AutoPreview: A Framework for Autopilot Behavior Understanding
This addresses distrust and safety issues for potential and existing users of self-driving technology, but it is incremental as it builds on existing simulation and explainability methods.
The paper tackles the problem of mismatched expectations between self-driving car autopilots and users by proposing AutoPreview, a framework that allows consumers to preview autopilot actions before deployment, with results from a pilot study showing it helps users understand behavior in terms of driving style comprehension, deployment preference, and action timing prediction.
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.