A Cognitive Model for Understanding the Takeover in Highly Automated Driving Depending on the Objective Complexity of Non-Driving Related Tasks and the Traffic Environment.

AbstractThe aim of this study is to refine a cognitive model for the takeover in highly automated driving. The focus lies on the impact of objective complexity on the takeover and resulting outcomes. Complexity consists of various aspects. In this study, objective complexities are divided into the complexity of the non-driving-related task (no-task, listening, playing, reading, searching) and the traffic complexity (relevant vehicles in the driving environment). The impact of a non-driving related tasks' complexity on the takeover is evaluated in empirical data. Following, the cognitive model is run through situations of different traffic complexities and compared to empirical results. The model can account for empirical data in most of the objective complexities. Additionally, model predictions are tested on significant variations in different complexities until the action decision is made. In more complex traffic conditions, the model predicts longer times on different processing steps. Altogether, the model can be used to explain cognitive mechanisms in differently complex traffic situations.


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