## 1 Introduction  
The advent of automated driving (AD) systems and Human-Machine Interfaces (HMI) marks one of the biggest game changers in transportation research and development of our time. In 2013, the Society of Automotive Engineers [1] published the first version of their definition describing different levels of driving automation, which addresses challenges, sets foundation for future standardization, and establishes a common language. With SAE Level 2 (L2) driving automationalready on the road, it is only a matter of time until SAE Level 3 (L3) AD systems (ADS), or even higher levels, become commercially available. Thereby, the enormous potential of technological progress has sparked enthusiasm among human factors and Human-Computer Interaction researchers to develop user interfaces for this novel technology. Investigations of such interfaces through user studies are then conducted to first determine feasibility, and, in the next step, to fine-tune conceptual approaches. Here, basic research findings from engineering psychology, and human factors on one side, as well as computer science on the other, have been applied by automotive industry and academia. Furthermore, some lessons learned from prior automation development in the aviation sector [2; 3; 4] could be transferred. However, automated driving systems imply different preconditions which makes the situation much more complex. First, the driving environment is highly time-critical, and thus interventions must happen in seconds or even fractions of a second, while, in airplanes, pilots usually have more time to respond to critical events. Second, there exists greater variety among the targeted user groups. Automated vehicles are consumer products, and driver-passengers will have different levels of training and technology experience, acceptance of- and trust in automation, etc., and further come with additional goals (such as \"driving fun\" or the desire to engage in non-driving related activities) that are not that relevant, or not even present in classical operator settings. As a result, there are a wide range of challenges that need to be overcome, and a multitude of papers addressing these timely issues have been published over the last years. However, it is often hard to integrate and/or compare the obtained findings, as topics are often investigated differently. Furthermore, due to the sheer amount of results, it is hard for researchers to identify gaps where they could build upon. Thus, we claim that the time has come to systematically review which topics in driving automation received the most attention, and which methods have been applied to investigate these. Therefore, the present work aims at combining these approaches, and provide researchers and practitioners with an overview of current and past topics of AD. It also points towards improvements in the future, and unveils directions that have yet to be investigated. Moreover, we plan to give an impetus towards a standardization of methodology concerning self-report, behavioral, and physiological measures, as well as appropriate triangulation approaches of them. The main contribution of this work is twofold, concerning the status quo and derivation of future research directions:  
* Providing an overview of emerging possibilities for study design in driving automation research
* Outlining which constructs have been investigated, which data collection methods have been applied, and which specific parameters have been calculated and reported  
To reach this aim, we developed and followed a structured approach for reviewing related literature. First, we summarize important topics in driving automation that have been addressed in the last years, followed by a precise description of inclusion/exclusion criteria for publications used in this literature review. We then outline a procedure creating a database in which relevant contributions can be stored. Eventually, descriptive results of queries on the database are presented.