Proceedings of the Annual Meeting of the Cognitive Science Society

The aim of this study is to refine a cognitive model forthe takeover in highly automated driving. The focus lieson the impact of objective complexity on the takeoverand resulting outcomes. Complexity consists of variousaspects. In this study, objective complexities are di-vided into the complexity of the non-driving-related task(no-task, listening, playing, reading, searching) and thetraffic complexity (relevant vehicles in the driving envi-ronment). The impact of a non-driving related tasks’complexity on the takeover is evaluated in empiricaldata. Following, the cognitive model is run through sit-uations of different traffic complexities and compared toempirical results. The model can account for empiricaldata in most of the objective complexities. Additionally,model predictions are tested on significant variations indifferent complexities until the action decision is made.In more complex traffic conditions, the model predictslonger times on different processing steps. Altogether,the model can be used to explain cognitive mechanismsin differently complex traffic situations.