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<p>Recent advances in vehicle automation promise greater safety and efficiency. At the same time, the so-called “approval trap” highlights existing gaps in the safety evidence for highly automated driving. This makes new approaches to testing and risk assessment all the more urgent, especially in mixed traffic. In this chapter, the Functional Resonance Analysis Method (FRAM) is applied to an overtaking scenario on a&#xa0;rural road to examine the interactions between human drivers and automated vehicles. By identifying patterns that lead to or prevent accidents, system stability can be improved. In addition, methodological advancements of FRAM are presented, including new metrics for capturing complexity and interaction, tailored to a&#xa0;practitioner-oriented audience.</p><p><i>Practical Relevance</i>: The findings provide concrete guidance for designing safe human–automation interactions in road traffic. By identifying recurring patterns that capture both risks and resilience factors, key leverage points for technical support and function allocation between driver and automation can be defined. This enables developers to assess early on which automation functions are useful and where human involvement remains indispensable. For practitioners in industry and regulation, the approach offers a&#xa0;systemic evaluation of complex traffic dynamics and a&#xa0;more robust basis for safety assessment beyond purely empirical test mileage.</p>

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Gestaltung resilienter Verkehrssysteme: Funktionale Musteranalyse von Mensch–Automation-Interaktionen im Mischverkehr

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月7日

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  • Niklas Grabbe

摘要

Recent advances in vehicle automation promise greater safety and efficiency. At the same time, the so-called “approval trap” highlights existing gaps in the safety evidence for highly automated driving. This makes new approaches to testing and risk assessment all the more urgent, especially in mixed traffic. In this chapter, the Functional Resonance Analysis Method (FRAM) is applied to an overtaking scenario on a rural road to examine the interactions between human drivers and automated vehicles. By identifying patterns that lead to or prevent accidents, system stability can be improved. In addition, methodological advancements of FRAM are presented, including new metrics for capturing complexity and interaction, tailored to a practitioner-oriented audience.

Practical Relevance: The findings provide concrete guidance for designing safe human–automation interactions in road traffic. By identifying recurring patterns that capture both risks and resilience factors, key leverage points for technical support and function allocation between driver and automation can be defined. This enables developers to assess early on which automation functions are useful and where human involvement remains indispensable. For practitioners in industry and regulation, the approach offers a systemic evaluation of complex traffic dynamics and a more robust basis for safety assessment beyond purely empirical test mileage.