论文标题
自动驾驶汽车控制的未来注重控制障碍功能
Future-Focused Control Barrier Functions for Autonomous Vehicle Control
论文作者
论文摘要
在本文中，我们介绍了一类未来注重的控制障碍功能（FF-CBF），旨在改善传统上基于近视CBF的控制设计，并在无信号的四向交叉交叉问题的背景下研究其功效，以收集沟通和非通信自动驾驶汽车。我们的新颖FF-CBF编码，即车辆采取控制动作，以避免在任意漫长的未来时间间隔内零加速政策预测的碰撞。从这个意义上讲，FF-CBF定义了一个虚拟障碍，我们以轻松的未来以未来的CBF（RFF-CBF）的形式提出的松弛，它使虚拟的FF-CBF障碍物放松远非车辆之间的物理障碍。我们研究了基于FF-CBF和基于RFF-CBF的控制器通过一系列相交场景的模拟试验对车辆进行通信的性能，尤其是突出了基于RFF-CBF的控制器如何通过维护安全性和可行性来改善交叉路口来超越文献中的基准控制器。最后，我们在实验室环境中的交叉路口方案中展示了我们提出的FF-CBF控制法，并收集了5个非交通AION地面流浪者。
In this paper, we introduce a class of future-focused control barrier functions (ff-CBF) aimed at improving traditionally myopic CBF based control design and study their efficacy in the context of an unsignaled four-way intersection crossing problem for collections of both communicating and non-communicating autonomous vehicles. Our novel ff-CBF encodes that vehicles take control actions that avoid collisions predicted under a zero-acceleration policy over an arbitrarily long future time interval. In this sense the ff-CBF defines a virtual barrier, a loosening of which we propose in the form of a relaxed future-focused CBF (rff-CBF) that allows a relaxation of the virtual ff-CBF barrier far from the physical barrier between vehicles. We study the performance of ff-CBF and rff-CBF based controllers on communicating vehicles via a series of simulated trials of the intersection scenario, and in particular highlight how the rff-CBF based controller empirically outperforms a benchmark controller from the literature by improving intersection throughput while preserving safety and feasibility. Finally, we demonstrate our proposed ff-CBF control law on an intersection scenario in the laboratory environment with a collection of 5 non-communicating AION ground rovers.
