Our work introduces a module for assessing the trajectory safety of autonomous vehicles in dynamic environments marked by high uncertainty. We focus on occluded areas and occluded traffic participants with limited information about surrounding obstacles. To address this problem, we propose a software module that handles blind spots (BS) created by static and dynamic obstacles in urban environments. We identify potential occluded traffic participants, predict their movement, and assess the ego vehicle's trajectory using various criticality metrics. The method offers a straightforward and modular integration into motion planner algorithms. We present critical real-world scenarios to evaluate our module and apply our approach to a publicly available trajectory planning algorithm. Our results demonstrate that safe yet efficient driving with occluded road users can be achieved by incorporating safety assessments into the planning process. The code used in this research is publicly available as open-source software and can be accessed at the following link: https://github.com/TUM-AVS/Frenetix-Occlusion.

翻译：本文提出一种模块，用于评估动态高不确定性环境下自动驾驶车辆的轨迹安全性。我们聚焦于遮挡区域及信息有限的遮挡交通参与者，针对这一问题，提出一种软件模块处理城市环境中静态与动态障碍物产生的盲区（BS）。该模块识别潜在的遮挡交通参与者，预测其运动轨迹，并利用多种危险性指标评估自车轨迹。该方法可简洁模块化地集成至运动规划算法中。我们通过关键真实场景评估该模块，并将其应用于公开轨迹规划算法。结果表明，将安全性评估纳入规划过程，即可实现与遮挡道路使用者的安全高效驾驶。本研究代码以开源形式公开，可通过以下链接获取：https://github.com/TUM-AVS/Frenetix-Occlusion。