A risk evaluation method for human-machine interaction in emergencies based on multiple mental models-driven situation assessment

In the process of human-machine interaction (HMI) under abnormal situations, situation assessment (SAS) is a critical component as incorrect SAS can feed into inappropriate actions and cause severe accidents. Therefore, it is essential to model SAS for HMI risk evaluation. During emergency situations, SAS is predominantly affected by time pressure. However, current researches focus on deliberative SAS with sufficient time and have limitations in quantifying the time pressure effect on SAS during emergencies. This paper proposes a risk evaluation method for HMI in emergencies by quantitatively modeling the time pressure effect on SAS under the recognition-primed decision (RPD) model framework. Firstly, SAS process is characterized as identifying the first plausible cause of an abnormal phenomenon driven by multiple Bayesian network-based mental models, and a simulation algorithm for SAS process is proposed to evaluate HMI risk. Secondly, two effects of time pressure on SAS activities, i.e., filtration effect on information perception and impair effect on knowledge retrieval, are quantified based on the Saliency-Effort-Expectancy-Value (SEEV) model and the Adaptive Control of Thought-Rational (ACT-R) theory. Finally, this method is applied to a Boeing 737-31S accident with high time pressure and results demonstrate the effectiveness of the method.