Sampling for Model Predictive Trajectory Planning in Autonomous Driving
  using Normalizing Flows
Georg Rabenstein Lars Ullrich Knut Graichen
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.