12 research outputs found
Imitative Planning using Conditional Normalizing Flow
We explore the application of normalizing flows for improving the performance
of trajectory planning for autonomous vehicles (AVs). Normalizing flows provide
an invertible mapping from a known prior distribution to a potentially complex,
multi-modal target distribution and allow for fast sampling with exact PDF
inference. By modeling a 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. This mapping allows for control samples
and their associated energy to be generated jointly and in parallel. We propose
using neural autoregressive flow (NAF) as part of an end-to-end deep learned
system that allows for utilizing sensors, map, and route information to
condition the flow mapping. Finally, we demonstrate the effectiveness of our
approach on real world datasets over IL and hand constructed trajectory
sampling techniques.Comment: Submittted to 4th Conference on Robot Learning (CoRL 2020), Cambridge
MA, US
Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning
Learning-based approaches have achieved remarkable performance in the domain
of autonomous driving. Leveraging the impressive ability of neural networks and
large amounts of human driving data, complex patterns and rules of driving
behavior can be encoded as a model to benefit the autonomous driving system.
Besides, an increasing number of data-driven works have been studied in the
decision-making and motion planning module. However, the reliability and the
stability of the neural network is still full of uncertainty. In this paper, we
introduce a hierarchical planning architecture including a high-level
grid-based behavior planner and a low-level trajectory planner, which is highly
interpretable and controllable. As the high-level planner is responsible for
finding a consistent route, the low-level planner generates a feasible
trajectory. We evaluate our method both in closed-loop simulation and real
world driving, and demonstrate the neural network planner has outstanding
performance in complex urban autonomous driving scenarios.Comment: 6 pages, 8 figures, accepted by IROS202