  Ensuring safe behavior for automated vehicles in unregulated traffic areas
poses a complex challenge for the industry. It is an open problem to provide
scalable and certifiable solutions to this challenge. We derive a trajectory
planner based on model predictive control which interoperates with a monitoring
system for pedestrian safety based on cellular automata. The combined
planner-monitor system is demonstrated on the example of a narrow indoor
parking environment. The system features deterministic behavior, mitigating the
immanent risk of black boxes and offering full certifiability. By using
fundamental and conservative prediction models of pedestrians the monitor is
able to determine a safe drivable area in the partially occluded and
unstructured parking environment. The information is fed to the trajectory
planner which ensures the vehicle remains in the safe drivable area at any time
through constrained optimization. We show how the approach enables solving
plenty of situations in tight parking garage scenarios. Even though
conservative prediction models are applied, evaluations indicate a performant
system for the tested low-speed navigation.
