Sampling for Model Predictive Trajectory Planning in Autonomous Driving using Normalizing Flows

arXiv:2404.09657v1 Announce Type: cross
Abstract: 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 …

abstract arxiv autonomous autonomous driving control cs.lg cs.ro driving framework integral optimization paper path planning predictive sampling simplicity stochastic trajectory type