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.

中文翻译：

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AutoPreview：自动驾驶行为理解框架

自动驾驶汽车的行为可能与人们的期望有所不同（例如，自动驾驶仪可能会意外放弃控制权）。这种期望的不匹配会导致潜在的和现有的用户不信任自动驾驶技术，并可能增加发生事故的可能性。我们提出了一个简单但有效的框架AutoPreview，以使消费者能够在部署之前预览现实驾驶环境中的目标自动驾驶潜在动作。对于给定的目标自动驾驶仪，我们设计了一个委托策略，该策略使用可解释的动作表示形式复制目标自动驾驶仪的行为，然后可以在线查询该行为以进行比较并建立准确的心理模型。为了证明其实用性，我们提供了与CARLA模拟器集成的AutoPreview原型以及两个潜在的框架用例。我们进行了一项试点研究，以调查AutoPreview在首次体验新的自动驾驶仪策略时是否对自动驾驶仪行为提供更深入的了解。我们的结果表明，AutoPreview方法可以帮助用户从驾驶风格理解，部署偏好和准确的动作时间预测等方面了解自动驾驶仪的行为。