  A popular way to plan trajectories in dynamic urban scenarios for Autonomous
Vehicles is to rely on explicitly specified and hand crafted cost functions,
coupled with random sampling in the trajectory space to find the minimum cost
trajectory. Such methods require a high number of samples to find a low-cost
trajectory and might end up with a highly suboptimal trajectory given the
planning time budget. We explore the application of normalizing flows for
improving the performance of trajectory planning for autonomous vehicles (AVs).
Our key insight is to learn a sampling policy in a low-dimensional latent space
of expert-like trajectories, out of which the best sample is selected for
execution. By modeling the trajectory planner's cost manifold as an energy
function, we learn a scene conditioned mapping from the prior to a Boltzmann
distribution over the AV control space. Finally, we demonstrate the
effectiveness of our approach on real-world datasets over IL and
hand-constructed trajectory sampling techniques.

Robotics;Artificial Intelligence;Machine Learning