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.
Highlights
In the past, traffic safety was improved by three major safety strategies including engineering, enforcement, education [1], and their intertwinings
The functions are assigned respectively to the four different agents (EV, LV, React to EV’s overtaking (RV), and OV) and five temporal stages during the scenario (Follow, Swerve, Pass, Merge and Get in lane). This is a modification of the Abstraction/Agency framework by Patriarca et al [74] into a Space-Time/Agency framework, which should ensure enhanced knowledge representation combined with a multi-dimensional approach that is two dimensions: the temporal-spatial levels and the agency levels
This paper shows how functional resonance analysis method (FRAM) can be used for a systemic function allocation between humans and automation considering the interactions and complex dynamics of functional variabilities in a space-time continuum within and between agents in the system based on an enhancement of quantitative outputs of FRAM
Summary
Traffic safety was improved by three major safety strategies including engineering, enforcement, education [1], and their intertwinings. 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. The common idea behind this is that technology can be introduced into a system by substituting machines for people so that the system as a whole improves and there are no negative side effects This is a persistent oversimplification fallacy, called substitution myth [8,9]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
