  The behavior of self driving cars may differ from people 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
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.
