基于归一化流的自动驾驶模型预测轨迹规划采样
Sampling for Model Predictive Trajectory Planning in Autonomous Driving using Normalizing Flows
AI总结：
本文提出基于归一化流的采样方法，用于提高自动驾驶轨迹规划的效率和探索能力。
摘要：
在基于优化的规划器之外，由于其简单性，采样方法常用于自动驾驶轨迹规划。模型预测路径积分控制是一种基于优化原理并结合输入轨迹随机采样的框架。本文研究了几种用于轨迹生成的采样方法。在此背景下，来自变分推理领域的归一化流被考虑用于生成采样分布，因为它们可以建模从简单分布到更复杂分布的转换。相应地，针对任务本身，学习到的归一化流模型被训练以更高效地探索输入域。开发的算法和提出的采样分布在两个模拟场景中进行了评估。
英文摘要：
Alongside optimization-based planners, sampling-based approaches are often used in trajectory planning for autonomous driving due to their simplicity. Model predictive path integral control is a framework that builds upon optimization principles while incorporating stochastic sampling of input trajectories. This paper investigates several sampling approaches for trajectory generation. In this context, normalizing flows originating from the field of variational inference are considered for the generation of sampling distributions, as they model transformations of simple to more complex distributions. Accordingly, learning-based normalizing flow models are trained for a more efficient exploration of the input domain for the task at hand. The developed algorithm and the proposed sampling distributions are evaluated in two simulation scenarios.
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。
