A Study on the Human and the Automation in Automated Driving: Getting to Know Each Other

In recent years, advanced driver assistant systems (ADAS) and solutions for automated driving have been introduced by several automotive original equipment manufacturers (OEMs) and suppliers. Currently, these types of automation are designed for partially automated driving, but the step towards higher levels of automation can be expected to be made soon. One of the most commonly addressed use cases is driving on a highway such as the German Autobahn. In this paper, we propose an approach for adapting the automation's behavior to the human's driving preferences, providing a cognitive automation system with a machine-learning algorithm. This system has been implemented in a simulator for automated driving and has been used in a study addressing conditional automation. Within the presented experiment, typical situations for automated driving under varying conditions have been tested in the driving simulator. During cooperative human-machine driving, the automation can learn the human's preferences regarding relevant driving states.