Utility assessment in automated driving for cooperative human-machine systems

Currently, car manufacturers, suppliers, and IT companies are surpassing each other with ambitious plans regarding their driving automation technology. However, even the most optimistic announcements grant that, for a certain time, a human driver cannot be replaced in all driving situations. Hence, human drivers will still be a part of future traffic by working together with automation systems. Analyzing the joint decision-making process of such a human-machine system in automated driving provides a better understanding of the resulting traffic system. In this paper, a driving simulator study with 33 participants focusing on the utility of cooperative driver-vehicle systems with the use case of highway driving is presented. Based on the study's results, a model that explains the linkage between subjective measures such as the perceived utility and objective driving data is derived. Moreover, on an individual level, models are parameterized by using driving states as predictors and the individual utility perceived in a driving situation as response. This individual utility can be used for predicting driving actions such as the initiation of overtaking maneuvers.