In order to enable high-quality decision making and motion planning of
intelligent systems such as robotics and autonomous vehicles, accurate
probabilistic predictions for surrounding interactive objects is a crucial
prerequisite. Although many research studies have been devoted to making
predictions on a single entity, it remains an open challenge to forecast future
behaviors for multiple interactive agents simultaneously. In this work, we take
advantage of the Generative Adversarial Network (GAN) due to its capability of
distribution learning and propose a generic multi-agent probabilistic
prediction and tracking framework which takes the interactions among multiple
entities into account, in which all the entities are treated as a whole.
However, since GAN is very hard to train, we make an empirical research and
present the relationship between training performance and hyperparameter values
with a numerical case study. The results imply that the proposed model can
capture both the mean, variance and multi-modalities of the groundtruth
distribution. Moreover, we apply the proposed approach to a real-world task of
vehicle behavior prediction to demonstrate its effectiveness and accuracy. The
results illustrate that the proposed model trained by adversarial learning can
achieve a better prediction performance than other state-of-the-art models
trained by traditional supervised learning which maximizes the data likelihood.
The well-trained model can also be utilized as an implicit proposal
distribution for particle filtered based Bayesian state estimation.