  Citation: Grabbe, N.; Gales, A.; Höcher, M.; Bengler, K. Functional Resonance Analysis in an Overtaking Situation in Road Traffic: Comparing the Performance Variability Mechanisms between Human and Automation. Safety 2022, 8, 3. https://doi.org/10.3390/ safety8010003 Academic Editor: Raphael Grzebieta Received: 26 August 2021 Accepted: 17 December 2021 Published: 27 December 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). safety Article Functional Resonance Analysis in an Overtaking Situation in Road Traffic: Comparing the Performance Variability Mechanisms between Human and Automation Niklas Grabbe * , Alain Gales , Michael Höcher and Klaus Bengler Chair of Ergonomics, Technical University of Munich, 85748 Garching, Germany; alain.gales@tum.de (A.G.); michi-hoecher@web.de (M.H.); bengler@tum.de (K.B.) * Correspondence: n.grabbe@tum.de; Tel.: +49-89-289-15375 Abstract: Automated driving promises great possibilities in traffic safety advancement, frequently assuming that human error is the main cause of accidents, and promising a significant decrease in road accidents through automation. However, this assumption is too simplistic and does not consider potential side effects and adaptations in the socio-technical system that traffic represents. Thus, a differentiated analysis, including the understanding of road system mechanisms regarding accident development and accident avoidance, is required to avoid adverse automation surprises, which is currently lacking. This paper, therefore, argues in favour of Resilience Engineering using the functional resonance analysis method (FRAM) to reveal these mechanisms in an overtaking scenario on a rural road to compare the contributions between the human driver and potential automation, in order to derive system design recommendations. Finally, this serves to demonstrate how FRAM can be used for a systemic function allocation for the driving task between humans and automation. Thus, an in-depth FRAM model was developed for both agents based on document knowledge elicitation and observations and interviews in a driving simulator, which was validated by a focus group with peers. Further, the performance variabilities were identified by structured interviews with human drivers as well as automation experts and observations in the driving simulator. Then, the aggregation and propagation of variability were analysed focusing on the interaction and complexity in the system by a semi-quantitative approach combined with a Space-Time/Agency framework. Finally, design recommendations for managing performance variability were proposed in order to enhance system safety. The outcomes show that the current automation strategy should focus on adaptive automation based on a human-automation collaboration, rather than full automation. In conclusion, the FRAM analysis supports decision-makers in enhancing safety enriched by the identification of non-linear and complex risks. Keywords: automated driving; human driving; risk assessment; resilience engineering; systems thinking; overtaking manoeuvre 1. Introduction In the past, traffic safety was improved by three major safety strategies including engineering, enforcement, education [1], and their intertwinings. Nevertheless, according to the World Health Organisation [2], over 1.2 million people die each year on the world’s roads, and between 20 and 50 million suffer non-fatal injuries. These are still high numbers that need to be improved. A promising countermeasure seems to be a technology advance- ment by automated driving (AD, Level 3 and higher, according to SAE J3016 [3]), which offers great possibilities in traffic safety enhancement. A frequent argumentation for this assumption is that the human in his role as a driver is the main cause of accidents, claiming that human error causes approximately 90% of road crashes, e.g., [4–7]. Consequently, it is frequently recommended that the human driver be removed from the system and road accidents will probably decrease by 90%. The common idea behind this is that technology Safety 2022, 8, 3. https://doi.org/10.3390/safety8010003 https://www.mdpi.com/journal/safety