Safety of automated driving: The need for a systems approach and application of the Functional Resonance Analysis Method

Grabbe, Niklas; Kellnberger, Anna; Aydin, Beyza; Bengler, Klaus

doi:10.1016/j.ssci.2020.104665

Cited by 34 publications

(33 citation statements)

References 48 publications

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“…In future work, we propose analysing the derived scenarios using the novel approach by Grabbe et al (2020) in order to reduce the approval effort by a scenario-based approach based on the pre-selection of relevant scenarios and their systemic analysis. This would give us increased knowledge of the systemic interrelationships, which offers great potential to ensure safe automation as well as to reduce the validation effort.…”

Section: Discussionmentioning

confidence: 99%

“…Instead, the new perspective in this paper strongly argues for a safety-II perspective and a systemic approach. In particular, relevant scenarios as criteria for exclusion are derived in order to systemically assess (Grabbe et al, 2020) the contribution of human drivers and automation to road safety. Finally, based on the key insights from the subsequent systemic analysis in relevant scenarios, the validation work could be reduced to a reasonable effort by minimising the possible parameter space.…”

Section: Discussionmentioning

confidence: 99%

“…This should be assessed in terms of a safety-II perspective and a systemic approach (cf. Grabbe et al, 2020), which also means that significantly fewer test kilometres have to be covered since data can be gathered immediately. There is no need to wait for the accident to occur or something bad to happen, because you can measure anything at any time.…”

Section: Classification and Analysis Of Relevant Scenariosmentioning

confidence: 99%

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Safety Enhancement by Automated Driving: What are the Relevant Scenarios?

Grabbe

Höcher

Thanos

et al. 2020

Proceedings of the Human Factors and Ergonomics Society Annual

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Automated driving offers great possibilities in traffic safety advancement. However, evidence of safety cannot be provided by current validation methods. One promising solution to overcome the approval trap (Winner, 2015) could be the scenario-based approach. Unfortunately, this approach still results in a huge number of test cases. One possible way out is to show the current, incorrect path in the argumentation and strategy of vehicle automation, and focus on the systemic mechanisms of road traffic safety. This paper therefore argues the case for defining relevant scenarios and analysing them systemically in order to ultimately reduce the test cases. The relevant scenarios are based on the strengths and weaknesses, in terms of the driving task, for both the human driver and automation. Finally, scenarios as criteria for exclusion are being proposed in order to systemically assess the contribution of the human driver and automation to road safety.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Classification and Analysis Of Relevant Scenariosmentioning

confidence: 99%

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Safety Enhancement by Automated Driving: What are the Relevant Scenarios?

Grabbe

Höcher

Thanos

et al. 2020

Proceedings of the Human Factors and Ergonomics Society Annual

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“…Thus, a system is not the sum of the behaviour of its parts, it is a product of their interactions. Further, Grabbe et al [12] designate the frequent argumentation of AD as too simplistic thinking that falsely links the logic of a clear causal link. Rather, according to Bengler et al [13], the human driver is both an active and passive participant in an accident, as well as accident avoidance and compensation element in the same system.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, according to Grabbe et al [12], the very first step in the development process of automated vehicles is to understand the mechanisms of accident development and accident avoidance in road traffic. Hence, the driver's contribution in the corresponding situations can be assessed to derive requirements for automation and the potential of automation with its accompanying factors.…”

Section: Introductionmentioning

confidence: 99%

Functional Resonance Analysis in an Overtaking Situation in Road Traffic: Comparing the Performance Variability Mechanisms between Human and Automation

Grabbe¹,

Gales²,

Höcher³

et al. 2021

Safety

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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.

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Improving safety in complex systems: A review of integration of functional resonance analysis method with semi‐quantitative and quantitative approaches

Kumar,

Upadhyay,

Samanta

et al. 2024

Hum Ftrs & Erg Mfg Svc

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Functional resonance analysis method (FRAM) is extensively employed in analyzing and managing performance variabilities. Additionally, semi‐quantitative and quantitative methods have been increasingly integrated with the FRAM to analyze complex socio‐technical systems to improve safety levels. This review article presents a comprehensive and updated survey of current literature focused on semi‐quantitative and quantitative methods employed for quantifying performance variabilities and exploring aggregation/propagation rules. A total of 1659 studies published between 2012 and March 2024 from various scientific databases were systematically examined using preferred reporting items for systematic review and meta‐analysis, identifying 29 studies that met inclusion criteria. The identified studies were categorized into four groups based on the quantitative methods employed: Monte Carlo simulation, fuzzy logic, cognitive reliability and error analysis method, and miscellaneous approaches. While different methodologies had unique strengths, they commonly relied on expert judgment for data collection, whether for defining probability distributions in Monte Carlo simulations, membership functions, and fuzzy rule bases in fuzzy inference systems, or selecting common performance conditions, determining their interrelationships, and assigning scores. Addressing bias from expert judgment in assessing performance variabilities can be achieved by using suitable experts' opinions integration techniques, and leading safety indicators in the analysis.

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Safety of automated driving: The need for a systems approach and application of the Functional Resonance Analysis Method

Cited by 34 publications

References 48 publications

Safety Enhancement by Automated Driving: What are the Relevant Scenarios?

Safety Enhancement by Automated Driving: What are the Relevant Scenarios?

Functional Resonance Analysis in an Overtaking Situation in Road Traffic: Comparing the Performance Variability Mechanisms between Human and Automation

Improving safety in complex systems: A review of integration of functional resonance analysis method with semi‐quantitative and quantitative approaches

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