  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.
