Human Machine Interface (HMI) is a design concept that improves the interaction between the driver and the automated vehicle, which leads to greater safety and comfort for the driver and greater safety for the road user. Therefore, many papers and patents are published every year. Many papers use different methodologies and materials due to some limitations. Finding an insight about the development of HMI in automated driving could be tough. An overview, such as a systematic review, could be used to create this insight. This paper provides a detailed systematic review, which contains 340 analysed papers and distinguishes them over 20 different categories. Results show an increasing interest in HMI for automated driving systems that reflects the common interest of the general population, an increasing interest of some levels of automation and the use of certain methodologies and materials. Lastly, several insights, caveats, and future implications are discussed.

The development of automated vehicles on the road is in full swing. As vehicles are getting increasingly automated, the human factor is diminished or eventually removed from automated driving. Until then, a combination of human input and automation is necessary during automated driving. This research focuses on the interaction between humans and machine and how a safe interaction can be designed by incorporating meaningful human control. Initially, the aim was to study how different personalities are reflected in driver workload induced by take-over requests (TORs). However, the COVID-19 circumstances changed the aim to validate the design of the driving simulation experiment by means of an N = 1 experiment. Design variables that have been found to play a role in driver workload are varied in the validation experiment. These variables are the duration of the time budget, traffic density, location of the TOR and task involvement during automated driving. Subsequently, workload was measured by a combination of subjective and physiological indicators and driving performance. Notably, this study includes the Root Mean Square of Successive Differences (RMSSD) and Standard Deviation of Normal to Normal peak intervals (SDNN) as heart rate variability (HRV) measures, which is a novel approach in studies measuring TOR-induced workload. Despite the study design that involved performing an N = 1 driving simulation experiment, significant differences between attribute levels have been found. This study provides recommendations on an empirically-validated set of design variables for future studies involving TORs and driver workload, specifically for the future study on personality and automated driving.