  This work addresses the task of risk evaluation in traffic scenarios with
limited observability due to restricted sensorial coverage. Here, we
concentrate on intersection scenarios that are difficult to access visually. To
identify the area of sight, we employ ray casting on a local dynamic map
providing geometrical information and road infrastructure. Based on the area
with reduced visibility, we first model scene entities that pose a potential
risk without being visually perceivable yet. Then, we predict a worst-case
trajectory in the survival analysis for collision risk estimation. Resulting
risk indicators are utilized to evaluate the driver's current behavior, to warn
the driver in critical situations, to give suggestions on how to act safely or
to plan safe trajectories. We validate our approach by applying the resulting
intersection warning system on real world scenarios. The proposed system's
behavior reveals to mimic the general behavior of a correctly acting human
driver.
