The behavior of self-driving cars may differ from people's expectations (e.g. an autopilot may unexpectedly relinquish control). This expectation mismatch can cause potential and existing users to distrust self-driving technology and can increase the likelihood of accidents. We propose a simple but effective framework, AutoPreview, to enable consumers to preview a target autopilot's potential actions in the real-world driving context before deployment. For a given target autopilot, we design a delegate policy that replicates the target autopilot behavior with explainable action representations, which can then be queried online for comparison and to build an accurate mental model. To demonstrate its practicality, we present a prototype of AutoPreview integrated with the CARLA simulator along with two potential use cases of the framework. We conduct a pilot study to investigate whether or not AutoPreview provides deeper understanding about autopilot behavior when experiencing a new autopilot policy for the first time. Our results suggest that the AutoPreview method helps users understand autopilot behavior in terms of driving style comprehension, deployment preference, and exact action timing prediction.