In recent years, autonomous vehicles have become increasingly popular, leading to extensive research on their safe and efficient operation. Understanding road yielding behavior is crucial for incorporating the appropriate driving behavior into algorithms. This paper focuses on investigating drivers' yielding behavior at unsignalized intersections. We quantified and modelled the speed reduction time for vulnerable road users at a zebra crossing using parametric survival analysis. We then evaluated the impact of speed reduction time in two different interaction scenarios, compared to the baseline condition of no interaction through an accelerated failure time regression model with the log-logistic distribution. The results demonstrate the unique characteristics of each yielding behavior scenario, emphasizing the need to account for these variations in the modelling process of autonomous vehicles.

翻译：近年来，自动驾驶汽车日益普及，其安全高效运行的相关研究也随之广泛开展。理解道路让行行为对于将适当的驾驶行为融入算法至关重要。本文聚焦于研究驾驶员在无信号交叉口的让行行为。我们通过参数生存分析量化并建模了脆弱道路使用者在人行横道处的减速时间。随后，利用具有对数逻辑分布的加速失效时间回归模型，评估了与无交互基线条件相比，两种不同交互场景下减速时间的影响。结果表明，每种让行行为场景均具有独特特征，强调了在自动驾驶汽车建模过程中需考虑这些差异的必要性。