A theoretical framework for designing human-centered automotive automation systems

Abstract

Increasingly sophisticated and robust automotive automation systems are being developed to be applied in all aspects of driving. Benefits, such as improving safety, task performance, and workload have been reported. However, several critical accidents involving automation assistance have also been reported. Although automation systems may work appropriately, human factors such as drivers errors, overtrust in and overreliance on automation due to lack of understanding of automation functionalities and limitations as well as distrust caused by automation surprises may trigger inappropriate human–automation interactions that lead to negative consequences. Several important methodologies and efforts for improving human–automation interactions follow the concept of human-centered automation, which claims that the human must have the final authority over the system, have been called. Given that the human-centered automation has been proposed as a more cooperative automation approach to reduce the likelihood of human–machine misunderstanding. This study argues that, especially in critical situations, the way control is handed over between agents can improve human–automation interactions even when the system has the final decision-making authority. As ways of improving human–automation interactions, the study proposes adaptive sharing of control that allows dynamic control distribution between human and system within the same level of automation while the human retains the final authority, and adaptive trading of control in which the control and authority shift between human and system dynamically while changing levels of automation. Authority and control transitions strategies are discussed, compared and clarified in terms of levels and types of automation. Finally, design aspects for determining how and when the control and authority can be shifted between human and automation are proposed with recommendations for future designs.