The growing number of automated vehicles (AVs) necessitates good ride comfort for passengers. This research investigates currently available ride comfort methods and evaluates their performance with a validated simulation framework. The methodology developed encompasses a high-precision road surface model and uses Monte Carlo simulations to compile accurate and representative virtual chassis acceleration data. By utilizing a threshold method and standard ISO 2631 ride comfort guidelines, results are compared to classifications based on empirical International Roughness Index data. A case study conducted in Austria specifies that ISO 2631 comfort estimates are most similar to International Roughness Index classifications and that the thresholding procedure detects preventable situations and over- or underestimated ride comfort. Thus, this methodology can help to better understand requirements for AVs' comfort, as well as justifying the importance of developing a sophisticated performance metric.

中文翻译：

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利用路面模型和蒙特卡洛模拟对自动驾驶车辆进行乘坐舒适性评估

越来越多的自动驾驶汽车 (AV) 需要为乘客提供良好的乘坐舒适性。本研究调查了当前可用的乘坐舒适性方法，并使用经过验证的模拟框架评估其性能。开发的方法包括高精度路面模型，并使用蒙特卡罗模拟来编译准确且具有代表性的虚拟底盘加速度数据。通过使用阈值方法和标准 ISO 2631 乘坐舒适指南，将结果与基于经验国际粗糙度指数数据的分类进行比较。在奥地利进行的一项案例研究指出，ISO 2631 舒适度估计与国际粗糙度指数分类最相似，阈值程序检测可预防的情况和高估或低估的乘坐舒适度。因此，