to characterize the performance fluctuation. Finally, the quantitative cognitive model is incorporated into SHA framework. Thus, the human error produced in HSI could be presented and demonstrated dynamically. The performance of the proposed method is tested through a case study of the yellow traffic light dilemma.