  Autonomous systems in the road transportation network require intelligent
mechanisms that cope with uncertainty to foresee the future. In this paper, we
propose a multi-stage probabilistic approach for trajectory forecasting:
trajectory transformation to displacement space, clustering of displacement
time series, trajectory proposals, and ranking proposals. We introduce a new
deep feature clustering method, underlying self-conditioned GAN, which copes
better with distribution shifts than traditional methods. Additionally, we
propose novel distance-based ranking proposals to assign probabilities to the
generated trajectories that are more efficient yet accurate than an auxiliary
neural network. The overall system surpasses context-free deep generative
models in human and road agents trajectory data while performing similarly to
point estimators when comparing the most probable trajectory.

Machine Learning