Human reliability analysis in situated driving context considering human experience using a fuzzy-based clustering approach

Although more higher level advanced driver assistance systems (ADAS) are applied to driving, human driver reliability remains crucial for driving safety. Existing reliability approaches qualify human behaviors in a static manner. In this contribution dynamically changing situations are considered: as example dynamic and situated driving context is used for human reliability evaluation. The dynamic and situated driving context requires dynamic solutions for human reliability evaluation. 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. Human-related accidents account for the highest proportion of total accidents. Human experience as an important factor for driving safety so should be considered when human driver reliability is evaluated. In this contribution, human driver experience (HDE) is quantitatively characterized for the first time. Three variables are selected to evaluate HDE in situated driving context. A new list of common performance conditions (CPCs) in CREAM to characterize the situated driving context is generated due to the application limits of CPCs in original CREAM. To determine the levels in HDE variables and 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. Therefore, HDE and human driver reliability score (HDRS) in situated driving context are calculated quantitatively. In this contribution evaluation of HDE and HDRS is data-driven and the reliance on expert knowledge is reduced. Next, a new evaluation index, human performance reliability score (HPRS) is defined. The results show that the method could quantity and evaluate human driver reliability in real time.