基于鲁棒学习的新兴移动系统轨迹规划
Robust Learning-Based Trajectory Planning for Emerging Mobility Systems
摘要：
在本文中，我们扩展了我们之前开发的用于在无信号交叉口协调连接和自动驾驶车辆（CAV）的框架，以纳入不确定性。利用实际时间轨迹的可能噪声观测值并利用高斯过程回归，在线学习CAV偏离标称轨迹的有界置信区间。这些置信区间，我们重新制定的轨迹规划作为一个强大的协调问题，其解决方案，保证在系统中的约束条件得到满足的存在有界偏离标称轨迹。我们证明了我们的扩展框架的有效性，通过数值模拟。
英文摘要：
In this paper, we extend a framework that we developed earlier for coordination of connected and automated vehicles (CAVs) at a signal-free intersection to incorporate uncertainty. Using the possibly noisy observations of actual time trajectories and leveraging Gaussian process regression, we learn the bounded confidence intervals for deviations from the nominal trajectories of CAVs online. Incorporating these confidence intervals, we reformulate the trajectory planning as a robust coordination problem, the solution of which guarantees that constraints in the system are satisfied in the presence of bounded deviations from the nominal trajectories. We demonstrate the effectiveness of our extended framework through a numerical simulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
