ESTIMATION OF PROBABILITY OF COLLISION WITH INCREASING SEVERITY LEVEL FOR AUTONOMOUS VEHICLES

The present disclosure relates to a computer-implemented method and processing system for estimating a probability of failure for different severity levels for an Automated Driving System (ADS) feature in a virtual test environment. In more detail, the embodiments of the present disclosure enables estimation of a probability of crash of different severities, by utilizing a limit state function (LSF) that attains increasingly negative or positive values after crash (e.g. when TTC = 0 or PET = 0). This may for example be achieved by defining a function for severity that is more negative for more severe crashes. The LSF may for example comprises a function of the delta speed at collision (i.e. minus delta speed at collision). Being able to generate a probability of failure for different severity classes for a given ADS feature may be advantageous for focusing development and verification activities to the most needed areas/aspects of the system under test (ADS feature under test).