A computer-implemented method for estimating a risk exposure of a vehicle equipped with an automated driving system in a virtual test environment, the method comprising: obtaining input data comprising: at least one candidate state of the vehicle, wherein each candidate state comprises a set of candidate state parameters of the vehicle, at least one predicted scene of the surrounding environment of the vehicle at each candidate state, wherein each predicted scene comprises a set of scene parameters of the surrounding environment of the vehicle and a scene probability metric of the scene occurring at an associated candidate state, at least one event for each predicted scene, wherein each event comprises an event probability metric of the event occurring in the predicted scene and a set of event parameters of each event, a set of risk thresholds, wherein each risk threshold defines a predefined allowable frequency of occurrence of each of a plurality of predefined incidents, wherein each predefined incident defines a potential traffic incident that the vehicle could be exposed to during a predicted temporal development of each event; estimating a probability of each predefined incident occurring in the virtual test environment by running a probabilistic reliability method on each set of candidate state parameters, each set of scene parameters, the scene probability metric for each set of scene parameters, the set of event parameters, and the event probability metric for each set of event parameters; for each candidate state that is associated with a predefined incident occurring with a probability exceeding the predefined allowable frequency of occurrence for that incident, determining that candidate state as a high-risk state for the vehicle; and for each candidate state that is associated with a predefined incident occurring with a probability below the predefined allowable frequency of occurrence for that incident, determining that candidate state as an acceptable-risk state for the vehicle. obtaining input data comprising: at least one candidate state of the vehicle, wherein each candidate state comprises a set of candidate state parameters of the vehicle, at least one predicted scene of the surrounding environment of the vehicle at each candidate state, wherein each predicted scene comprises a set of scene parameters of the surrounding environment of the vehicle and a scene probability metric of the scene occurring at an associated candidate state, at least one event for each predicted scene, wherein each event comprises an event probability metric of the event occurring in the predicted scene and a set of event parameters of each event, a set of risk thresholds, wherein each risk threshold defines a predefined allowable frequency of occurrence of each of a plurality of predefined incidents, wherein each predefined incident defines a potential traffic incident that the vehicle could be exposed to during a predicted temporal development of each event; at least one candidate state of the vehicle, wherein each candidate state comprises a set of candidate state parameters of the vehicle, at least one predicted scene of the surrounding environment of the vehicle at each candidate state, wherein each predicted scene comprises a set of scene parameters of the surrounding environment of the vehicle and a scene probability metric of the scene occurring at an associated candidate state, at least one event for each predicted scene, wherein each event comprises an event probability metric of the event occurring in the predicted scene and a set of event parameters of each event, a set of risk thresholds, wherein each risk threshold defines a predefined allowable frequency of occurrence of each of a plurality of predefined incidents, wherein each predefined incident defines a potential traffic incident that the vehicle could be exposed to during a predicted temporal development of each event; estimating a probability of each predefined incident occurring in the virtual test environment by running a probabilistic reliability method on each set of candidate state parameters, each set of scene parameters, the scene probability metric for each set of scene parameters, the set of event parameters, and the event probability metric for each set of event parameters; for each candidate state that is associated with a predefined incident occurring with a probability exceeding the predefined allowable frequency of occurrence for that incident, determining that candidate state as a high-risk state for the vehicle; and for each candidate state that is associated with a predefined incident occurring with a probability below the predefined allowable frequency of occurrence for that incident, determining that candidate state as an acceptable-risk state for the vehicle.