We propose a framework for planning in unknown dynamic environments with probabilistic safety guarantees using conformal prediction. Particularly, we design a model predictive controller (MPC) that uses i) trajectory predictions of the dynamic environment, and ii) prediction regions quantifying the uncertainty of the predictions. To obtain prediction regions, we use conformal prediction, a statistical tool for uncertainty quantification, that requires availability of offline trajectory data - a reasonable assumption in many applications such as autonomous driving. The prediction regions are valid, i.e., they hold with a user-defined probability, so that the MPC is provably safe. We illustrate the results in the self-driving car simulator CARLA at a pedestrian-filled intersection. The strength of our approach is compatibility with state of the art trajectory predictors, e.g., RNNs and LSTMs, while making no assumptions on the underlying trajectory-generating distribution. To the best of our knowledge, these are the first results that provide valid safety guarantees in such a setting.

翻译：我们提出了一种利用共形预测在未知动态环境中实现概率安全保障的规划框架。特别地，我们设计了一种模型预测控制器（MPC），该控制器采用：i) 动态环境的轨迹预测，以及 ii) 量化预测不确定性的预测区域。为获取预测区域，我们采用共形预测这一不确定量化统计工具，其需要离线轨迹数据的可用性——这在自动驾驶等许多应用中是合理假设。预测区域具有有效性，即它们以用户定义的概率成立，从而保证MPC的可证明安全性。我们在行人密集路口场景下，利用自动驾驶模拟器CARLA验证了该方法的有效性。本方法的核心优势在于：既能与最先进的轨迹预测器（如RNN和LSTM）兼容，又无需对底层轨迹生成分布作任何假设。据我们所知，这是首个在此类场景下提供有效安全保证的研究成果。