A method for run-time estimation of risk exposure of an ADS-equipped vehicle, the method comprising: obtaining, by one or more processors of the vehicle, 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; processing, by one or more processors of the vehicle, the obtained input data using a trained machine learning algorithm configured to evaluate the at least one candidate state, by: for each candidate state that is associated with at least one predefined incident occurring with a probability exceeding a predefined allowable frequency of occurrence for that incident during a predicted temporal development of each event, generating, by one or more processors of the vehicle, an output indicating that candidate state as a high-risk state for the vehicle; and for each candidate state that is associated with at least one predefined incident occurring with a probability below the predefined allowable frequency of occurrence for that incident during a predicted temporal development of each event, generating, by one or more processors of the vehicle, an output indicating that candidate state as an acceptable-risk state for the vehicle; determining, by one or more processors of the vehicle, a path to be executed by the vehicle based on the evaluated at least one candidate state. obtaining, by one or more processors of the vehicle, 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; 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; processing, by one or more processors of the vehicle, the obtained input data using a trained machine learning algorithm configured to evaluate the at least one candidate state, by: for each candidate state that is associated with at least one predefined incident occurring with a probability exceeding a predefined allowable frequency of occurrence for that incident during a predicted temporal development of each event, generating, by one or more processors of the vehicle, an output indicating that candidate state as a high-risk state for the vehicle; and for each candidate state that is associated with at least one predefined incident occurring with a probability below the predefined allowable frequency of occurrence for that incident during a predicted temporal development of each event, generating, by one or more processors of the vehicle, an output indicating that candidate state as an acceptable-risk state for the vehicle; for each candidate state that is associated with at least one predefined incident occurring with a probability exceeding a predefined allowable frequency of occurrence for that incident during a predicted temporal development of each event, generating, by one or more processors of the vehicle, an output indicating that candidate state as a high-risk state for the vehicle; and for each candidate state that is associated with at least one predefined incident occurring with a probability below the predefined allowable frequency of occurrence for that incident during a predicted temporal development of each event, generating, by one or more processors of the vehicle, an output indicating that candidate state as an acceptable-risk state for the vehicle; determining, by one or more processors of the vehicle, a path to be executed by the vehicle based on the evaluated at least one candidate state.