Familiarity and Complexity during a Takeover in Highly Automated Driving

摘要

This paper shows, how objective complexity and familiarity impact the subjective complexity and the time to make an actiondecision during the takeover task in a highly automated driving scenario. In the next generation of highly automated drivingthe driver remains as fallback and has to take over the driving task whenever the system reaches a limit. It is thus highlyimportant to develop an assistance system that supports the individual driver based on information about the drivers’ currentcognitive state. The impact of familiarity and complexity (objective and subjective) on the time to make an action decisionduring a takeover is investigated. To produce replicable driving scenarios and manipulate the independent variables situationfamiliarity and objective complexity, a driving simulator is used. Results show that the familiarity with a traffic situationas well as the objective complexity of the environment significantly influence the subjective complexity and the time tomake an action decision. Furthermore, it is shown that the subjective complexity is a mediator variable between objectivecomplexity/familiarity and the time to make an action decision. Complexity and familiarity are thus important parametersthat have to be considered in the development of highly automated driving systems. Based on the presented mediation effect,the opportunity of gathering the drivers’ subjective complexity and adapting cognitive assistance systems accordingly isopened up. The results of this study provide a solid basis that enables an individualization of the takeover by implementinguseful cognitive modeling to individualize cognitive assistance systems for highly automated driving.