  This paper describes the methodology for building a dynamic risk assessment
for ADAS (Advanced Driving Assistance Systems) algorithms in parking scenarios,
fusing exterior and interior perception for a better understanding of the scene
and a more comprehensive risk estimation. This includes the definition of a
dynamic risk methodology that depends on the situation from inside and outside
the vehicle, the creation of a multi-sensor dataset of risk assessment for ADAS
benchmarking purposes, and a Local Dynamic Map (LDM) that fuses data from the
exterior and interior of the car to build an LDM-based Dynamic Risk Assessment
System (DRAS).
