Autonomous vehicles (AVs) are expected to handle traffic scenarios more safely and efficiently than human drivers. However, it needs to be better understood which AV decisions are perceived to be unsafe or risky by drivers. To investigate drivers' perceived risk, we conducted a driving simulator experiment where participants are driven around by two types of AVs---car and sidewalk mobility---with a driving style that matches the participant's driving style. We developed a computational model that allows us to examine drivers' perceived risk of scenarios when interacting with an AV based on the drivers' interventions. The model allows us to quantify and compare the relative perceived risk of different scenarios for the two mobility types. Our results indicate that 1) drivers perceived higher risk in scenarios where the AV attempts to match the driver's preferred driving style, and 2) different scenarios were perceived as having higher risk across the two mobility types. The ability to quantify the perceived risk of scenarios and an understanding of how perceived risk differs across mobility types will provide critical insights for the design of human-aware mobility.