Estimation of probability of collision with increasing severity level for autonomous vehicles

A non-transitory computer-readable storage medium storing one or more instructions configured to be executed by one or more processors of a processing system, the one or more instructions for performing a method for estimating a probability of failure for different severity levels for an Automated Driving System, ADS, feature in a virtual test environment, the method comprising:

• obtaining a parametrized statistical model indicative of a statistical distribution related to at least one scenario in a real-world environment in an Operational Design Domain, ODD, of the ADS feature;
• estimating a probability of failure of the ADS feature over time in the virtual test environment by running a structural reliability method based on the parametrized statistical model and on a Limit State Function, LSF, indicative of the ADS feature's performance;
• wherein the LSF, gi(θ), is a function of a set of scenario parameters, θ=[θ1, θ2, . . . , θn], indicative of an operating environment of the ADS feature, the LSF, gi(θ), comprising:
• a first function, gF(θ), that is a function of at least one scenario parameter indicative of an occurrence of a failure scenario;
• and
• a second function, gS(θ), that is a function of at least one scenario parameter indicative of a severity level of the failure scenario, such that the estimated probability of failure of the ADS feature is further indicative of an estimated probability of failure for at least two different severity levels.

A processing system for estimating a probability of failure for different severity levels for an Automated Driving System, ADS, feature in a virtual test environment, the processing system comprising:

• control circuitry configured to:
• obtain a parametrized statistical model indicative of a statistical distribution related to at least one scenario in a real-world environment in an Operational Design Domain, ODD, of the ADS feature;
• estimate a probability of failure of the ADS feature over time in the virtual test environment by running a structural reliability method based on the parametrized statistical model and on a Limit State Function, LSF, indicative of the ADS feature's performance;
• wherein the LSF, gi(θ), is a function of a set of scenario parameters, θ=[θ1, θ2, . . . , θn], indicative of an operating environment of the ADS feature, the LSF, gi(θ), comprising:
• a first function, gF(θ), that is a function of at least one scenario parameter indicative of an occurrence of a failure scenario;
• and
• a second function, gS(θ), that is a function of at least one scenario parameter indicative of a severity level of the failure scenario, such that the estimated probability of failure of the ADS feature is further indicative of an estimated probability of failure for at least two different severity levels.