  Risk assessment is a central element for the development and validation of
Autonomous Vehicles (AV). It comprises a combination of occurrence probability
and severity of future critical events. Time Headway (TH) as well as
Time-To-Contact (TTC) are commonly used risk metrics and have qualitative
relations to occurrence probability. However, they lack theoretical derivations
and additionally they are designed to only cover special types of traffic
scenarios (e.g. following between single car pairs). In this paper, we present
a probabilistic situation risk model based on survival analysis considerations
and extend it to naturally incorporate sensory, temporal and behavioral
uncertainties as they arise in real-world scenarios. The resulting Risk Spot
Detector (RSD) is applied and tested on naturalistic driving data of a
multi-lane boulevard with several intersections, enabling the visualization of
road criticality maps. Compared to TH and TTC, our approach is more selective
and specific in predicting risk. RSD concentrates on driving sections of high
vehicle density where large accelerations and decelerations or approaches with
high velocity occur.
