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<p>Before automated vehicles can safely operate in real-world traffic, it is crucial to ensure their reliability not only in normal conditions but also in rare and critical situations, such as traffic conflicts. Understanding these critical situations is essential for generating test cases that ensure robust system performance. However, current models of real-world traffic behavior in such situations are limited. This study addresses this gap by detecting rare critical situations at an urban signalized intersection, analyzing road user behavior, and deriving relevant parameter distributions through a long-term analysis of naturalistic trajectory data. Specifically, we focus on interactions between motorized road users (MRU) and crossing vulnerable road users (VRU) in illegal U-turn scenarios. Using over 180&#xa0;days of video recordings, we extracted 9 million trajectories and identified four critical MRU–VRU interactions utilizing Surrogate Safety Measures and deceleration metrics. The analysis reveals that these interactions occur when the VRU traffic light switches from red to green. In addition, we descriptively model the driving behavior to generate parameter distributions for U-turn scenarios. Unlike previous studies, we differentiate between object classes, allowing us to effectively illustrate variations in curve radius—such as median values of 8.1&#xa0;m for cars, 9.7&#xa0;m for vans, and 14.3&#xa0;m for trucks. Our results demonstrate an approach for modeling traffic participant behavior using large-scale trajectory data, showcasing a use case of data science in transportation and contributing valuable insights for simulation-based testing and scenario generation in automated vehicle development.</p>

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Detection and Analysis of Critical Interactions in Illegal U-Turns at an Urban Signalized Intersection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月4日

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  • Clemens Schicktanz,
  • Kay Gimm

摘要

Before automated vehicles can safely operate in real-world traffic, it is crucial to ensure their reliability not only in normal conditions but also in rare and critical situations, such as traffic conflicts. Understanding these critical situations is essential for generating test cases that ensure robust system performance. However, current models of real-world traffic behavior in such situations are limited. This study addresses this gap by detecting rare critical situations at an urban signalized intersection, analyzing road user behavior, and deriving relevant parameter distributions through a long-term analysis of naturalistic trajectory data. Specifically, we focus on interactions between motorized road users (MRU) and crossing vulnerable road users (VRU) in illegal U-turn scenarios. Using over 180 days of video recordings, we extracted 9 million trajectories and identified four critical MRU–VRU interactions utilizing Surrogate Safety Measures and deceleration metrics. The analysis reveals that these interactions occur when the VRU traffic light switches from red to green. In addition, we descriptively model the driving behavior to generate parameter distributions for U-turn scenarios. Unlike previous studies, we differentiate between object classes, allowing us to effectively illustrate variations in curve radius—such as median values of 8.1 m for cars, 9.7 m for vans, and 14.3 m for trucks. Our results demonstrate an approach for modeling traffic participant behavior using large-scale trajectory data, showcasing a use case of data science in transportation and contributing valuable insights for simulation-based testing and scenario generation in automated vehicle development.