On the path towards mass deployment of automated vehicles (AVs) several challenges still have to be resolved. One of these is the development of approaches that allow the safe operation of an AV within uncertain environments, while not imposing excessive safety margins. Existing proposals, such as the Responsibility Sensitive Safety (RSS) approach from Intel/Mobileye, are a good first step towards this goal. These approaches are based on parameterizable models, where the parameter choice is often a balancing process of safety and usefulness (e.g. traffic density). Hence, a proper selection of the parameters is crucial, which requires a proper situation understanding within the models. Therefore, we propose to consider the risk of a driving situation inside RSS. The result is a novel risk-aware RSS approach, which allows for significant reductions in safety margins (i.e. increased traffic density) in a situation-dependent manner, while risk limits are maintained, thus achieving the desired balance between safety and usefulness.