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Human reliability estimation based on fuzzy logic-modified CREAM approach

Human reliability is one of the key issues in driver-vehicle systems as human-related accidents accounts for the highest proportion of total accidents. Furthermore, the behaviors of drivers become increasingly essential for driving safety as the driving context is of increasing complexity. Cognitive reliability and error analysis method (CREAM) provides the evaluation method for human reliability in industrial fields, when it is applied to situated context, adaption is required. In this contribution, a modified fuzzy-based CREAM approach is introduced to evaluate human driver reliability in situated driving context using the data collected from driving simulator. Firstly, a new list of common performance conditions (CPCs) characterizing the situated driving context is generated due to the application limits of CPCs in CREAM. Secondly, to determine the levels in the new generated CPCs, fuzzy neighborhood density-based spatial clustering of application with noise (FN-DBSCAN) is applied to driving data defining the membership function parameters, which reduces reliance on expert knowledge and can better characterize human behaviors individually. Next, a new evaluation index, human performance reliability score (HPRS), is proposed for the quantitative and dynamic evaluation of human reliability. The results show that the new proposed method could quantify and evaluate human driver reliability in real time.