  Forecasting short-term motion of nearby vehicles presents an inherently
challenging issue as the space of their possible future movements is not
strictly limited to a set of single trajectories. Recently proposed techniques
that demonstrate plausible results concentrate primarily on forecasting a fixed
number of deterministic predictions, or on classifying over a wide variety of
trajectories that were previously generated using e.g. dynamic model. This
paper focuses on addressing the uncertainty associated with the discussed task
by utilising the stochastic nature of generative models in order to produce a
diverse set of plausible paths with regards to tracked vehicles. More
specifically, we propose to account for the multi-modality of the problem with
use of Conditional Variational Autoencoder (C-VAE) conditioned on an agent's
past motion as well as a rasterised scene context encoded with Capsule Network
(CapsNet). In addition, we demonstrate advantages of employing the Minimum over
N (MoN) cost function which measures the distance between ground truth and N
generated samples and tries to minimise the loss with respect to the closest
sample, effectively leading to more diverse predictions. We examine our network
on a publicly available dataset against recent state-of-the-art methods and
show that our approach outperforms these techniques in numerous scenarios
whilst significantly reducing the number of trainable parameters as well as
allowing to sample an arbitrary amount of diverse trajectories.
