Recent innovations, such as automated driving and smart mobility, have elevated the safety-criticality of automotive systems due to the impact of these technologies on the traffic behavior and safety. New safety validation and assess- ment methodologies are required to provide the level of assurance that matches the societal impact of these systems. The objective of this paper is to introduce a novel method for assessment and quantification of the risk of a driving scenario considering the operational design domain. For our proposed method, we assume that a scenario consists of activities (performed by different actors) and environmental conditions that leads to a potentially hazardous conse- quence. The risk of a driving scenario is the product of the probability of the exposure of a scenario and the severity of the hazardous consequence of that scenario. We introduce a systematic method for calculating the probability of exposure, where we assume a causal relation between the activities that constitute a scenario. By making educated assumptions on the dependencies among the different activities and environmental conditions, we simplify the calcula- tion of the probability of the exposure. For estimating the severity, we employ Monte Carlo simulations. We illustrate the use of our proposed method by applying it to an example of a collision avoidance system in a cut-in scenario. We use naturalistic driving data acquired from field studies on the Dutch highways to determine the risk. The presented example illustrates the potential of our proposed risk estimation method. Using our proposed method, we can compare the safety criticality of various scenarios in a quantitative manner, which can be used as a safety metric for evaluating automated driving systems. This can lead to stronger justification for design decisions and test coverage for developing automated vehicle functionalities.